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VISVESWARAYA TECHNOLOGICAL UNIVERSITY

DHARWAD- 580 002

A SEMINAR REPORT ON

Probabilistic Analysis for Resolving FatigueFailures of the connecting rod oil hole

Under the Guidance Of

Shri. J Y KUDRIYAVAR

Submitted by

RAJAT KAWAL

DEPARTMENT OF MECHANICAL ENGINEERING

2010

Dept. of Mechanical Engg. S. D. M. C. E. T, Dharwad Page 1


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Probabilistic Analysis for Resolving Fatigue Failures of the Connecting rodoil hole

________________________________________________

VISVESWARAIAH TECHNOLOGICAL UNIVERSITYBELGAUM

S D M COLLEGE OF ENGINEERING & TECHNOLOGY

DHARWAD-580002

DEPARTMENT OF MECHANICAL ENGINEERING

C E R T I F I C A T E

Certified that a seminar entitled Probabilistic Analysis for Resolving Fatigue

Failures of the connecting rod oil hole is bonafied work carried out by

RAJAT KAWAL in partial fulfillment for the award of degree of Master of

Technology in Mechanical Engineering of the Visveswaraya Technological

University, Belgaum, during the year 2009-10 . The seminar report has been

approved as it satisfies the academic requirements.

.. . .

Shri. J Y Kudriyavar Prof. V K HEBLIKAR Prof. A V KULKARNI

(Guide) (HOD) (Seminar Co-ordinator)



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Abstract

This paper describes an application of ANSYS Probabilistic Design System(PDS) as a superior method to efficiently identify the relative influences of randominput variables on fatigue stress and to optimize the chosen random input variable toachieve the desired reliability.

The axial oil-hole of a connecting rod in a reciprocating air conditioning compressor acts as a stress raiser and can lead to early fatigue failures. A parametric3-D finite element model of the connecting rod with a hollow wrist pin was developed to study the fatigue stress. Contact elements were placed between the rod and the pin.The service stresses were simulated by pushing the pin against the connecting rod. A

high predicted fatigue stress at an observed failure origin verified the model.A probabilistic analysis was carried out with three independent random

input variables: the wrist pin bearing ID, the wrist pin OD and the hollow wrist pinbore ID. A macro file was created by a computer program to relocate selected nodes,by which the dimensions defined by the random input variables were automaticallyadjusted during each analysis loop.

The maximum fatigue stress at the failure location was the random output parameter. This random output parameter was explicitly related to the random input variables using the Response Surface Method. The sensitivity analysis revealed that

the wrist pin hollow ID had the most effect on the fatigue stress.Additional PDS runs were performed to optimize the pin ID and tolerance

based on the estimated probabilities of failure using the Monte Carlo simulated cumulative distributions of the maximum fatigue stress. The analysis showed that the

fatigue failures could be eliminated by reducing the wrist pin hollow ID, which wasalso the most cost effective fix.



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CONTENTS

1. Introduction... 05

2. Procedure... 06

3. Analysis.. 07

4. Analysis Results and Discussion.. 11

5. Conclusion. 15

6. References. 16



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1. Introduction

Connecting rod failures at the edge of the wrist pin bearing oil-hole early inthe life of the part initiated this FEA investigation. The purpose was to develop adesign variation that would remove this failure mode. Besides the application of ANSYS PDS described in this paper, other design options that were also examinedincluded:

Adding a groove to the oil-hole end of wrist pin bearing, Reinforcing the external sides of the connecting rod, Tilting the oil-hole, Changing the oil-hole diameter and Adding a chamfer to the oil-hole end of wrist pin bearing .

These design options had a very limited effect and were thus abandoned. Theanalysis then focused on the wrist pin bearing ID and the hollow wrist pin OD and ID.Modifications of these variables are more easily accommodated by Manufacturingand they have less of an effect on cost. ANSYS PDS provides a very efficient way tostudy these design variables simultaneously. More importantly, the traditionaldeterministic analysis method tends to over-design. This can be more costly and couldhave a negative effect on compressor performance due to parts that are heavier thanthey need to be. Therefore, the ANSYS PDS was used to obtain an optimized design.

