Machines Tool Operation Optimization Considering the Effective Criteria for Reliability in Industry 4.0
الموضوعات :
masoumeh lajevardi
1
,
Mehrdad Nikbakht
2
,
Omid Boyer Hassani
3
,
رضا توکلی مقدم
4
1 - POBOX - 81465-384 ESFAHAN - IRAN
2 - Associate Professor of Industrial Engineering, Najafabad Branch, Islamic Azad University
3 - Assistant Professor,
Faculty of Engineering
Najafabad branch
Islamic Azad University
4 - Tehran University
الکلمات المفتاحية: Machine Tool, Operation, Optimization, Reliability, Industry 4.0,
ملخص المقالة :
Industry 4.0 includes an important regeneration of production and management systems within manufacturing, where the majority of the procedures will be entirely or partially automated. However, there are insufficient research studies related to machines tool operation optimization considering the effective criteria for reliability in industry 4.0 to enable plants to measure their own conditions and to make future strategies for their activities in this field. Thus, this article proposes a decision-making model using a combination of DEMATEL, ANP and Shannon Entropy, and VIKOR methods with fuzzy features in cellular production systems, considering the effective criteria for reliability in Industry 4.0. Use of fuzzy features aims to bring the problem closer to the real world in this study. The efficiency of proposed model has been validated in a large automotive parts manufacturing plant as a case study. Based on the results, the most critical machine in the category of automatic lathe machines is Machine3, and the ordinary lathe machines is Machine31. Sensitivity analysis shows that changing the weights of criteria affects the individual prioritization of machines but does not have any impact on their overall prioritization. This prioritization has a high level of alignment in terms of priority and accuracy with the perspectives of experts and decision-making teams. The selected critical machine is a sensitive machine in plant and cannot be replaced throughout its equipment lifetime. Finally, practical recommendations for Machines Tool Operation Optimization have been provided in Industry 4.0.
[1] Yue, G., Tailai, G., Dan, W. (2021). Multi-Layered Coding-Based Study on Optimization Algorithms for Automobile Production Logistics Scheduling. Technological Forecasting and Social Change, 170, 120889.
[2] Lee, J. G., Kim, T., Sung, K. W., Han, S. W. (2021). Automobile Parts Reliability Prediction Based on Claim Data: The Comparison of Predictive Effects with Deep Learning. Engineering Failure Analysis, 129, 105657.
[3] Lee, H. T. (2009). Failure Modeling to Predict Warranty Cost for Individual Markets. Journal of the Korea Academia-Industrial cooperation Society, 10(6), 1346-1352.
[4] Zhan, H., Xiao, N. C., Ji, Y. (2022). An Adaptive Parallel Learning Dependent Kriging Model for Small Failure Probability Problems. Reliability Engineering & System Safety, 108403.
[5] Pan, F., Nagi, R. (2010). Robust Supply Chain Design under Uncertain Demand in Agile Manufacturing. Computers & operations research, 37(4), 668-683.
[6] Costantino, N., Dotoli, M., Falagario, M., Fanti, M. P., Mangini, A. M. (2012). A Model for Supply Management of Agile Manufacturing Supply Chains. International Journal of Production Economics, 135(1), 451-457.
[7] Khan, M., Jaber, M. Y., Ahmad, A. R. (2014). An Integrated Supply Chain Model with Errors in Quality Inspection and Learning in Production. Omega, 42(1), 16-24.
[8] Rajaguru, R., Matanda, M. J. (2013). Effects of Inter-Organizational Compatibility on Supply chain capabilities: Exploring the mediating role of inter-organizational information systems (IOIS) Integration. Industrial marketing management, 42(4), 620-632.
[9] Chehade, A., Savargaonkar, M., Krivtsov, V. (2022). Conditional Gaussian Mixture Model for Warranty Claims Forecasting. Reliability Engineering & System Safety, 218, 108180.
