section 7 design & manufacturing€¦ · widely employed tightening strategies in the...

SECTION 7

DESIGN & MANUFACTURING

ASME 2012 Early Career Technical Journal - Vol. 11 251

ASME Early Career Technical Journal 2012 ASME Early Career Technical Conference, ASME ECTC

November 2 – 3, Atlanta, Georgia USA

HOW THE LATEST DEVELOPMENTS IN FASTENING TESTING SYSTEMS CAN BE APPLIED TO ESTABLISHING AND VERIFYING A TIGHTENING STRATEGY FOR

CYLINDER HEAD BOLTS

Brian S. Munn Cold Heading Company

Warren, MI, USA

ABSTRACT Modern light weight engine designs have demanding requirements. To meet these requirements, cylinder head bolts have undergone significant improvements in materials and manufacturing. Along with these improvements have come advancements in tightening procedures for bolt installation into an engine block now made from materials other than steel. Recent advancements in fastener testing methods have provided further opportunities for tightening optimization in an ever demanding engine market.

In this paper (Part 1 of 2); a thirty (30) piece study was conducted to determine the rundown behavior of a cylinder-head bolt as it was tightened into a joint made from aluminum. The recorded joint data, in the form of a torque-angle curve, was then reviewed to determine the yield point of the bolt. Using the yield point as reference, a torque-plus-angle tightening strategy was developed to stretch the bolt beyond its elastic limit. In most critical fastener applications, the bolt must be stretched beyond its proportional limit in order to provide the most uniform clamp load.

Every tightening method has its own set of drawbacks and limitations. This is due in large part to the frictional forces encountered by the tightening of any fastener at assembly. In Part 2 of this study (not presented), the tightening strategy presented in Part 1 has been programmed into an Atlas Copco assembly system and a series of rundowns executed under production-like processing conditions. Bolt stretch is the most reliable predictor of bolt preload and joint clamping force. The most accurate means to measure bolt stretch is through the use of ultrasonic technology. A fastener specific ultrasonic testing system, with an acoustic transducer attached to the bolt head, has been used to measure the change in bolt length. To validate the tightening strategy, a permanent deformation (strain) in the bolt must be consistently measured at the end of each bolt rundown.

INTRODUCTION The fastening process for a joint consists of rotating a bolt

which forces the bolt to elongate producing a clamping force to secure the joint. Tightening the fastener as much as possible without incurring damage to either the bolt or joint members is the goal of any tightening strategy. There are numerous tightening strategies that can be employed and a variety of high tech equipment to execute any strategy [1]. For example, manufacturers of advanced fastening systems now have the ability to offer various algorithms to not only monitor the tightening rate, but to detect a change in that rate as the bolt passes through its elastic to plastic regions [2]. Implementing a strategy for a particular bolt application is a simple matter of setting the algorithm parameters to control how far the bolt advances into the plastic zone [3]. Unfortunately, these advanced systems are expensive with extensive training to learn how to set-up and program the fastening system. The most widely employed tightening strategies in the automotive industry today are still torque control for non-critical applications, and torque-angle control for more critical applications such as engine-cylinder head bolts or con-rod bolts [4].

In this paper a tightening strategy will be developed for a steel engine-cylinder head bolt. A threaded fastener torque/angle/tension system will be employed to study the rundown behavior of the steel bolt as it is tightened into an aluminum insert. Aluminum was chosen to represent the joint material since there is an ever increasing interest for lighter weight materials in automotive designs. Since a material difference exists between bolt and joint, extra care must be taken in developing a tightening strategy that will not only produce a sound joint but will also minimize any risk of damage to the softer aluminum joint.

The steps required to develop a sound joint (no damage) with a consistent clamping load, are to first determine joint behavior as the bolt is being tightened and, then, to devise a tightening strategy that produces consistent results. The final


step is to verify that the tightening strategy works under production-like conditions.

In this paper the focus was on determining the joint behavior through a rundown study on the cylinder-head bolt of choice. A thirty (30) piece rundown study is performed on the cylinder-head bolt using a torque/angle/tension testing system produced by RS Technologies. This system has the capability to measure input torque, angle of turn and clamp load. The rundown data was compiled and analyzed to develop a tightening strategy. The tightening strategy must be robust enough to not only optimize the clamping load but to minimize the damage risk to the softer aluminum joint.

The second part of this investigation will verify that the proposed tightening strategy produces a permanent bolt deformation (strain), and is robust enough to withstand a simulated production type of environment. Bolt stretch or deformation is the most reliable means to measure the effectiveness of any tightening strategy since bolt stretch is a variable that does not depend upon joint frictional characteristics.

A production process will be simulated by employing an Atlas Copco assembly system programmed with the proposed tightening strategy from Part 1 of this study. A series of rundowns will then be conducted under a variety of production like conditions.

A Micro Control Inc.’s ultrasonic testing system will be used to record the bolt stretch as it is being tightened into the joint. A special rotary torque angle transducer attached to the bolt head first delivers a short burst of sound into the bolt head. This burst travels down the length of the bolt, echoes off the far end, and returns to the transducer. A transient recorder analyzer measures precisely the round trip time of the sound burst, and also has the capability to then convert time differences into bolt stretch. This testing method is the most accurate and reliable means to verify any permanent bolt stretch or deformation.

Proposed Tightening Method

Cylinder-head bolts used to be tightened with a precisely defined torque that remained in the elastic elongation range of the bolt material. Multiple stages were used to tighten each bolt and retightening of the bolts was frequently required. However, this type of strategy is no longer in use on the current generation of engines. The standard practice now is to tighten the bolt beyond the material yield limit [5]. The typical range for permanent deformation is from 0.003 to 0.13 mm [6].

To consistently hit this target range of permanent deformation a new method called torque-angle control was developed. In the torque-angle controlled tightening process, the bolt is first tightened with a predefined, low torque value well within the elastic limits of the material. At the end of the torque-controlled tightening step, the bolt is tightened further by a predefined angle of bolt head rotation. This combination of torque and angle controlled tightening elongates the bolt beyond the material elastic limit deforming the bolt permanently. This method provides a reliable and reproducible result in maximizing the clamping load provided by the bolt.

Test Set-up/Procedure The bolt type chosen for this study was a M11 x 1.25 x

122, Class 10.9 cylinder-head bolt shown in Figure 1. Each bolt was tested in as-received condition which consisted of a light application of phosphate and oil on all bearing surfaces. The testing apparatus used was a RS Technologies threaded fastener torque/angle/tension system. This system has the capability to measure input torque, angle and clamp load.

Figure 1. M11 x 1.25 cylinder-head bolt

Prior to any testing, special blocks were machined out of engine-grade aluminum and tapped with a hole conforming to the thread configuration of the cylinder-head bolt. These aluminum blocks were then secured in place to the RS test machine providing the desired thread bearing surface characteristics for the tightening of each bolt. This test configuration is shown in Figure 2a. In addition, aluminum washers were used to provide the appropriate bearing surface characteristics for underneath the bolt head during rundown as shown in Figure 2b.

(a) (b)

Figure 2. Test configuration a) aluminum insert and b) drive with bolt head and aluminum washer

Each bolt was tightened in a two step process in the RS test machine. The first step was to tighten each bolt to a target torque (snug torque) of 60 N-m at a speed of 100 RPM’s. From the target torque of 60 N-m, the head of each bolt was then turned an additional 400 degrees. The turn angle of 400 degrees was chosen to ensure that each bolt was stretched well beyond the yield point of the material. At this point, the tightening sequence was shut down to avoid breaking any bolts in the RS test machine. The torque, angle and tension of each bolt was tracked and recorded for analysis. In addition, all thirty (30) bolts were measured before and after testing to verify that permanent elongation had occurred due to the tightening sequence. Rundown Test Results

The rundown data for all thirty (30) bolts tested were compiled to produce the torque (Ti) and the tension (Fc) characteristic curves shown in Figure 3.


Figure 3. Characteristic torque-tension curves

To ensure that each bolt had a permanent (plastic) strain the original length was measured and then after testing, the length was again measured. The results of the before and after elongation measurements are summarized in Table 1. As can be seen in Table 1, all bolts tested yielded, since each bolt had a positive increase in length.

Table 1. Cylinder head bolt elongation measurements

Original Length (mm) Tested Length (mm) Increase in Length (mm)

122.920 123.900 0.980122.816 123.644 0.828122.936 123.536 0.600122.712 123.616 0.904122.862 123.512 0.650122.810 123.398 0.588122.900 123.340 0.440122.752 123.390 0.638122.920 123.594 0.674122.866 123.666 0.800122.890 123.722 0.832122.896 123.556 0.660123.002 123.674 0.672122.990 123.684 0.694122.940 123.366 0.426123.012 123.720 0.708122.796 123.638 0.842123.042 123.416 0.374122.986 123.620 0.634122.872 123.396 0.524122.992 123.728 0.736122.932 123.708 0.776122.922 123.914 0.992122.966 123.762 0.796122.968 123.698 0.730122.748 123.720 0.972122.804 123.836 1.032

Yield Point Tightening with Angle Control

It is possible to use the yield point of the fastener to control the tightening process. However, three (3) torque values must be determined on each rundown curve to develop an accurate angle of turn;

1. TR, this is the snug torque value which has been defined as 60 N-m.

2. TE, this is the torque that defines the upper limit of the elastic region. TE is that point at which the elastic torque rate (slope) begins to deviate (see Figure 4).

3. TP, this is the torque that defines the lower limit of the plastic region. TP is a point on the characteristic torque curve defined as follows;

tanβ = ⅓tanα (1)

where tanβ is the slope at plastic region and tanα is the slope at elastic region.

Figure 4. Illustration of how to find TR, TE and AR-E

Using the torque TR as a reference point, the angle AR-E can be determined for each of the thirty rundowns as shown in Table 2.