The wrist pin bearing ID, hollow wrist pin OD and ID were chosen asrandom input variables to the ANSYS PDS. Examination of the finite elementgenerated stress distribution revealed that the maximum fatigue stress at the failurelocation caused the oil-hole fatigue failure. To resolve the failure mode, therefore, themaximum fatigue stress at the failure location was chosen as the random output

parameter.Historic product reliability data indicated that connecting rods without an oil-

hole exhibit sufficient fatigue life. Therefore, a finite element model of the connectingrod and wrist pin without the oil-hole was also analyzed; and the results were used as

the baseline for the allowable fatigue stress.



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2. Procedure

The FEA model included the hollow wrist pin and the connecting rod. The3-D solid model geometry of the connecting rod was extracted from Pro/E software.All unimportant geometric details were deleted to save computation time. The hollowwrist pin was modeled using the ANSYS preprocessor. The connecting rod and wrist

pin model was reduced to a quarter symmetric FEA model as shown in Figures 1 and3.

Contact elements between the wrist pin bearing ID and the wrist pin ODwere used to simulate the hydrodynamic bearing oil film. A uniform pressure wasapplied to pin to simulate the 1000 lb axial load transferred from the piston.Inspection of failed connecting rods indicated the failures at the oil-hole were not

related to the high bearing friction due to bearing failures. Therefore, nocircumferential bearing friction force was included in the model since the friction of a

properly functioning bearing is generally low. Since the inertial loads were small, theywere neglected as well. The crank pin bearing end of the connecting rod was alsoexcluded from the FEA model. Instead, the connecting rod was supported against theaxial push load at the rod end of crank pin bearing.

For probabilistic analysis, the FEA model has to be set up parametrically sothat it can change its dimensions in response to the three random input variables for PDS calculation loops. The solid model was meshed with a sufficiently fine mesh atthe failure location. All nodes located on the surfaces of the wrist pin bearing ID,hollow wrist pin OD and ID were selected and saved to an ANSYS text file. Acomputer program was developed to read in the text file and create an ANSYS macrofile that relocated the selected nodes according to the three random input variables.

Note that special attention was given to the elements associated with the selectednodes so that they remained good shapes after the relocations of the selected nodes.

All random input variables were declared as independent random variables.There were no correlations defined between the random input variables. Thevariations of random input variables were characterized by uniform distributions. Thedescriptions of random input variables and the random output parameter are

summarized in Table 1. As an example, the cumulative distribution function of thehollow wrist pin ID is illustrated in Figure 5. Another macro file was written toretrieve the random output parameter from the ANSYS result file for each PDS loop.



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3. Analysis

The Box-Behnken Matrix Design Method under the Response SurfaceMethod was used for probabilistic analysis sampling. The Box-Behnken Matrix(BBM) Design Method, a design of experiment method, determined 12 sampling

points for three random input variables. Each sampling point defined a set of distinctvalues for all three random input variables. The FE model was then updated accordingto the random input variables. Each sampling point required a PDS loop to computethe random output parameter.

(a) Original Pro/E rod model (b) Simplified rod solid model

(c) Quarterly symmetric FEA rod / pin model with an oil hole

(d ) The finite element meshes near the oil hole. The volume at the bearing contact surfacewas selected to show FEA results in Fig 4

Figure 1. Rod/pin finite element model with an axial oil hole



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(a) Hoop stress (b ) 1 st principal stress (S1)

(c) 2nd

principal stress (S2) (d) 3rd principal stress (S3)

Figure 2. Stress distributions for the model with an axial oil hole (One half of the wrist pinbearing surface contacting the wrist pin is showing)

Figure 3. Rod/pin finite element model without an axial oil hole



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(a) Radial stress (b) Hoop stress

(c) Von Mises stress (Seqv) (d) 1 st principal stress (S1)

Figure 4. Stress distributions for the model without an axial oil hole (One eighth of the



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wrist pin bearing surface contacting the wrist pin is showing)

Table 1. Input and output random variable description

Variable Name Input / Output Description

DBORE Random input variable Wrist pin bearing ID

DPIN Random input variable Wrist pin OD

IDPIN Random input variable Wrist pin hollow ID

SFATIGUE Random output parameter Max. fatigue stress at the

failure location

Figure 5. The cumulative distribution function of the wrist pin ID

The PDS results were used to fit the response surface that was anapproximation describing a random output parameter as an explicit function of random input variables. Monte Carlo Simulations were conducted based on theresponse surface to obtain probabilistic results for statistical post-processing.



