It is well known that there is a large variance of fatigue life associated with the data of fatigue tests under nominally identical conditions. Understanding and controlling this variance are essential to enhance the safety and competitiveness of designing and manufacturing fatigue critical products. However, no analytical model quantitatively linking input variables with the variance of fatigue life has been found in current literature. To address this issue, a methodology for analytically predicting the variance of fatigue life is proposed. Using this methodology, the variance of fatigue life can be decomposed into individual components. The significance of this decomposition is two-fold. First, it provides a tool for pinpointing key driving factors of the variance of fatigue life, which is essential for the variance reduction of fatigue life. Second, the time consuming and costly fatigue tests to obtain critical variance information for reliability design may be divided into less time consuming tests for obtaining variance information for individual variables contributing to fatigue variance. Based on the variance prediction tool, a methodology for systematically incorporating manufacturing influence into the prediction of variance and average value of fatigue life is proposed. A verification model is built to predict the variance and average value of fatigue life of a structure with a central hole in the high cycle fatigue regime. The predicted fatigue life matches the actual average fatigue life well. Statistical analysis shows that the predicted variances of the fatigue life are equal to those estimated from actual fatigue life.
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August 2002
Technical Papers
A Methodology for Predicting the Variance of Fatigue Life Incorporating the Effects of Manufacturing Processes
Xiaoping Yang, Assoc. Mem. ASME,
Xiaoping Yang, Assoc. Mem. ASME
Cummins Inc., Mail Code: 91617, 635 South Mapleton, Columbus, IN 47201
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C. Richard Liu, Professor, Fellow, ASME
C. Richard Liu, Professor, Fellow, ASME
School of Industrial Engineering, 1287 Grissom Hall, Purdue University, West Lafayette, IN 47907-1287
Search for other works by this author on:
Xiaoping Yang, Assoc. Mem. ASME
Cummins Inc., Mail Code: 91617, 635 South Mapleton, Columbus, IN 47201
C. Richard Liu, Professor, Fellow, ASME
School of Industrial Engineering, 1287 Grissom Hall, Purdue University, West Lafayette, IN 47907-1287
Contributed by the Manufacturing Engineering Division for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received March 6, 2001; Revised November 2001. Associate Editor: T. Kurtess.
J. Manuf. Sci. Eng. Aug 2002, 124(3): 745-753 (9 pages)
Published Online: July 11, 2002
Article history
Received:
March 6, 2001
Revised:
November 1, 2001
Online:
July 11, 2002
Citation
Yang, X., and Liu, C. R. (July 11, 2002). "A Methodology for Predicting the Variance of Fatigue Life Incorporating the Effects of Manufacturing Processes ." ASME. J. Manuf. Sci. Eng. August 2002; 124(3): 745–753. https://doi.org/10.1115/1.1475318
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