Table 2: AR-E at a snug torque of 60 N-m

Run No T E A R-E Run No T E A R-E Run No T E A R-E

1 105 106 11 120 127 21 121 1262 105 105 12 122 126 22 113 993 113 119 13 132 163 23 115 1054 110 116 14 133 138 24 126 1365 112 117 15 131 134 25 119 1166 119 131 16 122 117 26 123 1137 120 128 17 115 118 27 122 1498 119 124 18 123 130 28 128 1149 110 119 19 123 123 29 109 121

10 119 134 20 112 107 30 119 123

From the data in Table 2, the following parameters were

determined; TE (nominal) = 119 N-m Standard deviation (σ) = 7.4 N-m AR-E (nominal) = 123 degrees Standard deviation (σ) = 13.5 degrees


The angle of turn, AR-E can now be incorporated into the overall angle control strategy to be developed for this application.

The next step is to develop another angle of rotation, AE-P from TE to the plastic tightening torque, TP. This can be accomplished by first applying Equation 1 to each of the 30 characteristic torque-angle curves to find TP, and then an angle of turn for AE-P can be determined as shown in Figure 5.

Figure 5. Illustration of how to find TP and AE-P

Using the elastic torque TE as a point of reference, the angle AE-P can be determined for each of the thirty rundowns as summarized in Table 3.

Table 3: Angle measured from TE to TP Run No T P A E-P Run No T P A E-P Run No T P A E-P

1 121 53 11 136 69 21 131 382 119 54 12 135 46 22 134 843 123 54 13 145 34 23 131 624 126 71 14 148 53 24 135 345 129 64 15 140 45 25 135 616 130 43 16 142 63 26 136 397 132 46 17 129 58 27 125 258 129 32 18 139 63 28 132 289 119 47 19 137 46 29 137 66

10 130 51 20 132 73 30 132 52

From the data in Table 3, the following parameters were determined;

TP (nominal) = 132 N-m Standard deviation (σ) = 7.0 N-m AE-P (nominal) = 52 degrees Standard deviation (σ) = 14.3 degrees The total angle of turn required, AR-P is specified as the following;

AR-P = AR-E + AE-P = 123° + 52° = 175° (2)

All the required parameters have now been specified in order to develop a tightening strategy based on angle control. Torque to Yield Tightening Procedure

The following (proposed) tightening procedure was again developed for a current M11 x 1.25 class 10.9 cylinder head bolt. The tightening procedure is based on a snug torque value of 60 N-m and a total angle of turn (AR-P) equal to 175 degrees.

Prior to the actual tightening procedure, it is recommended that a “cycling” sequence be executed to ensure proper seating and to improve loading accuracy of the bolts. This involves running each bolt down to 50 percent of the final torque value (132 N-m) and then backing each bolt out.

The stages and sequence of bolt tightening are detailed in Table 4 and Figure 6 respectively. Also included in Table 4 are some suggested shutdown criteria and rundown speeds (rpm’s).

Table 4. Tightening procedure

Bolt ClassificationSpindle

RotationRPM

ForwardBackwardForward

BackwardTorque 60 N-m Forward 200-400 not to exceed 68 N-m

Angle add 60° Forward 100



not to exceed 68 N-m

Tightening StageTightening Parameter

Shutdown Criteria

Cycle 1 Torque 61 N-m

Tightening Sequence

all spindles

all spindles

all spindlesspindles sequenced

(see Figure 4)

Stage 3spindles sequenced

(see Figure 4)not to exceed 74°

not to exceed 69°

M11 x 1.25Stage 2 not to exceed 74°

Stage 4 all spindles

30-50 not to exceed 68 N-m

Stage 1

Cycle 2 Torque 61 N-m 200-400

When tightening multiple bolts into a joint it is important

to minimize any adverse effects that gasket compliance and elastic interaction might have on the target clamp load. To this end a tightening sequence for stages 2 and 3 has been recommended and is shown in Figure 6.

Figure 6. Tightening sequence for the bolt installation


NEXT STEPS To validate the proposed tightening strategy, an Atlas

Copco assembly system will be programmed with the necessary values for snug torque, and angle of turn. A series of rundowns will then be executed with processing parameters to simulate a production like environment.

Stretch in the bolt will be continuously recorded through a special rotary-torque acoustic transducer attached to the bolt head. This acoustic transducer provides live feedback to a transient recorder analyzer that recorders the real time stretch in the bolt being rundown into the joint. The objective will be to determine if the permanent deformation or increase in final length of the bolts falls between 0.003 and 0.13 mm under production-like rundown conditions.

REFERENCES [1] Bickford, J. H., and Nassar, S., 1998, Handbook of Bolts and Bolted Joints, Marcel Dekker, Inc., New York, Basel, Chap. 28. [2] Chapman, I., Newnham, J., and Wallace, P., 1986, “The tightening of bolts to yield and their performance under load,” Trans. ASME, J. Vibration, Acoust., Stress Reliability Des., Vol. 108, pp 213. [3] Boys, J. T., and Wallace, P. W., 1977, “Design and performance of an automatic control system for fastener tightening,” Proc. Inst. Mech. Eng., Vol. 191, pp.371. [4] Juvinall, R. C., and Marshek, K. M., 2006, Fundamentals of machine component design, John Wiley & Sons, Hoboken, NJ, Chap. 10. [5] ElringKlinger AG, “Cylinder head bolts – a practical guide,” www.elring.de/en/03en/033_zks_kat.php [6] Friedrich, C., 2004, Designing Fastening Systems, Marcel Dekker, Inc., New York, Basel, Chap. 6. Acknowledgements The author would like to give special thanks to Ron Compean, whose hard work made this paper possible.



November 2 - 3, 2012, Atlanta, Georgia USA

FIELD EVALUATION OF COMMERCIAL PEOPLE COUNTER TO MEASURE OCCUPANCY FOR HVAC APPLICATIONS

Trevor D Wolf

Georgia Tech Graduate Research Assistant Atlanta, GA, US

Donald P Alexander

Georgia Tech Facilities Engineer Manager Atlanta, GA, US

Sheldon M Jeter Georgia Tech Associate Professor

Atlanta, GA, US

Gregory M Spiro Georgia Tech Senior Facilities Engineer

Atlanta, GA, US

ABSTRACT This field study evaluated the people counting accuracy of Infodev DA-20 people counters in two buildings on the Georgia Institute of Technology campus. The Georgia Institute of Technology installed infrared people counters in the Hinman Research Building (HRB) and Clough Undergraduate Learning Commons (CULC) as a pilot program with an end goal of integrating the occupancy values into the building HVAC control system. Evaluation of the HRB people counters consisted of a comparison between a direct people count and the sensor people count. A relative error of 3% and 3% for people in and people out count, respectively, indicates the sensors are accurate and do not require calibration for a low to moderate traffic building like the HRB. Investigation of the net building population determined by the people counters in the CULC indicate a net population gain of approximately 2000 people on a typical day. Analysis of the net building population and a calibration study suggests the sensors are undercounting the people leaving when in large groups. Based on the calibration study, a calibration threshold of 150 people per interval was determined to be appropriate for the CULC. A numerical solution varying the calibration factor to minimize the sum of the square of the CULC building population at 5:00 AM calculated an optimal calibration factor of 1.297. The occupancy profile meets the expected population characteristic of the CULC after application of the calibration factor, calibration threshold, and population reset.

INTRODUCTION ASHRAE Standard 62.1 prescribes the minimum outdoor

air ventilation rate per occupant [1]. The goal of this standard is to prevent buildup of pollutants and contaminants in the occupied space of a building. Occupancy of a building varies with time resulting in a changing required outdoor air ventilation rate for the building or space. Demand control

ventilation is the concept developed to accommodate the variable occupancy of a space with the ultimate goal of reducing costs for conditioning the space. Traditional demand control ventilation uses carbon dioxide (CO2) as an indicator of the internal air quality since CO2 levels are directly related to the number of people respiring in a space. CO2 concentrations cannot readily be used to evaluate the number of people in a space since respiration varies between people and depends on activity level. A discrete people counting system can be used to determine the population of a building, floor, or room depending on how the system is set up. This type of people counting system can be used to complement or replace CO2 sensors for demand control ventilation. Occupancy trends also have potential for integration with predictive system management. That is, a zone could be cooled prior to a large influx of people to allow smoother HVAC system operation. With adaptive and predictive control possibilities, a direct population counting system has positive prospects for building energy management.

The Infodev people counters have traditionally been used to measure populations and traffic in relation to marketing and sales for businesses. The Georgia Institute of Technology is the first instance of using the Infodev people counting system with an end goal of integration with the HVAC control system. Georgia Tech installed the Infodev DA-20 directional sensor in the Hinman Research Building (HRB) and the Clough Undergraduate Learning Commons (CULC) as part of a pilot program with an end goal of integrating the occupancy values into the building HVAC control system. The Infodev DA-20 directional sensor consists of three separate infrared sensors. Figure 1 shows the structure of the DA-20 directional sensor. A population count is triggered when something passes within the detection zone of the sensor. Directionality of the object passing through the detection zone is determined by the sequence in which the three infrared sensors are triggered. Sensitivity of the detection zone is initially calibrated based on


the height of the sensor. Based on customer testing reported to Infodev, the DA-20 directional sensor has an accuracy of above 95% [2]. Even though they have a high accuracy, there will be some error in the people counting of each sensor. Over time this error will add up potentially resulting in a negative or unrealistically positive occupancy of the space. To prevent this accumulation of error the data logger resets the total people in and people out count at a time when the population is typically at a minimum for the day.

Figure 1. Infodev DA-20 directional sensor [2]

The CULC and the HRB represent two very different types

of buildings. The HRB is representative of an average college research or office building. This building is an architecture research building with offices, computer clusters, and a large high bay design studio. Traffic through the building will typically consist of the professors and graduate students working in the offices and undergraduate students making use of the computer clusters and design studio. During a typical weekday the number of people entering and exiting the HRB, as reported by the people counters, is approximately 600 and 600, respectively. The CULC is a combination building for classes and open learning and gathering spaces. Only recently completed, the CULC contains two large lecture halls, several medium sized classrooms, lab space for Physics, Chemistry, and Biology classes, and large common areas for studying. Additionally, the building contains its own Starbucks Café. Unlike other buildings, the CULC is open 24/7. With all these high traffic locations internal to it, the CULC was designed for very high influx and efflux of people during all hours of the day. During a typical weekday the number of people entering and exiting the CULC, according to the people counters, is approximately 12,000 and 10,000, respectively, which is inconsistent.