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4. Analysis Results & Discussion

The stress distributions on the wrist pin bearing surface near the oil-hole areillustrated in Figure 2. FEA results successfully duplicate the failure mode byshowing high tensile stresses at the failure location. Since the maximum tensile stress(S1) at the failure location is predominant, the fatigue failure can be closely analyzedas a uniaxial loading condition using the maximum tensile stress as the fatigue stress.The stress distributions for the model without an axial oil-hole are shown in Figure 4.The radial compressive stresses at the wrist pin bearing edges were artificially high

because the actual hydrodynamic bearing pressure became lower close to the bearingedge.

The goodness-of-fit measures from ANSYS PDS output indicated high

quality response surfaces. The sensitivity plot in Figure 6 clearly shows that the wrist pin ID has the greatest influence on the maximum fatigue stress. Therefore, the wrist pin ID is the best parameter to tackle for fatigue stress reduction.

Figure 6. Sensitivity plot for the model with an oil hole

The initial PSD run was performed with a reduced hollow wrist pin ID for the connecting rod and wrist pin model with the oil-hole. The cumulative distributionfunction of the initial PDS run is shown in Figure 7. The rod/pin model without anoil-hole was also analyzed as the baseline. The cumulative distribution function of the

baseline is shown in Figure 8. Comparing Figures 7 and 8, it can be seen that themaximum fatigue stresses of the initial PDS run became even lower than the ones of the baseline due to the reduction of pin ID.



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Figure 7. The results of the initial PDS run. The histogram and cumulative distributionfunction of random output parameter (maximum fatigue stress at the failure location) for

the model with an oil hole and a reduced pin ID.

Additional PDS runs were performed to reach the objective that the rod with

an oil-hole would have approximately the same reliability as the rod without an oil-



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hole by reducing the wrist pin ID. This objective was realized based on thecumulative distribution functions. First, the allowable fatigue stress was read from thecumulative distribution function of the baseline in Figure 8 at 99.9% probability,

corresponding to a probability of failure of 0.1%. After the determination of theallowable fatigue stress, additional PDS runs were performed with different ID valuesof the wrist pin. The wrist pin ID dimensions were finally determined when themaximum fatigue stress read from PDS generated cumulative distribution function at0.1% probability of failure was about the same as the allowable fatigue stress. Thecumulative distribution function for the final design is given in Figure 9.

Figure 8. The results of the baseline PDS run. The histogram and cumulative distributionfunction of random output parameter (maximum fatigue stress at the failure location) for the

model without an oil hole.



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Figure 9. The results of the final design PDS run. The histogram and cumulative distributionfunction of random output parameter (maximum fatigue stress at the failure location) for the

model with an oil hole and a reduced pin ID.

Bench strain gage tests were conducted to confirm the FEA analysis. Straingages were laid inside the oil hole and close to the edge where the failure initiated.The strain gage tests confirmed the major FEA result: a stiffer pin led to a lower fatigue stress at the failure location.

5. Conclusion

A probabilistic analysis using ANSYS PDS has been successfully employedto resolve the fatigue failure of the connecting rod oil-hole. FEA modeling



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successfully duplicated the failure mode by predicting a high fatigue stress at theobserved failure location.

The sensitivity analysis revealed that the hollow wrist pin ID had the

greatest influence on the maximum fatigue stress. Reducing the wrist pin ID was themost effective and economic method to remove the failure mode.

According to the PDS generated cumulative distributions of the maximumfatigue stresses at the failure location, with an 11% reduction of the wrist pin ID, theconnecting rod with an oil hole can achieve approximately the same reliability as theconnecting rod without an oil-hole.

6. References

[1] ANSYS 7.1 Documentations.



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[2] Kenneth J. Rasche, Probabilistic Study of a Refrigeration Steel Heat Loop TubeJoint, ANSYS 2002 Conference.

[3] Andreas Vlahinos, Subhash Kelkar, Stefan Reh, Robert SeCaur, & Steve Pilz,Reliability Based Optimization within the CAD Environment, ANSYS 2002Conference.

[4] Adrian Rispler & John Raju, Optimization of an Aircraft Control Surface,ANSYS 2002 Conference.

[5] Curtis R. Niemeier, Structural Optimization of a Refrigerator Cabinet, ANSYS2002 Conference.


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