[10] Wang, J., Zhang, C. (2018). Software Reliability Prediction Using a Deep Learning Model Based on the RNN Encoder–Decoder. Reliability Engineering & System Safety, 170, 73-82.
[11] Schumacher, A., Erol, S., Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia Cirp, 52, 161-166.
[12] Butollo, F., Jürgens, U., Krzywdzinski, M. (2019). From Lean Production to Industrie 4.0: More Autonomy for Employees?, Springer International Publishing, 61-80.
[13] Jafari-Asl, J., Seghier, M. E. A. B., Ohadi, S., Correia, J., Barroso, J. (2022). Reliability Analysis Based Improved Directional Simulation Using Harris Hawks Optimization Algorithm for Engineering Systems. Engineering Failure Analysis, 106148.
[14] Manouchehrynia, R., Abdullah, S., Singh, S. S. K. (2022). Fatigue-Based Reliability in Assessing the Failure of an Automobile Coil Spring under Random Vibration Loadings. Engineering Failure Analysis, 131, 105808.
[15] Huang, Y. S., Fang, C. C., Lu, C. M., Tseng, T. L. B. (2022). Optimal Warranty Policy for Consumer Electronics with Dependent Competing Failure Processes. Reliability Engineering & System Safety, 108418.
[16] Mi, J., Lu, N., Li, Y. F., Huang, H. Z., Bai, L. (2022). An Evidential Network-Based Hierarchical Method for System Reliability Analysis with Common Cause Failures and Mixed Uncertainties. Reliability Engineering & System Safety, 220, 108295.
[17] Xiao, N. C., Yuan, K., Zhan, H. (2022). System Reliability Analysis Based on Dependent Kriging Predictions and Parallel Learning Strategy. Reliability Engineering & System Safety, 218, 108083.
[18] Wang, J., Xu, G., Yuan, P., Li, Y., Kareem, A. (2022). An Efficient and Versatile Kriging-Based Active Learning Method for Structural Reliability Analysis. Available at SSRN 4356485.
[19] Soares, E., da Silva Lopes, I., Pinheiro, J. (2021). Methodology to Support Maintenance Management for the Identification and Analysis of the Degradation of Equipment Reliability. IFAC-PapersOnLine, 54(1), 1272-1277.
[20] Abolghasemian, M., Chobar, A. P., AliBakhshi, M., Fakhr, A., Moradi, S. (2021). Delay Scheduling Based on Discrete-Event Simulation for Construction Projects. Iranian Journal of Operations Research, 12(1), 49-63.
[21] Ghazi, A., Pourghader Chobar, A. (2019). Evaluation of Automobile Tire Reliability Using Fuzzy Logic in Matlab Software. Sixth International Conference on Management Criticism and Analysis in the third millennium, Mashhad.
[22] Hey, M., Xie, J., Wu, X., Hu, Q., Dai, Y. (2016). Capability Coordination in Automobile Logistics Service Supply Chain Based on Reliability. Procedia Engineering, 137, 325-333.
[23] Teymouri, A., Mazra'eh Farahani , M. (2009). A Model for Spare parts' Demand Forecasting Based on Reliability, Operational Environment and Failure Interaction of Parts. International Journal of Industrial Engineering and Production Management (Persian) (International Journal of Engineering Sciences), 20 (1), 55-64.
[24] Tortorella, G. L., Fettermann, D. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975-2987.
[25] Skrzeszewska, M., Patalas-Maliszewska, J. (2019, June). Assessing the Effectiveness of Using the MES in Manufacturing Enterprises in the Context of Industry 4.0. International Symposium on Distributed Computing and Artificial Intelligence, Springer, Cham, 49-56.
[26] Sadeghi-Niaraki, A. (2020). Industry 4.0 Development Multi-Criteria Assessment: An Integrated Fuzzy DEMATEL, ANP and VIKOR Methodology. IEEE Access, 8, 23689-23704. Article 8957153.
