A proposal for improving production efficiency of existing machining line through a hybrid monitoring and optimisation process

References

• Agnihotri, A., and N. Batra. 2020. “Exploring Bayesian Optimization: Breaking Bayesian Optimization Into Small, Sizeable Chunks.” Distill, doi:10.23915/distill.00026.
   Google Scholar
• Amouzgar, K., A. Nourmohammadi, and A. H. C. Ng. 2021. “Multi-objective Optimisation of Tool Indexing Problem: A Mathematical Model and a Modified Genetic Algorithm.” International Journal of Production Research 59 (12): 3572–3590. doi:10.1080/00207543.2021.1897174 .
   Web of Science ®Google Scholar
• Bittencourt, V. L., A. C. Alves, and C. P. Leão. 2020. “Industry 4.0 Triggered by Lean Thinking: Insights from a Systematic Literature Review.” International Journal of Production Research 59 (5): 1496–1510. doi:10.1080/00207543.2020.1832274.
   Web of Science ®Google Scholar
• Buer, S.-V., J. O. Strandhagen, and F. T. S. Chan. 2018. “The Link Between Industry 4.0 and Lean Manufacturing: Mapping Current Research and Establishing a Research Agenda.” International Journal of Production Research 56 (8): 2924–2940. doi:10.1080/00207543.2018.1442945.
   Web of Science ®Google Scholar
• Cheng, M., L. Jiao, P. Yan, H. Jiang, R. Wang, T. Qiu, and X. Wang. 2022. “Intelligent Tool Wear Monitoring and Multi-Step Prediction Based on Deep Learning Model.” Journal of Manufacturing Systems 62: 286–300.
   Web of Science ®Google Scholar
• Chuaqui, T. R. C., A. T. Rhead, R. Butler, and C. Scarth. 2021. “A Data-Driven Bayesian Optimisation Framework for the Design and Stacking Sequence Selection of Increased Notched Strength Laminates.” Composites Part B: Engineering 226: 109347. DOI:10.1016/j.compositesb.2021.109347.
   Web of Science ®Google Scholar
• Ciano, M. P., P. Dallasega, G. Orzes, and T. Rossi. 2020. “One-to-one Relationships Between Industry 4.0technologies and Lean Production Techniques: Amultiple Case Study.” International Journal of Production Research 59 (5): 1386–1410. doi:10.1080/00207543.2020.1821119.
   Web of Science ®Google Scholar
• Forrester, A. I. J., A. Sóbester, and A. J. Keane. 2007. “Multi-fidelity Optimization via Surrogate Modelling.” Proceedings of the Royal Society A 463: 3251–3269.
   Google Scholar
• Forrester, A. I. J., A. Sóbester, and A. J. Keane. 2008. Engineering Design via Surrogate Modelling: A Practical Guide. Chichester: John Wiley & Sons.
   Google Scholar
• Gao, R., L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu. 2015. “Cloud-enabled Prognosis for Manufacturing.” CIRP Annals 64 (2): 749–772.
   Web of Science ®Google Scholar
• Herbol, H. C., W. Hu, P. Frazier, P. Clancy, and M. Poloczek. 2018. “Efficient Search of Compositional Space for Hybrid Organic–Inorganic Perovskites via Bayesian Optimization.” npj Computational Materials 4: 51. doi:10.1038/s41524-018-0106-7.
   Web of Science ®Google Scholar
• Herwan, J., S. Kano, R. Oleg, H. Sawada, N. Kasashima, and T. Misaka. 2019. “Retrofitting old CNC Turning with an Accelerometer at a Remote Location Towards Industry 4.0.” Manufacturing Letters 21: 56–59.
   Google Scholar
• Herwan, J., S. Kano, R. Oleg, H. Sawada, and M. Watanabe. 2018. “Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System.” International Journal of Automation Technology 12 (3): 282–289.
   Web of Science ®Google Scholar
• Hesser, D. F., and B. Markert. 2019. “Tool Wear Monitoring of a Retrofitted CNC Milling Machine Using Artificial Neural Networks.” Manufacturing Letters 19: 1–4.
   Google Scholar
• Jemielniak, K. 2019. “Contemporary Challenges in Tool Condition Monitoring.” Journal of Machine Engineering 19 (1): 48–61.
