precision maching - contact length models

8/10/2019 Precision Maching - Contact Length Models

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Study Paper #1Examining the Significance of Contact Length in

Machining

Course: MESF 5570Precision Machining

Professor: Y. Gao

Date: Dec 10, 2014

Name Student ID Email

CHAN Sunny Yuk Lam 2013 8736 [email protected]


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List of Symbols

Contact Length Maximum Contact Length

Local Contact Length

Contact Area Depth of Cut Feed Rate Workpiece Speed Cutting Speed Rake Angle Contact Time Chip compression Ratio

Introduction

The tool and chip interface is very complex, cutting parameters and material properties

play a big role in determining the contact length. By knowing the contact length we can find

behaviours in heat distribution, energy consumption and machinability of the workpiece

material. There are three deformation zones, the primary zone is responsible for chip formation

while the secondary zone is the complex interaction between rake face and the chip [2]. The

third zone is responsible for ploughing and flank contact [2]. The relationship between contact

length and cutting speed will be presented in this paper, as well as some new contact length

prediction methods. Experimental work of the machining process shows different trends with

different materials and varying cutting speeds. The tool-chip contact length is dependent on the

workpiece material and cutting speed [1, 7].

The Effects on Cutting Length with Cutting Speed

High Speed Machining (HSM)

HSM short form for high speed machining is popular in recent years because it boosts

productivity for poor machinability materials. Benefits of HSM include the ability for direct

machining of hardened materials, lower cutting forces and possibility for improving surface

finish *1+. However, the toll is high strain ratesand nonlinear plastic deformation upon chip

formation as well as cutting tool degradation from high stresses and temperature spikes

[1].Further research in HSM needs to be carried out for various materials and cutting conditionsto develop reliable models.

Iqbal et al. has developed contact length models for high speed machining of AISI 1045

steel and Ti6Al4V titanium alloy [1]. Conventional to high speed cutting ranges are shown in

Figure 1 for typical workpiece materials. AISI 1045 steel is commonly used in forging of various

transportation components such as crankshafts, gears, rails and rail axles. Ti6Al4V titanium alloy


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is popular in the aerospace industry and the biomedical field due to its superb strength to

weight ratio and high corrosion resistance. Some applications include aircraft engine fan blades

and biomedical screws.

Compared to steel, titanium alloys have poor machinability characteristics.

Heat dissipation is weak and mostly conducted through the tool edge. The tool edge also

encounters high stress because of small contact area on a high strength workpiece. Reaching

above 500, uncoated tool materials will react with titanium alloys at the tool-chip interface[1]. Thus a reliable model for contact length prediction will result in better control of heat and

forces on any workpiece.

Figure 1: Cutting speed ranges for typical workpiece materials [1].

The experimental setup consists of a lathe machine, a tungsten carbide blade and a

Polyvar optical microscope. Dry orthogonal ( ) cutting tests were performed on theworkpieces at various cutting speeds and depth of cuts. Then the contact length is measured by

identifying trace marks on the tungsten carbide blade under the microscope. Figure 2a) and 2b)

displays two different trends for the workpieces but the results are consistent for every cutting

depth. In Figure 2a), as the cutting speed increases for AISI 1045, the contact length decreases.

However in Figure 2b) for Ti-6Al-4V, the trend is completely different, the contact length

increases then decreases as cutting speed increases. Contact length decreases as it

reached , entering the HSM range for titanium alloys (Figure 1). Iqbal et al. proposedthe phenomenon is due to adiabatic shear banding for low thermal conductivity materials [1].

Adiabatic shear banding occurs when heat and stress is localised on the chip whichdecreases the tool life but it benefits from lower cutting forces and improves chip rejection [1].

The adiabatic shear banding is measured by its frequency where higher values mean the

material has a high thermal conductivity and the phenomenon will not occur until reaching

higher cutting speeds. Figure 3a) and 3b) shows that shear banding bandwidth decreases as

cutting speed increases for both Ti-6Al-4V titanium alloy and medium carbon steel respectively.


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It is evident that contact length shortens at a higher cutting speed for the titanium alloy due to

increased chip segmentation frequency [1].

