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Process machining allowance for reliability analysis of mechanical parts based on hidden quality loss
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School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China
Submission date: 2023-06-20
Final revision date: 2023-07-31
Acceptance date: 2023-08-27
Online publication date: 2023-08-28
Corresponding author
Xintian Liu   

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(4):171594
  • Machining allowance is concerned to predict part’s reliability.
  • The machining allowance tolerance is used as the process capability criterion.
  • Part failure quantity prediction model based on hidden quality loss function is improved.
  • The process allowance-reliability prediction model is deduced based on hidden quality loss function.
  • The model is applicable to the reliability prediction of machining allowance under normal distribution.
The machining allowance variation is significant for the reliability of a part during the machining process. Usually, when the machining allowance of a part increases, the machining and production cost also increase. When the machining allowance decreases, the machining surface will have defects. The parts will produce many scraps and reliability will decrease. The machining allowance of a part consists of multiple process machining allowances. To analyze the impact caused by machining allowance variation, the hidden quality loss and process machining allowance are combined through the process capability index (PCI). Then the asymmetric quadratic quality loss function (AQF) and quadratic exponential function (QEF) are used to analyze them. A prediction model of hidden quality loss of process machining allowance is proposed. On the premise that the quality characteristic value obeys normal function distribution, a numerical model is given and used to obtain process machining allowance-inherent reliability of the product. The actual case is used to compare and verify the two models.
This work is supported by Ministry of Science and Technology Science and Technology Innovation 2030 New Generation Artificial Intelligence Major Project (2020AAA0109300). This work is also supported by the 2023 Young Scholars Cultivation fund in School of Mechanical a nd Automotive Engineering (Fatigue Life Prediction of Piston via Mass
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