   Google Scholar
• Jones, D. R. 2001. “A Taxonomy of Global Optimization Methods Based on Response Surfaces.” Journal of Global Optimization 21: 345–383.
   Web of Science ®Google Scholar
• Jones, D. R., M. Schonlau, and W. J. Welch. 1998. “Efficient Global Optimization of Expensive Black box Functions.” Journal of Global Optimization 13: 455–492.
   Web of Science ®Google Scholar
• Kamble, S., A. Gunasekaran, and N. C. Dhone. 2020. “Industry 4.0 and Lean Manufacturing Practices for Sustainable Organisational Performance in Indian Manufacturing Companies.” International Journal of Production Research 58 (5): 1319–1337. doi:10.1080/00207543.2019.1630772.
   Web of Science ®Google Scholar
• Komoto, H., G. Herrera, and J. Herwan. 2020. “An Evolvable Model of Machine Tool Behavior Applied to Energy Usage Prediction.” CIRP Annals 69 (1): 129–132.
   Web of Science ®Google Scholar
• Lauro, C. H., L. C. Brandão, D. Baldo, R. A. Reis, and J. P. Davim. 2014. “Monitoring and Processing Signal Applied in Machining Processes – A Review.” Measurement 58: 73–86.
   Web of Science ®Google Scholar
• Liu, C., Y. Li, C. Huang, Y. Zhao, and Z. Zhao. 2022. “A Meta-Reinforcement Learning Method by Incorporating Simulation and Real Data for Machining Deformation Control of Finishing Process.” International Journal of Production Research, doi:10.1080/00207543.2022.2027041.
   Web of Science ®Google Scholar
• Liu, C., P. Zheng, and X. Xu. 2021. “Digitalisation and Servitisation of Machine Tools in the era of Industry 4.0: A Review.” International Journal of Production Research, doi:10.1080/00207543.2021.1969462.
   PubMed Web of Science ®Google Scholar
• Lv, L., Z. Deng, C. Yan, T. Liu, L. Wan, and Q. Gu. 2020. “Modelling and Analysis for Processing Energy Consumption of Mechanism and Data Integrated Machine Tool.” International Journal of Production Research 58 (23): 7078–7093. doi:10.1080/00207543.2020.1756508.
   Web of Science ®Google Scholar
• Maier, M., R. Zwicker, M. Akbari, A. Rupenyan, and K. Wegener. 2019. “Bayesian Optimization for Autonomous Process set-up in Turning.” CIRP Journal of Manufacturing Science and Technology 26: 81–87.
   Web of Science ®Google Scholar
• Majdouline, I., S. Dellagi, L. Mifdal, E. M. Kibbou, and A. Moufki. 2021. “Integrated Production-Maintenance Strategy Considering Quality Constraints in dry Machining.” International Journal of Production Research 60 (9): 2850–2864. doi:10.1080/00207543.2021.1905193.
   Web of Science ®Google Scholar
• Misaka, T., J. Herwan, S. Kano, R. Oleg, H. Sawada, N. Kasashima, and Y. Furukawa. 2020. “Prediction of Surface Roughness in CNC Turning by Model-Assisted Response Surface Method.” Precision Engineering 62: 196–203.
   Web of Science ®Google Scholar
• Mukherjee, I., and P. K. Ray. 2006. “A Review of Optimization Techniques in Metal Cutting Processes.” Computers & Industrial Engineering 50 (1-2): 15–34.
   Web of Science ®Google Scholar
• Olufayo, O., V. Songmene, J. P. Kenné, and M. Ayomoh. 2020. “Modelling for Cost and Productivity Optimization in Sustainable Manufacturing: A Case of dry Versus wet Machining of Mould Steels.” International Journal of Production Research 59 (17): 5352–5371. doi:10.1080/00207543.2020.1778207.
   Web of Science ®Google Scholar
• Rosin, F., P. Forget, S. Lamouri, and R. Pellerin. 2020. “Impacts of Industry 4.0 Technologies on Lean Principles.” International Journal of Production Research 58 (6): 1644–1661. doi:10.1080/00207543.2019.1672902.