Figure 2: Contact length at various cutting speeds and uncut chip thicknesses for a) AISI 1045

steel and b) Ti-6Al-4V titanium alloy [1].

Figure 3: Shear bandwidth at various cutting speeds for a) Ti-6Al-4V titanium alloy and b)

medium carbon steel [1].

In another high speed machining study, AISI 4140H steel alloy was used as the

workpiece material to determine the contact length at various high cutting speeds. The

experimental apparatus used is similar to that of Iqbal et al. [1] except for the use of SEM

microscope to measure the contact length on the rake face. Abukhshim et al. [3] noticed that

cutting speed has a significant effect on contact length and that contact length models

conducted with conventional cutting speeds cannot be extrapolated to HSM speeds. From

experiment results, at cutting speeds above , the contact length will increase, whilespeeds below shows a decreasing trend which matches with conventional contact


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length models [3]. This cutting speed lies in the transitional speed regime for steel alloys shown

in Figure 1. The data set shows consistency with this trend for the 2 feed rates ( and ), and various cutting lengths as seen in Figure 4 and 5.

Figure 4: Contact length with cutting speeds at various cutting lengths for AISI 4041H steel

( ) [3].

Figure 5: Contact length with cutting speeds at various cutting lengths for AISI 4041H steel

( ) [3].


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Abukhshim et al. explained that the sudden change in trend at critical speed is called the seizure phenomenon. The heat generated from friction between the tool andthe workpiece causes full sticking where it replaces the sliding region of the shear zone. Shown

in Figure 6, the compressive stress acting on the cutting tool at the contact area is equal to the

yield stress of the workpiece material. [3]

Figure 6: Compressive stress at the tool-chip interface and yield strength with temperature

during machining of AISI 1045 steel [3].

Conventional Cutting Speeds

Ojolo and Awe [4] conducted tests on unspecified aluminum alloy and mild steel

workpieces to determine the effects of cutting stability with varying feed rates for two different

cutting speeds and depths of cut. The results correspond to those discussed in the previousinvestigations for conventional cutting speeds. Lower forces were observed when cutting at a

higher speed which increases the contact length [4]. However, contact length decreases as

cutting speed is increased from 47m/min to 71m/min for both materials at 2 cuts of depth as

shown in Figure 8 and 9. It is concluded that cutting stability decreases with increase in contact

length and/or depth of cut.


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Figure 8: Contact length with feed rates for 2 cutting speeds of aluminum alloy and mild steel

( ) [4].

Figure 9: Contact length with feed rates for 2 cutting speeds of aluminum alloy and mild steel

( ) [4].

The difference between milling and grinding process is the tool. Milling tools have

evenly spaced blades/inserts located at the ends, on the face and/or on the sides. Abrasive

wheels are typically used in grinding, it contain an indefinite number of cutting grains in random

orientation, spacing and relative grain heights. The depth of cut in grinding is small as it is

usually the final step in machining to produce a quality machined part within its geometric

tolerance.

Cong et al. [6] used a modified Peklenik method called the Critical Contact State Mode

to measure the contact time of each grain cutting through the workpiece. The contact length ()

0.45

0.55

0.65

0.75

0.85

0.95

1.05

0.25 0.3 0.35 0.4 0.45 0.5 0.55

ContactLe

ngth(mm)

Feed Rate (mm/rev)

MS 47m/min MS 71m/min Al 47m/min Al 71m/min

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.05 0.1 0.15 0.2 0.25 0.3

ContactLength(mm)

Feed Rate (mm/rev)

MS 47m/min MS 71m/min Al 47m/min Al 71m/min


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can be calculated (Equation 1) by multiplying the contact time () with the workpiece speed ()[6]. In Figure 10b) An insulated platinum wire is embedded in the workpiece material to form a

thermocouple with the workpiece. As a grain cuts through the thermocouple, platinum is

smeared onto the workpiece surface creating a thermoelectric signal [6]. Each signal peak refers

to one contact time of a single grain shown in Figure 10a).

(1)

Figure 10: Experimental setup for the Peklenik thermocouple method [6].