The goal of this paper is to analyze the effectiveness of the Infodev commercial people counters in the HRB and CULC. A comparison between a direct people count and sensor count is presented to assess the accuracy of the people counters in the HRB. Analysis of the net building population as measured by the people counters is discussed for initial assessment of the people counters in the CULC. Based on the building population assessment, a method was developed to determine a calibration threshold and calibration factor. The CULC served as a test case for the calibration factor method developed and the results of the analysis are discussed.

HINMAN RESEARCH BUILDING The HRB is equipped with eight Infodev people counters

at five different external doorways. A direct comparison or calibration of the Infodev counters was made to assess their accuracy and uncertainty. The HRB at Georgia Tech has five entrances, each of which has its own infrared people counter sensors and doorway designation. Each sensor was calibrated using a direct count for comparison. Direct people counts were taken over an hour long interval and compared with the electronic people counters. In this study, two researchers watched one entrance at a time. Both used a manually operated mechanical push button totalizing counter [3]. One researcher counted the people entering the building, and the other researcher counted the number of people leaving the building. The researchers reported no apparent uncertainty on the count, which is as expected since there are typically no routine periods of very high traffic such as class changes in this building. The Infodev people counter software is programmed to record its counts every 15 minutes on the quarter hour. So the researchers would start a count on the quarter hour to be synchronized with the people counters. After each 15 minutes of the test, the count was recorded culminating in four readings over the full hour of the test.

The HRB has three floors, and each floor has at least one entrance. Door 100E is the only entrance to the Hinman building on the first floor. People counter sensor 5 is located at the doorway located on the east side of the building. This doorway is low traffic as there are primarily custodial and maintenance type rooms on the first floor. People counter sensors 1, 2, and 3 are located on the second floor of the HRB. Sensor 1 is on Door 322K which is located on the east side of the second floor. Door 200 is located on the northwest side of the building and has people counter sensor 2. Sensor 3 is located on the southeast side of the second floor over Door 322F. Door 322F is normally locked so there is not a substantial amount of human traffic through it. Door 300 EA-EB is the only entrance to the HRB on the third floor. People counter sensor 4 is located at this doorway which is on the west side of the building. This doorway is normally locked, requiring occupants to enter from a different level and take the stairs to get to the third floor. Therefore, this entrance has very low human traffic that primarily consists of people leaving or propping the door open while making phone calls or similar brief activities.

Each sensor was tested in the same manner of one hour long direct people counting to be compared with the sensor total for the entrance. Performance of people counters was assessed through the absolute error and relative error. For all error calibration the direct people count was assumed to be the most accurate of the measurements. Equation 1 was used to determine the absolute error of each measurement,

DSΑΒS NNErr (1)


where ErrABS is the absolute error, NS is the sensor people count, and ND is the direct people count. The absolute error gives an indication of the total count discrepancy between what the sensor measured and what actually happened. Equation 2 was used to calculate the relative error,

D

DSREL

ABS

NNN

Err

(2)

where ErrREL is the relative error and ABS(X) indicates the absolute value of the quantity within the parenthesis. The relative error normalizes the absolute error so that the count magnitude is relayed in the results.

Table 1 and Table 2 present the people count recorded through the sensors and a direct count for the HRB, respectively. As seen in both tables, the people count for Door 200 and Door 322K is significantly higher making these

Table 1. Sensor Total for each door

Sensor Totals

Door People In People Out

Sensor 1, Door 322K 14 23

Sensor 2, Door 200 37 31

Sensor 3, Door 300 EA-EB 1 3

Sensor 4, Door 322F 5 5

Sensor 5, Door 100E 3 4

Total 60 66

Table 2. Direct Count Total for each door

Direct Count Totals


Sensor 1, Door 322K 15 25





Total 58 64

doorways the primary points of entry and exit from the building. The total people in and people out both resulted in an average of one person entering or exiting the building every minute. Traffic of one person every minute is indicative that the HRB is a low traffic building.

Table 3 and Table 4 contain the absolute error and relative error values calculated for each door, respectively. Sensor people counts agree with direct people counts with a relative error of less than 10% on average. The three instances of a relative error of greater than 10% were a result of a small overall people count for that specific doorway. When the people counts are summed over all five doorways there is a total relative error of 3% and 3% for people in and people out, respectively. The low relative errors indicate that the people counter sensors are accurate and do not require calibration.

CLOUGH UNDERGRADUATE LEARNING COMMONS Initial investigation of the populations recorded by the

Infodev people counters in the CULC suggests the sensors need to be calibrated. With a typical count of people entering and exiting the building at 12,000 and 10,000, respectively, there would supposedly be an average of 2,000 people in the building at the time of the sensors’ reset. During weekends and less active days the population of the building at the time of the count reset approaches or falls slightly below zero. In rare instances this negative value has a maximum magnitude around the 200 people range.

Table 3. Absolute Error for each door

Absolute Error


Sensor 1, Door 322K -1 -2





Total Absolute Error: 2 2

Figure 2 shows a graph of the net population of the CULC from the end of November 2011 through May 2012 with an initial net population of zero. There are three points of interest in the graph where the net population doesn’t change substantially. These three points of interest are the end of December 2011 to the beginning of January 2012, the end of March 2012, and the beginning of May 2012. December 2011 to the beginning of January 2012 corresponds to the winter break at Georgia Tech. During the 2012 year, a week long


spring break took place the second to last week of March 2012. May 2012 corresponds to the end of the spring semester and beginning of the summer semester at Georgia Tech along with the week off between semesters. That is, each of these three periods represents low traffic periods for the building. During the remainder of the time, there was the high traffic typical of the CULC.

Table 4. Relative Error for each door

Relative Error


Sensor 1, Door 322K 6.67% 8.00%

Sensor 2, Door 200 5.71% 3.33%

Sensor 3, Door 300 EA-EB 0.00% 0.00%

Sensor 4, Door 322F 25.00% 25.00%

Sensor 5, Door 100E 0.00% 100.00%

Total Relative Error: 3.45% 3.13% Results from the testing of the people counters in the HRB

suggest that the Infodev people counters are accurate when there is not heavy traffic. During periods of low traffic in the CULC the net building population did not change significantly. When there was the typical high traffic in the CULC the net building population increased steadily over time. Based on these three pieces of information it is likely that the people counters have a higher error during high traffic periods resulting in an undercount of people leaving the building. This discrepancy between the people in and people out count could be explained by student behavior. Based on observations, students tend to enter a class at a gradual rate instead of a single burst. When a class ends all the students leave at a single time resulting in a very high traffic for a short time span. Additionally, classes typically end at the same time increasing the magnitude of the traffic. In order for the people counter to accurately count a person, there needs to be a clear distinction between the start of one person and the start of the next person. With people bunched up in large groups it is more likely for the people counter sensor to consider multiple people passing through a door as a single person.

A direct people count comparison was conducted for the CULC to verify and quantify the undercounting that occurs when classes let out. The way the CULC is designed there are three main entrances to the building. Two of these entrances consist of five doors and the third consists of two double doors. Each door of the five door entrances has one people counter sensor. Three people counter sensors are situated on each of the

Figure 2. Net Population in CULC

double doors. One of the five door entrances is located on the second floor of the building near the Starbucks located within the CULC, the elevators, and the central stairs. The double door entrance to the CULC is a connection to the Georgia Tech Library located on the fourth floor of the CULC. Each double door serves only a single direction of traffic flow. There are several medium size classrooms and lab rooms nearby this entrance as well as the central stairs and elevators. The third entrance is on the first floor near the two large auditoriums in the CULC. Additionally there are several moderate sized classrooms just past the two auditoriums. The entrance on the first floor was used for the calibration study, but any of the entrances would have been appropriate.

The procedure for the people counter testing was similar to that of the HRB expect for the number of researchers involved with the experiment. The time interval used for testing was Tuesday and Thursday from 3:15 PM to 4:15 PM. At Georgia Tech, the schedule for normal classes is the same on Tuesday and Thursday. Based on investigation of the Georgia Tech class schedules for summer 2012, it was determined that several smaller classes and one large auditorium class would let out students around 3:45 PM. An experimental trial was conducted on both Tuesday and Thursday in order to account for any differences in scheduling. Due to the high traffic expected during the time interval, two researchers performed the direct count for people entering, and two different researchers performed the direct count for people exiting the building.

Table 5 and Table 6 present the people out count recorded from the sensors and from a direct count for the CULC, respectively. As seen in the both tables, the people count is large in magnitude as expected for one of the primary points of entry and exit from the building. That is, the magnitudes indicate that the time interval used is a high traffic period.

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

11/2

3/11

12/1

8/11

1/12

/12

2/6/

12

3/2/

12

3/27

/12

4/21

/12

5/16

/12

6/10

/12

Net

Bui

ldin

g Po

pula

tion

Time


Table 5. Sensor Total for each trial

Sensor Totals


Peak Traffic Trial 1 234 204

Peak Traffic, Trial 2 150 197

Total 384 401

Table 6. Direct Count Total for each trial

Direct Count Totals




Total 403 446

Table 7 and Table 8 contain the absolute error and relative error values calculated for each trial, respectively. Sensor people in counts agree with direct people in counts with a relative error of approximately 5%. This relative error is within the acceptable range for the Infodev people counters. The people out counts have relative error of around 10%. Since the absolute error is negative the people counter sensors are significantly undercounting people leaving during high traffic intervals as predicted.