   Web of Science ®Google Scholar
• Sano, S., T. Kadowaki, K. Tsuda, and S. Kimura. 2020. “Application of Bayesian Optimization for Pharmaceutical Product Development.” Journal of Pharmaceutical Innovation 15: 333–343.
   Web of Science ®Google Scholar
• Sharma, A., Z. Zhang, and R. Rai. 2021. “The Interpretive Model of Manufacturing: A Theoretical Framework and Research Agenda for Machine Learning in Manufacturing.” International Journal of Production Research 59 (16): 4960–4994. doi:10.1080/00207543.2021.1930234.
   Web of Science ®Google Scholar
• Sibalija, T. V. 2019. “Particle Swarm Optimisation in Designing Parameters of Manufacturing Processes: A Review (2008–2018).” Applied Soft Computing Journal 84: 105743.
   Web of Science ®Google Scholar
• Siddhpura, A., and R. Paurobally. 2013. “A Review of Flank Wear Prediction Methods for Tool Condition Monitoring in a Turning Process.” The International Journal of Advanced Manufacturing Technology 65: 371–393.
   Web of Science ®Google Scholar
• Teti, R., K. Jemielniak, G. O’Donnell, and D. Dornfeld. 2010. “Advanced Monitoring of Machining Operations.” CIRP Annals – Manufacturing Technology 59: 717–739.
   Web of Science ®Google Scholar
• Tortorella, G. L., and D. Fettermann. 2017. “Implementation of Industry 4.0 and Lean Production in Brazilian Manufacturing Companies.” International Journal of Production Research 56 (8): 2975–2987. doi:10.1080/00207543.2017.1391420.
   Web of Science ®Google Scholar
• Traini, E., G. Bruno, and F. Lombardi. 2020. “Tool Condition Monitoring Framework for Predictive Maintenance: A Case Study on Milling Process.” International Journal of Production Research 59 (23): 7179–7193. doi:10.1080/00207543.2020.1836419.
   Web of Science ®Google Scholar
• Wang, Y., L. Zheng, and Y. Wang. 2021. “Event-driven Tool Condition Monitoring Methodology Considering Tool Life Prediction Based on Industrial Internet.” Journal of Manufacturing Systems 58 (A): 205–222.
   Web of Science ®Google Scholar
• Yamaguchi, K., S. E. Phenisee, Z. Chen, M. Salviato, and J. Yang. 2020. “Ply-drop Design of non-Conventional Laminated Composites Using Bayesian Optimization.” Composites Part A: Applied Science and Manufacturing 139: 106136. doi:10.1016/j.compositesa.2020.106136.
   Web of Science ®Google Scholar
• Yusup, N., A. M. Zain, and S. Z. M. Hashim. 2012. “Evolutionary Techniques in Optimizing Machining Parameters: Review and Recent Applications (2007–2011).” Expert Systems with Applications 39: 9909–9927.
   Web of Science ®Google Scholar
• Zhang, C., W. Wang, and H. Li. 2022. “Tool Wear Prediction Method Based on Symmetrized dot Pattern and Multi-Covariance Gaussian Process Regression.” Measurement 189: 110466.
   Web of Science ®Google Scholar
• Zhang, Y. M., H. Wang, J. X. Mao, and Z. D. Xu. 2021. “Probabilistic Framework with Bayesian Optimization for Predicting Typhoon-Induced Dynamic Responses of a Long-Span Bridge.” Journal of Structural Engineering 147 (1): 04020297.
   Web of Science ®Google Scholar
• Zheng, T., M. Ardolino, A. Bacchetti, and M. Perona. 2020. “The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review.” International Journal of Production Research 59 (6): 1922–1954. doi:10.1080/00207543.2020.1824085.
   Web of Science ®Google Scholar
• Zhou, Y., and W. Xue. 2018. “Review of Tool Condition Monitoring Methods in Milling Processes.” The International Journal of Advanced Manufacturing Technology 96: 2509–2523.
   Web of Science ®Google Scholar
• Zhu, K., G. Li, and Y. Zhang. 2020. “Big Data Oriented Smart Tool Condition Monitoring System.” IEEE Transactions on Industrial Informatics 16 (6): 4007–4016.
   Web of Science ®Google Scholar