A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration

References

• Agogino, A., and K. Goebel. 2007. “BEST lab, UC Berkeley.” In Milling Data set. Nasa Ames Prognostics Data Repository. Moffett Field, CA: NASA Ames Research Center.
   Google Scholar
• Abdul-Malak, D. T., J. P. Kharoufeh, and L. M. Maillart. 2019. “Maintaining Systems with Heterogeneous Spare Parts.” Naval Research Logistics (NRL) 66 (6): 485–501. https://doi.org/10.1002/nav.21864
   Web of Science ®Google Scholar
• Alaswad, S., and Y. Xiang. 2017. “A Review on Condition-Based Maintenance Optimization Models for Stochastically Deteriorating System.” Reliability Engineering & System Safety 157: 54–63. https://doi.org/10.1016/j.ress.2016.08.009
   Web of Science ®Google Scholar
• Chen, Y., and J. Jin. 2006. “Quality-oriented-maintenance for Multiple Interactive Tooling Components in Discrete Manufacturing Processes.” IEEE Transactions on Reliability 55 (1): 123–134. https://doi.org/10.1109/TR.2005.864152
   Web of Science ®Google Scholar
• Chen, N., Z. S. Ye, Y. Xiang, and L. Zhang. 2015. “Condition-based Maintenance Using the Inverse Gaussian Degradation Model.” European Journal of Operational Research 243 (1): 190–199. https://doi.org/10.1016/j.ejor.2014.11.029
   Web of Science ®Google Scholar
• de Jonge, B. 2019. “Discretizing Continuous-Time Continuous-State Deterioration Processes, with an Application to Condition-Based Maintenance Optimization.” Reliability Engineering & System Safety 188: 1–5. https://doi.org/10.1016/j.ress.2019.03.006
   Web of Science ®Google Scholar
• Deng, Y. H., H. P. Zhu, G. J. Zhang, and H. Yin. 2012. “Optimal Tool Replacement Decision Method Based on Cost and Process Capability.” Mechanical Engineering and Technology, AISC 125: 9-14. Springer Berlin Heidelberg.
   Google Scholar
• Dou, J., S. Jiao, C. Xu, F. Luo, L. Tang, and X. Xu. 2020. “Unsupervised Online Prediction of Tool Wear Values Using Force Model Coefficients in Milling.” The International Journal of Advanced Manufacturing Technology 109 (3-4): 1153–1166. https://doi.org/10.1007/s00170-020-05684-1
   Web of Science ®Google Scholar
• Gan, J., W. Zhang, S. Wang, and X. Zhang. 2022. “Joint Decision of Condition-Based Opportunistic Maintenance and Scheduling for Multi-Component Production Systems.” International Journal of Production Research 60 (17): 5155–5175. https://doi.org/10.1080/00207543.2021.1951447
   Web of Science ®Google Scholar
• Gouarir, A., G. Martínez-Arellano, G. Terrazas, P. Benardos, and S. J. P. C. Ratchev. 2018. “In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis.” Procedia CIRP 77: 501–504. https://doi.org/10.1016/j.procir.2018.08.253
   Google Scholar
• Guo, L., Y. Yu, H. Gao, T. Feng, and Y. Liu. 2022. “Online Remaining Useful Life Prediction of Milling Cutters Based on Multisource Data and Feature Learning.” IEEE Transactions on Industrial Informatics 18 (8): 5199–5208. https://doi.org/10.1109/TII.2021.3118994
   Web of Science ®Google Scholar
• He, Z., T. Shi, J. Xuan, and T. Li. 2021. “Research on Tool Wear Prediction Based on Temperature Signals and Deep Learning.” Wear 478-479: 203902. https://doi.org/10.1016/j.wear.2021.203902
   Web of Science ®Google Scholar
• Huang, Y., Z. Lu, W. Dai, W. Zhang, and B. Wang. 2021. “Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model.” Applied Sciences 11 (11): 5011. https://doi.org/10.3390/app11115011
   Google Scholar
• Keizer, M. C. O., S. D. P. Flapper, and R. H. Teunter. 2017. “Condition-based Maintenance Policies for Systems with Multiple Dependent Components: A Review.” European Journal of Operational Research 261 (2): 405–420. https://doi.org/10.1016/j.ejor.2017.02.044
   Web of Science ®Google Scholar
• Kim, G., S. M. Yang, S. Kim, D. Y. Kim, J. G. Choi, H. W. Park, and S. Lim. 2023. “A Multi-Domain Mixture Density Network for Tool Wear Prediction Under Multiple Machining Conditions.” International Journal of Production Research, 1–20. published online, https://doi.org/10.1080/00207543.2023.2289076.