Table 7. Absolute Error for each trial

Absolute Error


Peak Traffic Trial 1 -11 -26

Peak Traffic Trial 2 -8 -19

Total Absolute Error: -19 -45

PEOPLE COUNTER CALIBRATION The method devised to rectify the undercounting of people leaving in groups is to use a calibration factor for the people out count. There are two characteristics of the net building population seen in Figure 2 that need to be taken into account for a calibration factor. First, the increase in the net building

Table 8. Relative Error for each trial

Relative Error


Peak Traffic Trial 1 4.49% 11.30%

Peak Traffic, Trial 2 5.06% 8.80%

Total Relative Error: 4.71% 10.09% population over time has a relatively uniform slope. During prolonged periods of a constant increase in building population over time, such as the spring semester, a single calibration factor can be used to prevent a growing building population. The spring semester corresponds to January 2012 through April 2012 and a majority of the population data. During the month of May, the slope is significantly smaller but still positive. This month corresponds to the end of the spring semester and the beginning of the summer semester. At Georgia Tech the summer semester has a drastically reduced student body as compared to the spring and fall semesters. The second relevant aspect of the graph is the periods of constant net building population. Periods of constant net building population correspond to periods of low traffic where the people counter sensors are expected to be accurate. That is, the number of people leaving the CULC during each 15 minute time interval is small. A threshold needs to be incorporated to prevent the calibration factor from being applied to periods where the people leaving is small and likely accurate as demonstrated by the results from the HRB calibration study. For example, if the number of people leaving the building in a given 15 minute time interval is greater than 100, then the calibration factor shall be applied to the number of people leaving the building during said time interval. A calibration factor and calibration threshold can accommodate the characteristics of the net building population graph in Figure 2.

Due to the variability of building entrances and the configuration of people counters, a general approach was developed for determining the calibration factor and the calibration threshold. A peak traffic study was conducted on one of the primary entrances to the building. For the CULC, any one of the three main entrances could have been studied to determine the calibration threshold. The comparison of the change in the people out total for the sensor and direct count reveals an individual entrance threshold beyond which the absolute error increases. Table 9 and 10 contain the change in people out for the two main door peak traffic studies. More than two studies of the entrance may be necessary if there is not a trend in the results. For this entrance of the CULC there were five time intervals out of eight that had a people count of approximately 50 people each of which had a sizeable absolute error. Based on this observation the apparent individual entrance threshold is approximately 50 people leaving during a


given time interval. This deduced number is not an exact value but a threshold that generally holds true since it is impossible to always know when the people count is a result of a clump of

The individual entrance threshold value is different than the overall building calibration threshold. The calibration threshold is determined using the sensor people counter data for all of the primary entrances to the building. Table 11 contains the sensor people out data for the primary entrances to the CULC. The difference between the primary entrance total and the building total is small as expected. Additionally, the difference between the two totals is the people out count for the nine other entrances. This means the traffic through these nine other entrances is minimal and will typically not result in undercounting the people leaving. Conversely, the primary entrances will be the entrances experiencing undercounting. Since the traffic patterns are similar for the primary entrances the calibration threshold will be proportional to the individual entrance threshold and the number of people counter sensors. This relationship is described by Equation 3,

RE

1i

1IET N

S

SCC n

i

(3)

where CT is the calibration threshold, CIE is the individual entrance threshold, Si is the number of people counter sensors for the ith primary entrance, S1 is the number of people counter sensors for the primary entrance corresponding to the CIE entrance, and NRE is the people count for regular entrances. Two of the primary entrances including the one that was used for the calibration study have five people counters. The third entrance has six sensors, but due to unidirectional setup of the double doors only three of the sensors have a people out count. Based on the data in Table 11, the average people count for the regular entrances is 20 people per 15 minute interval. The value of NRE is subject to additional refinement during period of different occupancy like the fall semester. With these values the calibration threshold for the CULC is 150 people out per interval.

Table 9. Change in people out count during each time interval for trial 1

Change in People Count Counts

Time Sensor Direct Count Absolute Error

4:15 PM 25 30 -5

4:00 PM 91 98 -7

3:45 PM 55 67 -12

3:30 PM 33 35 -2

3:15 PM 0 0 0

Table 10. Change in people out count during each time

interval for trial 2

Change in People Count Counts

Time Sensor Direct Count Absolute Error

4:15 PM 35 38 -3

4:00 PM 44 50 -6

3:45 PM 61 66 -5

3:30 PM 57 62 -5

3:15 PM 0 0 0

There is a nonlinear relationship between the calibration factor and the building population. A numerical solution approach was used to solve for a calibration factor that minimizes the overall building population when there should be a minimum. For most buildings, this time period will be between 12:00 AM and 6:00 AM. Through investigation of the historical people counter data it was determined that the minimum building population occurs around 5:00 AM in the CULC. The calibration factor and threshold can result in negative building populations. To accommodate the potential for negative values, the square of the building population at 5:00 AM was summed for all available building population data. This total was minimized by varying the calibration factor. With a calibration threshold of 150 people per interval, the calibration factor that minimized the CULC building population at 5:00 AM is 1.297. Table 11. CULC Primary Entrance people out count over

trial period

Sensor Totals

Door Trial 1 Trial 2

First Floor Entrance 204 197

Second Floor Entrance 239 195

Fourth Floor Entrance 122 100

Primary Entrances Total 565 492

Building Total 649 562

Figure 3 shows the net building populations after applying the calibration factor and calibration threshold. Although the


population was minimized at 5:00 AM, the magnitude is not zero resulting in unstable population growth. However, the population profile represents a significant improvement over the raw data. There is evidence of the oscillatory behavior that is expected for building population. Between the months of January and April the population stays within a range of several thousand people. During the other time periods, the population profile does not follow the expected behavior. The random nature of people entering and exiting the building makes it impossible to adequately modify the data with only a one criterion calibration factor. However, more complicated calibration thresholds that are dependent on time would be beyond the scope of a typical building.

Figure 3. Net Population in CULC after calibration factor

A simple correction to add to the calibration factor and calibration threshold that is within the capabilities of a typical building is a population reset. That is, the people in count and people out count will be set to zero at a specified time. Infodev people counters have a default reset at 12:00 AM to prevent an overflow error in the controller. For regular buildings, this reset time would be appropriate since the building population will be zero or at a minimum between 12:00 AM and 6:00 AM. The population in the CULC is at a minimum at 5:00 AM so the count reset was set to take place at 5:00 AM. As with the calibration factor and calibration threshold, the reset is limited in its accuracy in that the building population for the CULC is not actually zero at 5:00 AM. Additionally, the minimum population will be different each day, weekend, and holiday. A final correction to the calibrated building population is setting the net building population to zero instead of allowing a negative population.

Figure 4 shows the net building population after incorporating the population reset. The resolution of the graph is too small to see the day to day characteristics of the calibration, but the macroscopic characteristics can be seen. The net building population typically peaks around 1,400 people. Based on the seats available in the classrooms, labs, and corridor areas there is a maximum seating capacity of approximately 2,000 people in the CULC. This number does not include people that are standing or walking around. The

seating capacity value will typically not be reached since it is atypical for all classes to be in session or completely filled at the same time. Considering this observation, the general profile present agrees with the population expectations. The large peak at the end of May corresponds to the week before finals at Georgia Tech. Since the CULC is a common studying spot, there will be people gathering and walking through the area even if no seating is available. This peaking behavior is consistent with the finals week in the fall semester which was near the beginning of December. The irregular data at the beginning of January corresponds to a period when the people counters were randomly off or not resetting properly.

Figure 4. Net Population in CULC after calibration factor, calibration threshold, and population reset

Figure 5 is a close-up of the data contained in Figure 4 focusing on the date range of 2/18/12 to 2/25/12. February 18 corresponds to a Saturday and February 25 corresponds to the following Saturday. All of the weekend days had significantly lower peak populations than the weekdays. Every day exhibits the expected increase in population in the morning and decrease in population in the evening. The only irregularity is the second peak around 12:00 AM during the weekdays. However, this behavior agrees with the activity characteristics of the CULC. For the secondary peaks, the first minimum is around 6:00 PM or around the time classes end. The subsequent increase represents the students that go to the CULC in the evening and nighttime to study and do homework. Overall the occupancy profile represented by the calibrated data meets the expectations for the CULC.

-10000

-5000

0

5000

10000

15000

20000

25000

30000

11/2

3/11

12/1

8/11

1/12

/12

2/6/

12

3/2/

12

3/27

/12

4/21

/12

5/16

/12

6/10

/12

Net

Bui

ldin

g Po

pula

tion

Time

0

500

1000

1500

2000

2500

3000

11/2

3/11

12/1

8/11

1/12

/12

2/6/

12

3/2/

12

3/27

/12

4/21

/12

5/16

/12

6/10

/12

Net

Bui

ldin

g Po

pula

tion

Time


Figure 5. Close-up of Figure 4 looking at 2/18 to 2/25

CONCLUSION The people counting accuracy of Infodev DA-20 people

counters was evaluated in two buildings on the Georgia Institute of Technology campus. The Hinman Research Building (HRB) and Clough Undergraduate Learning Commons (CULC) represent two building with significantly different levels of traffic during typical days. For the HRB typical traffic will be around 600 people while the CULC will have traffic in the range of 10,000-12,000. Initial investigation of the people counters supports the idea that infrared people counting technology suffers when large groups of people clump together through doorways. In a low traffic building like the HRB the comparison of a direct count and the sensor count produced a relative error of 3% and 3% for the in and out count, respectively. This low relative error indicates the people counters can accurately measure building populations during low traffic situations. In the CULC there is a net population gain of approximately 2000 people per day resulting from undercounting the number of people exiting the building. With near constant population gain and inaccuracy in high traffic situations, the undercounting of the people counters can be corrected with a calibration factor and a calibration threshold. An appropriate calibration threshold of 150 people per interval was determined using the data from the calibration study performed on one of the primary entrances of the CULC. A calibration factor of 1.297 was calculated using a numerical approach by minimizing the sum of square of the overall building populations at 5:00 AM. Application of the calibration factor, calibration threshold, and population reset to the people counter data resulted in an occupancy profile that meets the atypical population expectations for the CULC.

REFERENCES [1] ASHRAE, “ANSI/ASHRAE Standard 62.1-2010, Ventilation for Acceptable Indoor Air Quality”, Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. 2010.

