Turning tool life reliability analysis based on approximate Bayesian theory

Tianhong Gao, Yuxiong Li, Xianzhen Huang and Honglei Li

Journal of Risk and Reliability, 2022, vol. 236, issue 5, 696-709

Abstract: Turning tool is a critical part of numerical control machining, and its reliability directly affects machining efficiency and stability of the entire system. Turning tool life reliability analysis has major theoretical and practical significance. Laboratory test data or information monitoring are one of commonly employed methods for assessing mechanical performance. This information is conducive for determining mechanical property distribution and assessing the structural reliability. Therefore, experimental monitoring data is incorporated into turning tool life reliability analysis to obtain adequate evaluation results. Considering tool wear process uncertainty, reliability model based on Taylor tool life equation is proposed. Approximate Bayesian theory is introduced to update reliability model parameters. According to obtained parameter posterior samples, tool life reliability is analyzed via Monte Carlo simulation. Effectiveness of the proposed method is validated against experimental samples of turning tool wear. Results of tool life reliability analysis are in accordance with the actual working conditions, which provides a theoretical basis for the selection of numerical control machining process parameters and tool replacement strategies.

Keywords: Reliability analysis; Taylor tool life; Bayesian theory; approximate Bayesian theory; particle filter (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:236:y:2022:i:5:p:696-709

DOI: 10.1177/1748006X211043753

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