   Web of Science ®Google Scholar
• Lei, Y., N. Li, L. Guo, N. Li, T. Yan, and J. Lin. 2018. “Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction.” Mechanical Systems and Signal Processing 104: 799–834. https://doi.org/10.1016/j.ymssp.2017.11.016
   Web of Science ®Google Scholar
• Lu, B., Z. Chen, and X. Zhao. 2021. “Data-driven Dynamic Predictive Maintenance for a Manufacturing System with Quality Deterioration and Online Sensors.” Reliability Engineering & System Safety 212: 107628. https://doi.org/10.1016/j.ress.2021.107628
   Web of Science ®Google Scholar
• Lu, B., X. Zhou, and Y. Li. 2016. “Joint Modeling of Preventive Maintenance and Quality Improvement for Deteriorating Single-Machine Manufacturing Systems.” Computers & Industrial Engineering 91: 188–196. https://doi.org/10.1016/j.cie.2015.11.019
   Web of Science ®Google Scholar
• Mohamed-Larbi, R., and A. K. Daoud. 2023. “Condition-based Maintenance Optimisation for Multi-Component Systems Using Mean Residual Life.” International Journal of Production Research, 1–25. published online. https://doi.org/10.1080/00207543.2023.2280882.
   Web of Science ®Google Scholar
• Nakagawa, T. 2006. Maintenance Theory of Reliability. London: Springer Science and Business Media.
   Google Scholar
• Omshi, E. M., A. Grall, and S. Shemehsavar. 2020. “A Dynamic Auto-Adaptive Predictive Maintenance Policy for Degradation with Unknown Parameters.” European Journal of Operational Research 282 (1): 81–92. https://doi.org/10.1016/j.ejor.2019.08.050
   Web of Science ®Google Scholar
• Pan, D., J. B. Liu, F. Huang, J. Cao, and A. Alsaedi. 2017. “A Wiener Process Model with Truncated Normal Distribution for Reliability Analysis.” Applied Mathematical Modelling 50: 333–346. https://doi.org/10.1016/j.apm.2017.05.049
   Web of Science ®Google Scholar
• Park, M., and H. Pham. 2023. “Condition-based Maintenance for a Degradation-Shock Dependence System Under Warranty.” International Journal of Production Research 61 (15): 5212–5227. https://doi.org/10.1080/00207543.2022.2099319
   Web of Science ®Google Scholar
• Pearn, W. L., Y. C. Hsu, and J. J. Horng Shiau. 2007. “Tool Replacement Policy for one-Sided Processes with low Fraction Defective.” Journal of the Operational Research Society 58 (8): 1075–1083. https://doi.org/10.1057/palgrave.jors.2602224
   Web of Science ®Google Scholar
• Rebaiaia, M. L., and D. Ait-Kadi. 2023. “A new Integrated Strategy for Optimising the Maintenance Cost of Complex Systems Using Reliability Importance Measures.” International Journal of Production Research 1–22, published online. https://doi.org/10.1080/00207543.2023.2254406.
   Web of Science ®Google Scholar
• Ren, Y., Y. Ding, and S. Zhou. 2006. “A Data Mining Approach to Study the Significance of Nonlinearity in Multistation Assembly Processes.” IIE Transactions 38 (12): 1069–1083. https://doi.org/10.1080/07408170600735538
   Web of Science ®Google Scholar
• Rizal, M., J. A. Ghani, M. Z. Nuawi, and C. H. C. Haron. 2013. “Online Tool Wear Prediction System in the Turning Process Using an Adaptive Neuro-Fuzzy Inference System.” Applied Soft Computing 13 (4): 1960–1968. https://doi.org/10.1016/j.asoc.2012.11.043
   Web of Science ®Google Scholar
• Shaban, Y., M. Aramesh, S. Yacout, M. Balazinski, H. Attia, and H. Kishawy. 2017. “Optimal Replacement Times for Machining Tool During Turning Titanium Metal Matrix Composites Under Variable Machining Conditions.” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231 (6): 924–932. https://doi.org/10.1177/0954405415577591
   Web of Science ®Google Scholar
• Shi, J., and S. Zhou. 2009. “Quality Control and Improvement for Multistage Systems: A Survey.” IIE Transactions 41 (9): 744–753. https://doi.org/10.1080/07408170902966344
   Web of Science ®Google Scholar
• Sun, H., J. Zhang, R. Mo, and X. Zhang. 2020. “In-process Tool Condition Forecasting Based on a Deep Learning Method.” Robotics and Computer-Integrated Manufacturing 64: 101924. https://doi.org/10.1016/j.rcim.2019.101924
   Web of Science ®Google Scholar
• Taguchi, G. 1986. Introduction to Quality Engineering: Designing Quality Into Products and Processes. Tokyo: The Organization Tokyo.