[2] Infodev, “Directional People Sensor: DA-20”, 2012, From http://www.infodev.ca/buildings/products-and-people-counters/products/counting-devices/da-20.html. [3] McMaster-Carr Supply Company, “Push-Button Counters”, 2012, From http://www.mcmaster.com/#push-button-counters/=ez7jhy.

0

200

400

600

800

1000

1200

2/18

/12

2/19

/12

2/20

/12

2/21

/12

2/22

/12

2/23

/12

2/24

/12

2/25

/12

2/26

/12

Net

Bui

ldin

g Po

pula

tion

Time




DEVELOPMENT, TESTING, AND COMPARISON OF A REGULAR BRAIDED EYE SPLICE

Austin Yuill, Caitlin Plunket, and David Branscomb Auburn University

Auburn, Alabama, USA

Chad Rodekohr Presbyterian College

Clinton, South Carolina, USA

ABSTRACT The purpose of this study was to compare the difference

in tensile strength between a regular braided eye splice and a diamond braided eye splice. Results of tensile tests conducted on diamond braided eye splices were published in a paper titled “Development and Testing of a Diamond Braided Eye Splice”, in the 2011 ASME ECTC conference [1]. The results recorded for the present paper were compared to those presented in the 2011 paper. The same criteria were used to test both types of braided eye splices. For every set of splice samples tested, corresponding rope samples were prepared and tested. The production parameters of the splices were: braid angle for the rope section, yarn tension, yarn material, core material, and yarn size. Similar to the diamond braided eye splices it was observed that certain factors led to premature failure of the regular braid. The number of revolutions needed to lock the splice in the rope body was determined to be three. Furthermore a one-inch transition section is formed after the helical revolutions. Jamming the braid throughout the splice section was also observed to cause failure due to a dramatic reduction in cross-sectional area. The test results for the regular splice versus a similar rope showed that there was a 9 percent reduction in splice strength. When compared to the diamond splice the tensile tests showed that the regular braided eye splice was 10 percent stronger.

INTRODUCTION Ropes have been used by mankind from our earliest

beginnings. Ropes have been used in all sorts of environments for an endless number of tasks. Using ropes to assist in doing work can provide great mechanical advantage to manpower. Knots or spliced ends can be used to anchor ropes to objects. A common failure point when using ropes is in the connections or terminations.

A splice is different from a knot in that it is permanent and is designed to be more efficient. Having a permanent fixture rather than a temporary one is often desirable. A splice, however, is not perfect. Like a knot, a splice often times cannot withstand as much load as the rope from which it is made. Splice strength varies according to the architecture and size; however, a reduction in strength is commonly expected

[2]. Because of this reduction in strength, larger ropes must be used to maintain the factor of safety on the job site with conventional splices.

The most common way of making a splice is by inserting the end of a rope into the body of the rope. This requires a hollow rope, i.e. a rope with no core. A conventional splice called a buried eye splice can be seen in the top portion of Figure 1. Generally the Buried Eye splice is made after the rope has been braided. In this case, only friction prevents the splice from coming undone. In contrast, the method reported in this study for fabricating a splice is different in that the terminal end of the rope is braided back into the rope and thus an integral part of the rope itself. The basic structure of the braided splice can be seen in the bottom portion of Figure 1.

In theory, a regular braid is stronger than a diamond braid in tensile loading because there is less crimp per length of yarn. Crimp occurs when two yarns cross and have to bend around each other. Crimp creates many weak points in the yarn by adding a lateral component to the total force on the yarn. Therefore, the sum of the forces on the yarn is greater in a diamond braid as compared to a regular braid. As seen in Figure 2, a regular braid varies only slightly in appearance, but significantly, from a diamond braid. The repeating pattern for any yarn in a diamond braid is over one yarn and under one yarn. The regular braid yarn differs by traveling over two yarns and under two yarns.

EXPERIMENTAL SETUP The splices were made on a Wardwell braiding machine

that holds 32 yarn carriers. The type of braid used was a regular braid, as seen in Figure 2. The braiding yarns were nominally 1500 denier Vectran® with no twist. The properties of Vectran® can be seen in Table 1. Sixteen yarns were

Figure 1 – Top: CAD Model of the Buried Eye Splice, Bottom: CAD Model of the Braided Splice


braided using the machine to make the rope. The core of the rope was a 34485.3 denier cotton yarn with one twist per inch. The configuration of the braiding machine and capstan can be seen in Figure 3. The properties of the materials used are in Table 2.

Figure 3 - Manufacturing Processes for Braided Splices

The braid angle was held constant by a computer controlled capstan. The braid angle can be seen in Figure 4. The braid angle can be controlled by varying the ratio of the braiding machine speed to the speed of the capstan. A higher ratio corresponds to a higher braid angle or a jammed braid, where the braid is stable in tension and compression. A lower ratio corresponds to a lower braid angle or a pulled back state.

Table 1 - Properties of Material Used [3]

Properties of Vectran® Tensile Strength 434 ksi

Elongation at Break 3.3-3.7% Moisture Regain <0.1%

Chemical Resistance Hydrolytically stable

To make the splice, a one-foot length of rope was braided while attached to the capstan. The braiding machine and capstan were simultaneously stopped and the rope was disconnected from the capstan. Keeping the braiding point in the same position, the end of the rope was loaded back into the braiding machine on a specific carrier. A thimble was placed at the braid point to create a consistently sized loop or “eye” in the splice. The braiding machine was run until the carrier with the rope end completed three revolutions. The remaining

Table 2 - Constituent Properties of Splice

Constituent Properties of Splice

Property Cotton Core

Braiding Yarns

denier (grams/9000 meters)

34485.3 1529.1

Tenacity (grams/denier)

3-5 25.6

Density (pounds/in3) 0.056 0.050

Number of Yarns 1 16

length of the original one foot section was cut one inch behind the braiding point and tucked in with the core to form a transition section from the splice to the rope. The newly made splice was connected to the capstan and 5 feet of rope were braided after the splice.

Figure 4 – Braid Formation Positions Left: Pulled Back

Right: Jammed State

The minimum length of the splice, so the splice would not pull itself out of the rope when loaded, was incremented until an appropriate length was found. Five specimens were made with increasing numbers of revolutions from one to five. When tested, the specimens with one and two wraps failed by splice pullout of the rope. It was subsequently determined that three revolutions in the splice are sufficient to prevent failure by this method.

The braid point at the beginning of the splice, braid point at the end of the splice, tension of the braider yarns, and number of revolutions the splice makes around the machine were all variables tested to find the most reliable splice structure. Our testing showed that a high braid angle at the beginning of the splice section (just after the loop) was best for keeping the loop secure while a lesser braid angle at the

Regular Diamond

Splice end Splice body Splice start Loop

Figure 2 - The Repeating Patten of Regular and Diamond Braids

Figure 5 - Parts of the Splice


end of the splice section would not lead to a premature failure at that point. All of the parts of the splice can be seen in Figure 5.

The manufacturing of splices and braided rope was carried out using a custom-designed servo-actuated take-up machine (Figure 3). A desktop computer was used for the motion control (Figure 6). This system allows for precision in sample manufacture. It is a four axis computer-controlled take-up system, including two brushless DC servo motors, and one AC Servo Motor, a braiding machine shaft mounted encoder, and LabVIEW 8.2 custom motion control programs. The computer-controlled servo-axes include the capstan, variable pitch traverse, and spool.

Figure 6 The experimental take-up machine motion

control system, including (top row, left to right) computer, motion control card/breakout board, encoder on braiding machine shaft, (second row) servo-amplifier, DC servo-motor, (third row) DC servo-amplifier, DC servo-motor.

(third row) DC servo-amplifier, DC [4].

TESTING Testing was done with an Instron® model 5565

mechanical testing machine. The loop on the splice was secured with a 0.5 inch diameter steel pin and the other end of the specimen was secured by sequential wrapping around a capstan. Crosshead speed was set to 12 inches per minute. Setup of the testing method can be seen in Figure 7.

RESULTS Testing concluded that the splice could withstand more

than 90% of the measured rope strength. Usually the specimen would fail in the rope section and not the splice section. During testing, the rope would typically fail at the point of tangency with the capstan due to tensile elongation. Further testing should be done to investigate this phenomenon.

In previous research, the average strength of the splice specimens for all three data sets was 720.06 lbf and the average strength for the diamond braided rope specimens was

Figure 7 – The Testing Setup with the Instron Testing Machine and a Splice Specimen

777.32 lbf [1]. The data in Table 3 and Table 4 show that regular braided specimens are stronger than the diamond braid equivalent. The percent difference between the regular braided splice and the diamond braided splice was 10.9%. The percent difference between the regular braided rope and the diamond braided rope was 13.17%. These results confirm our hypothesis that a regular braided splice is stronger in tensile loading than a diamond braided splice.

Table 3 - Test Results of Regular Braid Splice

Splice Specimen Tensile Strength (lbf) 1 759.03 2 747.23 3 858.47 4 820.53 5 808.05

Mean 798.66

Table 4 - Test Results of Regular Braid Rope

Rope Specimen Tensile strength (lbf) 1 869.19 2 928.22 3 901.28 4 839.18 5 848.48 6 891.81

Mean 879.69

The regular braid and the diamond braid vary greatly in appearance due to the configuration of the carriers on the braiding machine. In Figure 8 the loading pattern for the diamond braid and regular braid can be seen. The regular braid configuration on the top right of the figure has two mirrored gaps, one on either side of the machine. These gaps in the loading configuration create gaps that can be seen in the regular braid at the bottom of Figure 8. During braiding, these gaps appear ninety degrees from the previous gaps for every quarter revolution of the braiding machine. These open patches do not seem to affect the strength of the specimen but could present problems in the daily use of the splice. The

Rope Grip

Specimen

Pinned Connection


diamond braid configuration on the braiding machine only presents very small gaps while braiding. These gaps are filled when the yarns pack tightly at the point of convergence of the yarns.

