PSI - Issue 59

Viktor Kovalov et al. / Procedia Structural Integrity 59 (2024) 779–785 V. Kovalov et al. / Structural Integrity Procedia 00 (2019) 000 – 000

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Conclusions The article establishes the need to determine the reliability of a prefabricated heavy-loaded tool as a system not only in terms of its reliability, but also from the point of view of taking into account complex reliability indicators, for which a mathematical model of the turning cutter readiness function was developed using the theory of Markov processes, the use of which allows controlling the stability of the tool, which is especially important when machining parts on heavy machines. References Baksa, T., Kroupa, T., Hanzl, P., Zetek, M., 2015. Durability of cutting tools during machining of very hard and solid materials. Procedia Engineering 100, 1414 - 1423. Cui, Xiaobin, Jiao, Feng, Ming, Pingmei, Guo, Jingxia, 2017. Reliability analysis of ceramic cutting tools in continuous and interrupted hard turning. Ceramics International 43(13), 10109 - 10122. Dai, Wei, Sun, Jiahuan, Yongjiao, Chi, Lu, Zhiyuan, Xu, Dong, Jiang, Nan, 2019. Review of machining equipment reliability analysis methods based on condition monitoring technology. Applied Sciences 9, 2786. Huynh, K.T., 2021. An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process. Reliability Engineering and System Safety 213, 107695. Karandikar, J. M., Abbas, Ali E., Schmitz, T. L., 2014. Tool life prediction using bayesian updating. Part 2: Turning tool life using a Markov chain Monte Carlo approach. Precision Engineering 38(1), 18 - 27. Karimi, B., Niaki, S.T.A., Haleh, H., Naderi, B., 2019. Reliability optimization of tools with increasing failure rates in a flexible manufacturing system. Arabian Journal for Science and Engineering 44, 2579–2596. Klymenko, G. Kvashnin, V., 2019, Reliability assurance technological systems exploitation of heavy lathe. С utting and tool in technological system 91, 78 - 86. Letot, C., Serra, R., Dossevi, M., Pierre, Dehombreux, 2016. Cutting tools reliability and residual life prediction from degradation indicators in turning process. International Journal Advanced Manufacturing Technological 86, 495–506. Liu, Erliang, An, Wenzhao, Xu, Zhichao, Zhang, Huiping, 2020. Experimental study of cutting - parameter and tool life reliability optimization in inconel 625 machining based on wear map approach. Journal of Manufacturing Processes 53, 34 - 42. Mohamad, Gaddafee, Satish, Chinchanikar, 2020. An experimental investigation of cutting tool reliability and its prediction using Weibull and gamma models: A Comparative Assessment. Materials Today: Proceedings 24(2), 1478 - 1487. Qin, Guo, 2023. Reviews on the machining and measurement of large components. Advances in Mechanical Engineering 15(9) 1–11. Wardany, Tahany, Elbestawi, Mohamed, 1997. Prediction of tool failure rate in turning hardened steels. The International Journal of Advanced Manufacturing Technology 13, 1 - 16. Yamany, M., Abraham, D., Labi, S., 2021. Comparative analysis of Markovian methodologies for modeling infrastructure system performance. Journal of Infrastructure Systems 27, 1 - 13. Yanbin, Du, Yashi, Zheng, Guoao, Wu, Ying, Tang, 2020. Decision - making method of heavy - duty machine tool remanufacturing based on AHP entropy weight and extension theory. Journal of Cleaner Production 252, 119607. Yang, Tian, Zhifeng, Liu, Xinpeng, Xu, Guang, Wang, Qiwei, Li, Yang, Zhou, Jiangli, Cheng, 2019. Systematic review of research relating to heavy - duty machine tool foundation systems. Advances in Mechanical Engineering 11(1) 1–16. Zaretalab, Arash, Sharifi, Mani, Taghipour, Sharareh, 2020. Machining condition - based stochastic modeling of cutting tool’s life. The International Journal of Advanced Manufacturing Technology 111, 1 - 15.