It is important to note that the geometric contact length can never be represented as

the real contact length. It is evident in Figure 11 that the real contact length is longer than the

geometric contact length by several folds; it is more significant as the depth of cut is reduced. 45

steel is the same material, AISI 1045 steel alloy, used by Iqbal et al. [1] for studying the contact

lengths during high speed machining. Same results is observed, contact length decreases as

cutting speed increases.

Figure 11: Contact length with depth of cut for 2 cutting speeds of AISI 1045 steel [6].


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Tool Rake Contact Length

The contact length is conventionally measured on the rake face of the tool because it is

observable and can be directly measured on a microscope. However, the process is tedious and

time consuming. Ozlu et al. simulated the contact length based on their model with cutting

speed. A critical speed was determined where the sticking zone contact length () vanishes forcoated carbide and CBN cutting tools on a AISI 1050 steel alloy workpiece [2]. From the

simulation, shown in Figure 12a) and 12b), the fully sliding contact length is observed for coated

carbide cutting tool at 600m/min and CBN cutting tool at above 1250m/min, respectively. Even

for different cutting materials, the trend for contact length remains the same, as cutting speed

increases the contact length decreases.

CBN cutting tools has a lower sticking region ratio than coated carbide cutting tools but

it is difficult to restrict the contact length. Cutting speed needs to be increased by 10 times to

reduce the cutting length by 0.5, therefore it is not as effective for using CBN cutting tools to

reduce sticking region contact length at high speeds. Figure 13b) through 13d) showcases the

cutting marks on a coated carbide cutting tool for increasing cuttings speeds of 100m/min,

300m/min and 600m/min, respectively [2]. The experiment confirms that the simulation is

accurate in showing the narrowing of sticking region as cutting speed increases.

Figure 12: Simulation of contact length ratio with cutting speed for a) coated carbide and b) CBN

cutting tools [2].


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Figure 13: 200x magnification of coated carbide cutting tool rake face with cutting speed at b)

100m/min, c) 300m/min and d) 600m/min [2].

Contact Length Prediction Models

Iqbal et al. [1] has conveniently summarized some reviewed contact length models in

Table 1. Each model gives slightly different results from one another but it shows similar trends

which has been proven experimentally. The presented models are either as functions of uncut

chip thickness () or chip compression ratio (). Chip compression ratio is the ratio

between the real chip thickness () and the uncut chip thickness [1]. Some models areexpressed by the geometry of the chip after sheared deformation. Toporov and Ko stated that

the general laws of stress distribution and change of coefficient of friction along the toolchip

interface have not yet been found [5]. Therefore there are such vast diversity of results from

the combinations of workpiece materials, cutting tool materials and assumption coefficients.

Prediction models are classified in two groups, simulated and practical methods. Simulated

methods discussed in this paper are Slip-line method [5] and Genetic Programming [7]. As for


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practical methods, quick-stop method [8] and Applied Power Source method [9, 10] will be

presented.

Table 1: Summary of contact length models [1].

Slip-Line Method

The slip-line method proposed by Toporov and Ko [5] is shown in Figure 14. Basically

there are two line fields, ABD and ADE that represents the relationship between the rake tool

face and chip. Line field ABD is called the primary deformation zone, where line segments AB

and AD forms angles with the tool path called shear angles, and respectively. Duringdeformation boundary field AB is represented by the yield stress based on its temperature,

stress and strain [5]. The workpiece material hardens and remains constant in the whole zone

[5]. The secondary deformation zone is formed by line field ADE. Line segment DE is the final

boundary for plastic zone and the line passes the tool rake face and inward chip surface at angle [5]. The tool-chip contact length is represented by line segment AE thus the shear stress at

point E is zero [5]. From geometry and the theory of plasticity, line field ADE will form an

isosceles triangle because and there is no shear stress at point A [5]. Thereforethe expression for tool-chip contact length is given in Equation 2 [5].

( )

(2)


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Figure 14: Slip-line field for tool rake and chip interaction [5].