Figure 8 – Top Left: Diamond Braid Configuration Top Right:

Regular Braid Configuration Middle: Diamond Braid Rope Bottom: Regular Braid Rope

DISCUSSION The most effective way of producing the splice was

to jam the splice at first and then gradually return the braiding point to its original position by the end of the spliced section. Keeping the braiding point in the pulled back position throughout the manufacturing of the splice showed similar results as jamming the braid from the splice start and gradually transitioning to the pulled back state. For everyday use however, jamming the braid at the splice start prevents the splice from being worked undone. The jammed portion held the splice tightly so it could not be worked undone. During the splice formation, the braid point was gradually returned to the braid point of the rope formation; this allowed for a smooth transition between the splice section and the beginning of the rope. If the specimen had a large (more than 80 degrees) braid angle throughout the making of the splice section and into the rope section, a dramatic decrease in cross sectional area at the end of the splice would occur. Having a large decrease in cross-sectional area over a short distance in the rope proved to be detrimental to the overall strength of the splice. Often times the specimens would fail prematurely at the transition point.

CONCLUSION This paper introduced a novel method for producing a

braided eye splice. A comparison was made between a diamond braided eye splice and a regular braided eye splice.

Tensile tests were performed for the braided eye splice as well as a similarly manufactured set of ropes. The testing results showed that a regular braid is stronger than a diamond braid because of the reduction in the amount of crimp of the yarn. The testing also showed the eye splice could achieve more than 90% of the rope strength. Moreover this research also demonstrated a viable method for manufacturing braided rope splices.

ACKNOWLEDGEMENTS The Authors would like to thank the following people for

their contribution to research project: Austin Gurley, Jeremy Duffy, Dr Ramsis Farag, Dr Roy Broughton, Dr Stephen Bigbee, and David Clark. We would also like to thank Auburn University Department of Polymer and Fiber Engineering for use of the equipment and materials.

BIBLIOGRAPHY [1] Plunket, C., Yuill, A., & Branscomb, D. (2011). Development and Testing of a Braided Eye Splice. ASME Early Career Technical Journal. [2] Garland, J. J. (2004). Investigation of Synthetic Rope End Connections and Terminations in Timber Harvesting Applications. Oregon State Univesity Thesis. [3] (n.d.). Retrieved 06 21, 2011, from Vectran Fiber: http://www.vectranfiber.com/BrochureProductInformation/Twist.aspx. [4] Branscomb, D; Beale,. (2010). Machine Vision Uses for Fault Diagnostics and System Examination. ASME IDTEC & CIE Conference. Montreal.

Loaded- Unloaded-




Open-Architecture Composite Tube Design and Manufacture

David Branscomb, Austin Gurley, David Beale, Royall Broughton Auburn University

Auburn, Alabama, USA

ABSTRACT An open-architecture composite tube is designed,

manufactured, and tested for torsional applications. A computer aided design (CAD) based design process is presented. Topological optimizations performed in ANSYS Workbench are utilized as a design target. The resulting lattice-like pattern is realized using a conventional Maypole braiding machine to produce an open-architecture composite. A solid composite tube of similar weight and major dimensions is also manufactured and tested. The experimental results from torsion testing of the solid tube and the open-architecture tube are compared and discussed. Two analytical models are derived from the kinematics of the braiding machine components to produce three dimensional CAD models facilitating visualization, design parameterization, and finite element analysis. The merits of the analytical models are discussed and compared with physical testing results.

INTRODUCTION Composite materials have been employed to replace

conventional metallic structures in a multitude of applications [1]. They have inherent advantages of higher specific strength and stiffness [2-4]. By utilizing these advantages, composite materials may be suited for long tube and shaft applications, especially those subjected to complex loading. The mechanical properties of composite materials can be tailored to increase the torque capacity as well as the operation speeds during power transmission [5]. Additional merits of composite materials specific to rotating shafts include: increased natural frequencies, high damping capacity, reduced vibration and noise, good corrosion resistance, light weight, and high fatigue life (when compared to conventional engineering materials) [6].

In high performance applications, primary structural components are often required to carry multiple loads while maintaining light weight. Composite tubes in particular are used in many different transportation applications, from aircraft propulsion to automotive and marine drive shafts.

In the case of a drive shaft, slight misalignments between coupled components can induce complex loads. Complex loads on tubes have been studied for many years, although viable engineering solutions to such applications remain a continued focus [7]. The effects of these loads are exacerbated by the high rotating speeds achieved during

operation. In addition to torsion, centrifugal forces cause bending in drive shafts. Therefore resistance to bending is imperative. Furthermore, when tubes are used to transmit torque, power transmission can be improved through use of composite materials and the subsequent reduction of inertial mass and structural weight [8].

Analytical solutions are available for torsion, bending, and compression of thin walled isotropic cylinders [9, 10]. Similarly, anisotropic thin walled tubes, i.e. composite tubes, have effective, albeit more complicated, prediction methodologies [11]. Both of these material classes suffer from the same problem. As the wall thickness decreases so does the sensitivity to manufacturing defects and thus premature failure due to buckling. Significant research effort has considered the buckling behavior of thin cylindrical shells [12-14]. In particular, the performance of thin walled tubes for combined loading has been the focus of much scientific and engineering literature, as its implications affect many applications. Material nonlinearity, imperfect geometry, and residual stress all lead to premature failure of thin walled structures. The buckling limitations of thin-walled cylinders have been mitigated with the implementation of various lattice-like and ring reinforcements [15-17].

Composite materials are typically anisotropic, a fact that can be exploited using textile forming machinery to align material anisotropy appropriately along expected load paths [15]. However, material anisotropy significantly complicates the design and analysis requirements, as material properties necessary for predictive models are difficult to obtain [18]. As an example, carbon fiber reinforced composites can be successfully used in tubular drive shafts, although they have a lower modulus of elasticity than steel. A reduced elastic modulus (compared to steel) for drive shaft applications has been demonstrated to act as a shock absorber, reducing the dynamic stresses experienced in other drive train components and extending the life of the drive train [8].

Perhaps a lower torsional stiffness composite drive shaft may be acceptable if the bending strength is sufficient, resistance to buckling increased, and the weight reduced. In an attempt to develop a tubular composite drive shaft with specific improvements of reduced weight, increased torsion and bending resistance, and imperfection-sensitive behavior, we outline a CAD-based process and highlight the results using commercially available engineering software.


MATHEMATICAL DESCRIPTION OF TUBES IN TORSION

The effect of torque on tubes is of primary concern when designing a drive shaft. When an external torque is applied to a tube, a corresponding internal torque is created. Torque tends to twist tubes or shafts along the longitudinal axis. Mechanics of materials provides a basis for the treatment of uniform cross section tubes made from isotropic materials. The following information applies to tubes and will serve as the basis for obtaining the initial target design. Target values are chosen to be comparable with a commercially available automobile racing drive shaft (a.k.a. half-shaft).

Using Hooke’s law, the maximum shear stress of a tube is given by the torsion formula = (1)

Where the polar moment of inertia of the shaft cross sectional area is given by the following relationship = − (2)

The angular twist of a tube is given by = (3) from this relationship we can solve for the torque according to = ∗ (4) where τmax is the maximum shear stress J is the Polar moment of inertia Ri is the inner radius Ro is the outer radius θ is the angular deflection a.k.a. angle of twist l is the length of the tube G is the shear modulus Equations 1-4 are used to determine the initial required tube mechanical properties.

Table 1 Required Mechanical Properties and Targets Reference Analysis Input/ Target Output

1. Properties 2. Standard Driveshaft

3.Composite Tube

4.Optimization Starting Point

5.Carbon Open

Structure* Material 4130

Steel Carbon Fiber

Fabric Carbon

Fiber Yarn Length (mm) 431.8 431.8 431.8

Outer Diameter (mm)

15.88 61.14 60.96 60.96 (max)

Inner Diameter (mm)

9.5 60.33 54.61 54.61 (min)

Weight (g) 1120.37 260.82 2085.16 280.09 Weight

Reduction {reference} 77% N/A 75%

Target Stiffness (Nm/rad)

1005.2 1510.1 N/A 1510.1

* Tube Weight Reduced 90% via Shape Optimization

DESIGN TARGETS Two composite tubes are designed according to

specifications of Table 1. One tube is made from a novel open structure formed with a Maypole braiding machine (Columns 4 and 5 from Table 1. A second tube is formed from

conventionally braided fabric and is a thin walled tube with full coverage (Column 3 of Table 2). Target values are based on reduction in weight and increase in stiffness over the drive shaft reference (Column 2). The goal of Table 1 is to produce a 50 percent increase in torsional stiffness and a 75 percent decrease in mass for the same length reference steel shaft. The outer diameter of column 4 is determined by geometric constraints of the application and the inner diameter is determined such that the target value of column 5 in Table 1 results in a 90 % reduction in mass (the maximum reduction allowed by ANSYS Workbench Shape Optimization) from the initial mass of Column 4. Column 5 is therefore 25 percent of column 2.

SHAPE OPTIMIZATION AND CAD-BASED DESIGN PROCESS

CAD based designs evaluated in FEA have been used as a design and verification method for some time and are becoming a standard procedure. Recent developments in mathematics, computer software, and mechanics have led to a dramatic change in the modern engineering paradigm [19]. Figure 1 depicts the CAD-based design process used to develop the open architecture composite tube.

Figure 1 CAD Based Optimization Design Process

A circular tube with uniform cross section is an ideal shape for pure torsion [20]. However, merely reducing the thickness of the tube as a means for weight reduction tends to cause the tube to fail prematurely due to buckling [21]. Therefore, to carry both the required torsion and bending loads with a uniform cross section tube, heavy and excessively strong tubes are required. An optimal tube that can be designed to have sufficient torsional and bending stiffness is desired. For this we use a CAD-based design process to perform topological optimizations and predict optimal tube geometry.

Topological optimization is a form of "shape" optimization. The ANSYS Shape Optimization algorithm used seeks to find the most efficient elements in a volume of material, given a specified load and target reduction in volume. The “efficient” elements in the mesh are those which contribute most to the global stiffness (or, equivalently, minimize the compliance) of the overall structure [22].