   Google Scholar
• Traini, E., G. Bruno, and F. Lombardi. 2021. “Tool Condition Monitoring Framework for Predictive Maintenance: A Case Study on Milling Process.” International Journal of Production Research 59 (23): 7179–7193. https://doi.org/10.1080/00207543.2020.1836419
   Web of Science ®Google Scholar
• Vagnorius, Z., M. Rausand, and K. Sørby. 2010. “Determining Optimal Replacement Time for Metal Cutting Tools.” European Journal of Operational Research 206 (2): 407–416. https://doi.org/10.1016/j.ejor.2010.03.023
   Web of Science ®Google Scholar
• Van Oosterom, C., H. Peng, and G. J. van Houtum. 2017. “Maintenance Optimization for a Markovian Deteriorating System with Population Heterogeneity.” IISE Transactions 49 (1): 96–109. https://doi.org/10.1080/0740817X.2016.1205239
   Web of Science ®Google Scholar
• Xia, T., G. Shi, G. Si, S. Du, and L. Xi. 2021. “Energy-oriented Joint Optimization of Machine Maintenance and Tool Replacement in Sustainable Manufacturing.” Journal of Manufacturing Systems 59: 261–271. https://doi.org/10.1016/j.jmsy.2021.01.015
   Web of Science ®Google Scholar
• Xu, W., and L. Cao. 2015. “Optimal Tool Replacement with Product Quality Deterioration and Random Tool Failure.” International Journal of Production Research 53 (6): 1736–1745. https://doi.org/10.1080/00207543.2014.957878
   Web of Science ®Google Scholar
• Xu, X., J. Wang, W. Ming, M. Chen, and Q. An. 2021. “In-process tap Tool Wear Monitoring and Prediction Using a Novel Model Based on Deep Learning.” The International Journal of Advanced Manufacturing Technology 112 (1-2): 453–466. https://doi.org/10.1007/s00170-020-06354-y
   Web of Science ®Google Scholar
• Yuan, J., L. Liu, Z. Yang, J. Bo, and Y. Zhang. 2021. “Tool Wear Condition Monitoring by Combining Spindle Motor Current Signal Analysis and Machined Surface Image Processing.” The International Journal of Advanced Manufacturing Technology 116 (7-8): 2697–2709. https://doi.org/10.1007/s00170-021-07366-y
   Web of Science ®Google Scholar
• Zaretalab, A., H. S. Haghighi, S. Mansour, and M. S. Sajadieh. 2018. “A Mathematical Model for the Joint Optimization of Machining Conditions and Tool Replacement Policy with Stochastic Tool Life in the Milling Process.” The International Journal of Advanced Manufacturing Technology 96 (5-8): 2319–2339. https://doi.org/10.1007/s00170-018-1683-9
   Web of Science ®Google Scholar
• Zaretalab, A., H. S. Haghighi, S. Mansour, and M. S. Sajadieh. 2019. “Optimisation of Tool Replacement Time in the Machining Process Based on Tool Condition Monitoring Using the Stochastic Approach.” International Journal of Computer Integrated Manufacturing 32 (2): 159–173. https://doi.org/10.1080/0951192X.2018.1550677
   Web of Science ®Google Scholar
• Zhang, L., Y. Lei, and H. Shen. 2016. “How Heterogeneity Influences Condition-Based Maintenance for Gamma Degradation Process.” International Journal of Production Research 54 (19): 5829–5841. https://doi.org/10.1080/00207543.2016.1181282
   Web of Science ®Google Scholar