Genetic Algorithm

The author Zadshakoyan and Pourmostaghimi [7] wrote a genetic program that predicts

the tool-chip contact length based on parameters and results from other experimental works. It

is also agreed that the decrease in tool-chip contact length will decrease the cutting force and

cutting temperature [7]. Genetic programming connects the various relationships of inputs and

outputs to generate an optimized numerical function. This numerical equation can then be

applied to the manufacturing process for the determination of this length with a few numbers

of experiments. *7+ The inputs are the cutting speed, feed rate and depth of cut, while the

output is the contact length. Figure 15 shows methodology for the contact length genetic

program. In order for the genetic program to find the optimum function it has to follow the

following steps:

(I) Generation of a random population

(II) Evaluation of the fitness of all members in the population. Moreover, if a certain

criterion is reached (for example, a certain fitness threshold), the algorithm is

terminated and the member with the highest fitness is selected as the final result.

(III)Replacing the current population by a new population by means of applying genetic

operators (reproduction, crossover and mutation) probabilistically.

(IV) Return to step 2 *7+.


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Figure 15: Methodology of the contact length genetic program [7].

The genetic program started out with an expression such that contact length is equal to

the function of cutting speed, feed rate and depth of cut, given in Equation 3. The chosen

population size is , number of generations is G=2000, mutation rate is andcrossover rate is [7]. Addition of arithmetic operations in Equation 4 and theexperimental cutting parameter limits given in Table 2 results in the final contact length function,

shown in Equation 5 [7].

( ) (3) ( ) (4)

()

( ) [ ( )]

(5)

Table 2: Experimental cutting parameter limits [7].

Figure 16 shows the comparison between several existing contact length models with

the genetic program model. The genetic program model continues to show a decreasing contact

length trend as cutting speed increases for machining of AISI 1017 steel alloy. The decrease in

contact length is related to the thermal softening effect, where at high speeds the heat


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generated from grinding reduces the material flow stress and the friction coefficient () alsodecreases [7].

Figure 16: Comparison of existing contact length models with the genetic program model [7].

Quick-Stop Method

The quick-stop method involves launching the workpiece material towards cutting tools

to mimic orthogonal machining at various speeds [8]. The projectile holding the workpiece

material shoots so quickly that a high speed camera is needed to record the process. To be exact,

each experiment takes approximately five milliseconds to complete [8]. The experimental setup

is shown in Figure 17 with the high speed camera focused on capturing the chip and rake face of

the cutting tool. The workpiece material is an AISI 1017 steel alloy.

Figure 17: Experimental setup for Quick-stop method [8].

The experiment was conducted several times for different rake angles ()andcutting speeds between 25m/s to 60m/s [8]. Photographs captured by the high speed camera

display the chip and tool rake face, as shown in Figure 18 to Figure 20. The contact length model


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is defined in Equation 6 with a coefficient of determination of 0.68 [8]. By pure speculation of

the photographs, the contact length grew longer for deeper depth of cuts and that the chip is

continuous. Figure 21 shows the comparison of existing contact length models with quick stop

method. Contact length approaches zero as the chip compression ratio decreases.

(6)

Figure 18: High speed camera photograph of chip formation for a) , b) [8].

Figure 19: High speed camera photograph of chip formation for a) , b) , c) , d) [8].


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Figure 20: High speed camera photograph of chip formation for a) , b) [8].

Figure 21: Comparison of existing contact length models with the quick-stop model [7].

Applied Power Source Method

The Applied Power Source method (APS) is an iteration of Pekleniks thermocouple

method. Zhou and Lutterwelt [9] proposed that there should be two contact lengths, maximum

contact length () and local contact length (). This is due to the random orientations andspacing of grains on the grinding wheel that causes the scour pattern shown in Figure 22. The

expression for both and is given as:

(3)where: -- or [];

-- Workpiece Speed [];Duration of the signal [];Thickness of the insulation layer [] [9].


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Figure 22: Classification of contact length [9].

The setup of APS method for is shown in Figure 23. The workpiece is cut into halfand put back together with an insulating sheet between them [9]. Each of the two halves

becomes an electrode of the thermocouple. The circuit is completed as the grinding wheel

remains in contact to both surfaces. The maximum contact length is obtained by measuring the

contact time with an oscilloscope shown in Figure 24. The signal read by the oscilloscope should

be a square wave [9].