The optimization objective is stated mathematically as follows:

Minimize (5)


Subject to constraints

≤ 0.9 (6)

Do = 60.94 mm (7) Di = 54.61 mm (8) l=431.8 mm (9)

= 1510.1 (10)

Do and Di are the “essential dimensions” required by ANSYS Workbench i.e. maximum and minimum diameters determined for the design space. This optimization is performed multiple times using an isotropic material model with CAD models containing different size holes, all allowed sufficient iteration to attain the targeted 10 percent mass of Column 5.

For purposes of mass reduction, shape optimization of a tube in pure torsion simply produces a tube of reduced thickness. Thus, seed holes were placed in an alternating manner along the length of the tube to predispose a space truss configuration. The stress concentration regions near the holes only slightly degrade the stiffness [23]. However, material in bands along the principle shear axis contributes significantly to the global stiffness of the tube. Thus, the seed holes cause a helical distribution of material and it is the helical bands that contribute most to the part stiffness. The placement of the seed holes also determines the helical pitch of the optimized structure. In this case seed holes were placed to force a 45 degree helix similar to the angle in the full coverage braided sleeve (column 3, Table 1). Figure 2 illustrates the initial tube geometry before using the shape optimization algorithm in ANSYS Workbench. Figure 3 shows the optimal distribution of material after performing shape optimization.

The floating remnants seen in Figure 3 are simply a visualization and discretization anomaly. During optimization each element in the mesh is weighted based on its contribution to the design parameter, in this case maximizing global static stiffness. The representation shown in Figure 3 has reduced the visible elements to only those which are the highest rated by that standard. Reducing the visible element count, done to allow easy understanding of the shape features, allows the appearance of disconnected elements which would be reconnected should the marginal elements also be shown.

CAD-BASED MODELING APPROACH A CAD-based approach is employed to interpret the

results of the shape optimization performed in ANSYS Workbench. Multiple optimization models are compared, and trends in the resulting geometries are recognized. The most common resulting shape is chosen for geometric model fitting. This approach is useful as an initial design input and is the exact shape optimized geometry if not precisely reproduced, but the trend is maintained. The level of interpretation is left to designer’s discretion and requirements.

Second, two analytical models derived from the kinematics of the braiding machine components are used to produce the final three dimensional CAD models. The yarn paths are generated and the three dimensional geometry data exported to form CAD models. These models facilitate

visualization, design parameterization, and subsequent finite element analysis. The details of the kinematic braiding machine model can be found in reference [24].

Figure 2 Initial Tube with “Seed” Holes

Figure 3 Shape Optimized Tube Geometry

First, helical protrusions are used to approximately fit the distribution of material seen in Figure 3. Figure 4 shows the shape optimized CAD interpretation. Longitudinal elements are also incorporated in the CAD model to improve bending resistance of the tube. The CAD model of Figure 4 contains the pitch length and diameter of the open architecture composite tube which is used to define the analytical models.

Figure 4 Solid CAD Model Superimposed on Shape

Optimized Surfaces


Figure 5 Simple Helical CAD Model

Figure 6 Undulation Helical CAD Model

Figure 5 shows the first CAD based analytical model, named simple helical CAD model. This model represents the simplest case for embodying the “shape optimized” geometry of the composite tube. This parametric model is relatively easy to construct using common CAD features.

Figure 6 shows the second CAD based model named the undulation helical CAD model. This model represents a more geometrically accurate case for exhibiting the “shape optimized” geometry of the composite tube with regard to the actual tube formation geometry expected from a Maypole braiding machine. Maypole braiding machines form a structure by undulating yarns that travel in a circular fashion. The yarns interlace by moving over and under in the intersections. This model is also parametric but requires considerably more computational effort to generate.

MANUFACTURE OF COMPOSITE TUBES As stated previously, the shape optimization geometry of

the open-architecture composite tube considered in this work is recognized as one formed using a Maypole braiding machine. For information regarding braiding and braiding machinery see reference [25]. The loading pattern of yarns required to form this architecture is determined, and an aluminum tube serves as a formation mandrel to produce the desired major dimensions. Yarns referred to as axial yarns are fed through the braiding machine and incorporated along the length of the braided structure to increase the bending resistance. It should be noted that the intersecting joint structure of the simple helical CAD model cannot be reproduced by the braiding machine. However, rigid joints are

formed by the epoxy resin applied in the yarn interlacing regions.

Epoxy Resin Pre-impregnated Yarns

Epoxy resin pre-impregnated carbon fibers are used to manufacture the braided open architecture tube. These fibers are combined to make larger yarns. The benefit of resin pre-impregnated yarns is that they do not require subsequent resin infusion and thus simplify the manufacture of open architecture composite tubes.

Material Selection

The material properties of the pre-impregnated carbon fibers as cured composite are provided by the manufacturer (TCR Composites) in Table 2. Table 2 Material Properties of Typical Pre-impregnated Carbon Fiber Composite with 60% Fiber Volume [26]

Tensile Strength

Tensile Modulus

Percent Elongation

Weight/Length of Pre-impregnated Yarn

2.17 GPa 149.62 GPa 1.3 % 3.37 g/m Open Architecture Composite Tube

The open architecture composite tube is shown during

manufacturing in Figure 7. After thermally curing the braid, the aluminum mandrel is removed. The result is a lightweight rigid open architecture composite tube seen in Figure 8.

Figure 7 Manufacturing of Open Architecture Tube Using a Maypole Braiding Machine and Aluminum

Mandrel Full Coverage Composite Tube

A full coverage composite tube is constructed from biaxial braided carbon fiber sleeve. The major dimensions are approximately the same for both composite tubes. In order to make a careful comparison between the two tubes, the braided biaxial sleeve was chosen to provide an approximate weight, when infused with epoxy resin, to the open architecture composite tube. The ratio of weight contributions due to the


carbon fibers and resin was considered and approximately matched between the two tubes.

Figure 8 Manufactured Open Architecture Composite

Tube

The full coverage tube was formed by placing dry biaxial braided sleeve over a formation mandrel and infusing with epoxy resin. In this method, known as wet layup, resin is applied until the fabric is saturated, and then the tube is cured for 24 hours. Once the composite tube is rigid, the formation mandrel is removed. The manufactured full coverage composite tube is shown in Figure 9.

Figure 9 Manufactured Full Coverage Composite Tube

The final design specifications for each tube are listed in Table 3. Table 3 Final Design Specifications for Composite Tubes

Tube Identification Diameter Length Weight (mm) (mm) (g)

Open Architecture 60.96 568.33 138.95 Full Coverage 60.96 561.98 133.64

TESTING In order to test the composite tubes, a filament winder is

instrumented with dial calipers to measure the deflection from both ends of the composite tube which are secured using two three jaw chucks. One jaw is fixed and the other is loaded using a moment arm and steel weights. Figure 10 and Figure 11 show the torsion testing apparatus for both tubes. For each experiment, the dial indicators are zeroed to measure relative displacement, the moment arm distance is set, and steel weights are gradually added sequentially to load the composite tubes. Several tests were performed by varying the moment arm distance with good repeatability and linear behavior. All measuring devices are kept at constant radii and are nearly perpendicular to measured deflecting motion. Total angular deflection is kept within 14 degrees (.244 rad) such that Taylor series small angle linearization applies with error less than 1%.

Results of Testing and Simulation

The results of the physical testing and finite element analysis are shown in Figure 12.

FINITE ELEMENT ANALYSIS Finite element analysis is performed in ANSYS

Workbench using the CAD models presented in Figure 5 and Figure 6. The finite element models are imported, meshed, and a torsion load and fixed end boundary condition are applied. Both models use the same element types and an isotropic material model based on Table 2. The parameters used in the finite element analysis are listed in Table 4.

Figure 10 Testing of Open Architecture Composite Tube

Figure 11 Torsion Testing of Full Coverage Composite Tube

DISCUSSION The results of the finite element analysis for both models

show a comparable distribution of displacement. The magnitude of displacement for the two models is within 20 percent as seen in Figure 13 and Figure 14. The primary difference between the models is in the interaction of the yarns. The helical model is computationally simpler, but assumes each yarn intersects perfectly, as they share nodes through the centerline.

The results for deformation of the simplified model made with helical elements are shown in Figure 14. The maximum deflection is 1.07 degrees.


Figure 12 Testing Results

Table 4 ANSYS Workbench Meshing Parameters and

Settings ANSYS Workbench

Mechanical Parameters Values and Settings

Simple Helical Model

Undulation Helical Model Meshing Method Tetrahedrons Tetrahedrons Algorithm Patch Independent Patch Independent Minimum Edge Length 0.5879 (mm) 0.5879 (mm) Defeaturing Size Limit 0.5080 (mm) 0.5080 (mm) Number of Nodes 4718635 4381458 Number of Elements 3168007 2890748

The results for deformation of the model with realistic

undulation are shown in Figure 13. The maximum deflection is 1.33 degrees.

Figure 13 Finite Element Analysis of Mathematical

Model with Undulation

The undulation model requires significantly more computation and requires a complex meshing scheme. Meshing the simpler helical protrusion model is convenient because the yarn intersections have larger contact area through which multiple elements can join the yarns together. However, the more accurate undulation intersections are in nearly tangent contact, and require de-featuring of the part to generate a complete mesh. This iterative process to de-feature the part increases meshing time and does not perfectly

preserve the details in the model as was the intent of a more complex geometry, although it is a more realistic representation of the actual geometry of the open architecture composite tube.

Figure 14 Finite Element Analysis of Helical Model

without Undulation

The test results show that the open architecture tube has less torsional stiffness. It is however, observed to be noticeably stiffer radially and longitudinally. Securely holding the open architecture tube is difficult. The reduction in stiffness of the open architecture tube is believed to be a result of how the end caps are attached. The compliance of the end caps is currently under investigation. The closed tube has a higher stiffness, but the tendency to buckle was observed, as it is considerably less stable radially. The experimental results from torsion testing of the solid tube and the open-architecture tube suggest that although the full coverage tube made from biaxial braided sleeve has a higher stiffness the critical buckling load occurs before the peak expected torque.

The results of employing simple isotropic material based finite element models demonstrate that a significant difference of 148 percent exists in the ability to predict actual deformation of the composite open structure tube. It is likely that improving the open structure end cap connections i.e. eliminating compliance in the connection will produce results closer to the finite element predictions.

The finite element models however are in much closer agreement. The truss-like simplicity of the helical model, ease of mesh generation, and rapid solution time suggest that it may be beneficial as a design tool for developing open-architecture composite structures. Although the structures are actually made from anisotropic material, when considering cases such as tension, compression, bending, and torsion where the yarns may be conveniently aligned with the expected loads, the methodology presented is useful in initial evaluations. Reducing the complexity of engineering analysis by employing simple isotropic material finite element models reduce computation costs and will decrease product development time.

CONCLUSION We have developed a light weight composite tube for use

in torsion applications. A design methodology based on shape optimization and finite element analysis is presented to predict


the ideal shape of an open-architecture composite tube to be loaded in torsional applications such as drive shafts. Design goals are made and certain features are input to a topological optimization algorithm. The results of the optimization are altered from their triangulation form into a solid model by recognition of patterns in the shape. A simplified helical model and an undulating helical model are based on these patterns and evaluated in finite element analysis. A Maypole braiding machine is used to manufacture the open architecture composite tube. A solid full coverage composite tube of similar weight and major dimensions is also manufactured and compared. The open- architecture composite tube is less stiff in torsion but is observed to be stiffer in the radial and longitudinal directions. The finite element helical model and undulation model analyzed are in close agreement. This result demonstrates that a simplified approach i.e. using helical models rather than undulation models may be used in the design of braided structures. We have shown a design process which may be applied to future open structure geometries in general. Using the method presented, shapes may be found whose properties in certain loading conditions will surpass those of metallic and full-coverage composite alternatives.

ACKNOWLEDGEMENTS The authors would like to acknowledge the Auburn Office

of University Scholars Spirit of Auburn Presidential Scholarship, Alabama Space Grant Consortium NASA Training Grant #NNG05GE80H, TCR Composites, and AU Department of PFEN, Hayes Johnson, and Jonathan Hebert. Dr. Stephen Bigbee provided substantial suggestions.

BIBLIOGRAPHY [1] Degarmo, E., Black, J., Kohser, R, “Materials and Processes in Manufacturing”, 9th Ed. 2003. [2] Peters, S., “Handbook of Composites”, 2nd Ed., 2, 1998. [3] Sivakandhan, C., Suresh Prabhu, P., “Composite Drive Shaft is a Good Strength and Weight Saving to Compare Conventional Materials Design and Analysis of E Glass/Epoxy Composite Drive Shaft for Automotive Applications”, European Journal of Scientific Research, 2012. [4] Jones, R., Mechanics of Composite Materials, 2e, McGraw-Hill Book Company, New York. [5] Montagnier, O., Hochard, C., “Optimisation of a high speed rotating composite drive shaft using a genetic algorithm –Hybrid high modulus-high resistance carbon solutions”, Submitted to Composite Structures 2011. [6]Jin Kook Kim. Dai gilLee, and Durk Hyun Cho, “Investigation of Adhesively bonded Joints for Composite Propeller shafts”, Journal of Composite Materials, Vol 35 No11, pp 999-1021. [7]A. G. Greenhill, On the Strength of Shafting When Exposed Both to Torsion and to End Thrust, Proceedings of the Institution of Mechanical Engineers 1883. [8] Chowdhuri, M., Hossain, R., “Design Analysis of an Automotive Composite Drive Shaft”, International Journal of Engineering Technology, Vol 2.

[9] Fuchs, H., Hyer, M., “The nonlinear prebuckling response of short thin-walled laminated composite cylinders in bending”, Composite Structures, 1996. [10] Donnell, L., “A new theory far the buckling of thin cylinders under axial compression and bending”, Trans. ASME, 56 (1934) 79.5-806. [11] Bert, C., Kim, C., “Analysis of Buckling of Hollow Laminated Composites Drive Shafts”, Composites Science and Technology, 1995. [12] Lancaster, E., Calladine, C., Palmer, S., “Paradoxical buckling behavior of a thin cylindrical shell under axial compression”, International Journal of Mechanical Sciences, 2000. [13] von Karman, T., Dunn, L., Tsien, H-S., “The influence of curvature on the buckling characteristics of structures”, Journal of the Aeronautical Sciences, 1940. [14] Pircher, M., Bridge, R., “Buckling of thin-walled silos and tanks under axial load—Some New Aspects”, Journal of Structural Engineering, 2001. [15] Li, Z-M., Shen, H-S., “Postbuckling analysis of three-dimensional textile composite cylindrical shells under axial compression in thermal environments”, Composites Science and Technology, 2008. [16] Yazdani, M., Rahimi, H., Khatibi, A., Hamzeh, S., “An experimental investigation into the buckling of GFRP stiffened shells under axial loading”, Scientific Research and Essay, Vol. 4, 2009. [17] Prabu, B., Rathinam, N., Srinivasan, R., Naarayen, K., “Finite Element Analysis of Buckling of Thin Cylindrical Shell Subjected to Uniform External Pressure”, Journal of Solid Mechanics, 2009. [18] Lawrie, D., “Development of a High Torque Density, Flexible, Composite Driveshaft”, Proceeding from the American Helicopter Society 63rd Annual Forum, Virginia 2007. [19] Altair Student Guides-- CAE and Design Optimization—Basics, http://altair-india.com/edu/students/html/publiccontent/projects_about.htmAccessed 8/19/2012. [20]Banichuk, N., Ragnedda, F., Serra, M., “Optimum shapes of bar cross-sections”, Struct Multidisc Optim 23, 222-232. [21] Mitchell, J., “Experimental and Numerical Investigations Into Optimizal Partial Concrete Filling of FRP and Steel Tubular Poles” M.S. Thesis submitted to Queens University, 2008. [22]ANSYS Theory Guide. [23] Bauchau, O., Krafchack, T., Hayes, J., “Torsional Buckling Analysis and Damage Tolerance of Graphite/Epoxy Shafts”, Journal of Composite Materials, Vol. 22, 1988. [24] Branscomb, D., A Machine vision and Sensing System for Braid Defect Detection, Diagnosis and Prevention During Manufacture, Auburn University M.S. Thesis, 2007. [25] Branscomb, D., Beale, D., Broughton, R., “New Directions in Braiding”, Journal of Engineered Fibers and Fabrics, Accepted 2012. [26] TCR Composites Mechanical and Physical Properties Webpage, http://www.tcrcomposites.com/tcr_prepregs.html, accessed 8/15/12.


AUTHOR INDEX

ABDELHADI, OUSAMA M. 52, 78

ABDEL-KHALIK, SAID 156, 165

ADAMS, AARON L. 47, 174

AL-ANSARY, HANY 156

ALEXANDER, DONALD P. 258

ALI, MOHAMMED 228

ANUBI, OLUGBENGA M. 120

ARREDONDO, RODRIGO 24

ASHFORD, MARCUS D. 47, 139, 174

BARTH, ERIC J. 97

BEAL, DAVID 270

BRANSCOMB, DAVID 211, 266, 270

BROUGHTON, ROYALL 270

CHAN, WING 47

CRANE III, CARL D. 120

DAVIS, JASON 139

DILLON-TOWNES, L.-A. 71

DOOSTTALAB, ALI 244

DOOSTTALAB, MEHDI 244

DRABO, MEBOUGNA L. 47, 139, 174

DRIVER, TAD 139

EBRAHIMI, SAYNA 84

EGARIEVWE, STEPHEN 47

ELGIZAWY, A. 179

EL-LEATHY, ABDELRAHMAN 156

FRANCHETTI, MATTHEW J. 131

FUNG, ALAN S. 203

GARCIA, DANIEL A. 11, 24

GOLOB, MATTHEW 156

GURLEY, AUSTIN 270

HEWLIN, Jr., RODWARD L. 32

IBRAHIM, ESSAM A. 228

IBRAHIM, ISRAR BM 90

ISAAC, MITCHELL J. 211

IVANCO, MARIE L. 71

JAMES, RALPH B. 47

JETER, SHELDON 156, 165, 187, 258

KASSU, ASCHALEW 47

KHALID, ADEEL 3

KIZITO, JOHN P. 32, 221, 236

KRESS, CONNOR G. 131

LADANI, LEILA 52, 78

LURBE, YOJANS 11

McDONALD, DALE B. 105

McENROE, PATRICK 197

McFALL, KEVIN 197

MO, HUAN 40

MOHAMED, AHMED 63

MOHAMMADI, MOHAMMAD 244

MOHAN, RAM 63

MORRIS, MELISSA 11, 24, 148

MUHAMMAD, IBRAHEEM R. 221, 236

MUNN, BRIAN S. 253

OLCMEN, SEMIH 139

PATEL, DARSAN 120

PHAN, ANH-VU 84

PIDAPARTI, RAMANA 18, 40, 90

PLUNKET, CAITLIN 266

RADJA, ASJA 47

RAJAB, HUSAM H. 179


RAJENDRAN, A. M. 57

RODEKOHR, CHAD 211, 266

ROLLE, TRENICKA 18

ROOP, JONATHAN D. 187

SADOWSKI, DENNIS 14

SCHINETSKY, PHILIP 156

SCHLENDER, ERICH H. 113

SHAHZAMANIAN, M. M. 57

SHELINE, LIAM R. 165

SOTO, VICTOR 11

SPIRO, GREGORY M. 258

STEPHENS, JAKE 187

TADEPALLI, TEZESWI P. 57

TOSUNOGLU, SABRI 11, 24, 148

WOLF, TREVOR D. 258

YANG, GE 47

YANG, LE 40

YUILL, AUSTIN 266

ZABIHIAN, FARSHID 203

ZHU, YONG 97


ISBN 978-1-4675-5170-0


section 7 design & manufacturing€¦ · widely employed tightening strategies in the...

Documents