Figure 23: APS experimental setup to measure [9]. Figure 24: Model for measuring [9].

Similar to Pekleniksmethod the contact time is measured by the first and last contact

of the wheel traveling past the insulated wire [9]. The setup of APS method for is shown inFigure 25. The circuit is completed as the grinding wheel remains in contact with the surface and

the wire. The local contact length is obtained by measuring the contact time with an

oscilloscope shown in Figure 26.


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Figure 25: APS experimental setup to measure [9]. Figure 26: Model for measuring [9].

The author summarizes that the APS method is simple, reliable and produces accurate

results. and can be measured in real time and both signals can be recordedsimultaneously. Since the signals are electric, there are no issues with thermoelectric signal

losses [9]. Relationships between and were observed for machining AISI 1045 steel alloywith depth of cut shown in Figure 27.

i) and increases as increases [9].

ii)

There is elastic deformation between the wheel and workpiece, thereforeand is non-zero for zero depth of cut [9].

iii)

As discussed from before, the geometric contact length is much smaller than

and especially at smaller depth of cuts [9].iv)

Experiments show that is 22-50% longer than . The difference reduces asdepth of cut increases [9].

Figure 27: Comparison of and with depth of cut [9].


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Conclusion

The geometric contact length can only be used as a comparative value to show

the significance in the real contact length. As the depth of cut decreases the contact

length ratio increases. The stochastic behaviour of friction and heat influence the widerange of results we see from each author. It was shown that material properties affect

the trend for increasing or decreasing contact length with increasing cutting speeds.

Some methods for prediction of contact lengths were discussed in this paper. Applied

Power Source method is simple, accurate and reliable.

References

[1]

Iqbal, S., Mativenga, P., & Sheikh, M. (2008). A comparative study of the toolchip

contact length in turning of two engineering alloys for a wide range of cutting speeds.

The International Journal of Advanced Manufacturing Technology, 30-40. Retrieved

November 25, 2014, from Springer-Verlag London.

[2]

Ozlu, E., Budak, E., & Molinari, A. (2009). Analytical and experimental investigation of

rake contact and friction behavior in metal cutting. International Journal of Machine

Tools and Manufacture, 865-875. Retrieved November 25, 2014, from ELSEVIER.

[3]

Abukhshim, N., Mativenga, P., & Sheikh, M. (2004). An investigation of the tool-chip

contact length and wear in high-speed turning of EN19 steel. Proceedings of the

Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 889-

903.

[4]

Ojolo, S., & Awe, O. (2011). Investigation into the Effect of Tool-Chip Contact Length on

Cutting Stability. Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS), 626-

630. Retrieved on November 25, 2014, from Scholarlink.

[5]

Toropov, A., & Ko, S. (2003). Prediction of tool-chip contact length using a new slip-linesolution for orthogonal cutting. International Journal of Machine Tools and Manufacture,

1209-1215. Retrieved November 25, 2014, from ELSEVIER.

[6]

Mao, C., Zhou, Z., Zhou, D., & Gu, D. (2008). Analysis of Influence Factors for the Contact

Length between Wheel and Workpiece in Surface Grinding. Key Engineering Materials,

128-132.

[7]

Zadshakoyan, M., & Pourmostaghimi, V. (2012). Genetic equation for the prediction of

toolchip contact length in orthogonal cutting. Engineering Applications of Artificial

Intelligence, 1725-1730. Retrieved November 25, 2014, from ELSEVIER.

[8]

Sutter, G. (2004). Chip geometries during high-speed machining for orthogonal cutting

conditions. International Journal of Machine Tools and Manufacture, 719-726. Retrieved

November 25, 2014, from ELSEVIER.

[9]

Zhou, Z., & Lutterwelt, C. (1992). The Real Contact Length between Grinding Wheel and

Workpiece A New Concept and a New Measuring Method. CIRP Annals -

Manufacturing Technology, 387-391.

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