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Journal of Intelligent Manufacturing

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01-09-2020

Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence

Authors: Soheyl Khalilpourazari, Saman Khalilpourazary, Aybike Özyüksel Çiftçioğlu, Gerhard-Wilhelm Weber

Published in: Journal of Intelligent Manufacturing | Issue 6/2021

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Abstract

This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models and validated the results in real-world case studies. In addition, several artificial intelligence-based solution techniques are designed to solve the complex nonlinear problem. We determined the most efficient solution approach by solving various test problems. Then, simulated several scenarios to demonstrate the robustness of our results. The results showed that the solutions provided by the offered model significantly reduce energy consumption in different setups. To ensure the reliability of the results, we carried out worst-case sensitivity analyses and found the most critical parameters. The results of the worst-case analyses indicated that the offered robust model is efficient and saves a significant amount of energy comparing to traditional models. It is shown that the provided solution by the presented robust formulation is reliable in all situations and results in the lowest energy and the best machining precision.

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Ahilan, C., Kumanan, S., Sivakumaran, N., & Dhas, J. E. R. (2013). Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Applied Soft Computing, 13(3), 1543–1551.CrossRef

Arif, M., Stroud, I. A., & Akten, O. (2014). A model to determine the optimal parameters for sustainable-energy machining in a multi-pass turning operation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(6), 866–877. https://doi.org/10.1177/0954405413508945.CrossRef

Chauhan, P., Pant, M., & Deep, K. (2015). Parameter optimization of multi-pass turning using chaotic PSO. International Journal of Machine Learning and Cybernetics, 6(2), 319–337. https://doi.org/10.1007/s13042-013-0221-1.CrossRef

Chen, M. C. (2004). Optimizing machining economics models of turning operations using the scatter search approach. International Journal of Production Research, 42(13), 2611–2625. https://doi.org/10.1080/00207540410001666251.CrossRef

Chen, M. C., & Tsai, D. M. (1996). A simulated annealing approach for optimization of multi-pass turning operations. International Journal of Production Research, 34(10), 2803–2825. https://doi.org/10.1080/00207549608905060.CrossRef

De, A., Mogale, D. G., Zhang, M., Pratap, S., Kumar, S. K., & Huang, G. Q. (2020). Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies. International Journal of Production Economics, 225, 107566.CrossRef

Diyaley, S., & Chakraborty, S. (2019). Metaheuristics-based parametric optimization of multi-pass turning process: A comparative analysis. OPSEARCH, 57, 1–24.

Elishakoff, I., & Elettro, F. (2014). Interval, ellipsoidal, and super-ellipsoidal calculi for experimental and theoretical treatment of uncertainty: Which one ought to be preferred? International Journal of Solids and Structures, 51(7–8), 1576–1586.CrossRef

Fang, K., Uhan, N., Zhao, F., & Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234–240.CrossRef

Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets and Systems, 341, 69–91.CrossRef

Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2017). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2729-3.CrossRef

Hashemi Doulabi, H., Jaillet, P., Pesant, G., & Rousseau, L. M. (2020b). Exploiting the structure of two-stage robust optimization models with exponential scenarios. INFORMS Journal on Computing. https://doi.org/10.1287/ijoc.2019.0928.CrossRef

Hashemi Doulabi, H., Pesant, G., & Rousseau, L. M. (2020a). Vehicle routing problems with synchronized visits and stochastic travel and service times: Applications in healthcare. Transportation Science, 54(4), 1053–1072.CrossRef

Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3–28.CrossRef

Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8), 1683–1693.CrossRef

Khalilpourazari, S., & Khalilpourazary, S. (2017). A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process. Engineering Optimization, 49(5), 878–895. https://doi.org/10.1080/0305215X.2016.1214437.CrossRef

Khalilpourazari, S., Mirzazadeh, A., Weber, G. W., & Pasandideh, S. H. R. (2020a). A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization, 69, 63–90.CrossRef

Khalilpourazari, S., & Mohammadi, M. (2016, January). Optimization of closed-loop Supply chain network design: A Water Cycle Algorithm approach. In 2016 12th international conference on industrial engineering (ICIE) (pp. 41–45). IEEE.

Khalilpourazari, S., Naderi, B., & Khalilpourazary, S. (2020b). Multi-Objective Stochastic Fractal Search: A powerful algorithm for solving complex multi-objective optimization problems. Soft Computing, 24(4), 3037–3066.CrossRef

Khalilpourazari, S., & Pasandideh, S. H. R. (2016, January). Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. In 2016 12th international conference on industrial engineering (ICIE) (pp. 36–40). IEEE. https://doi.org/10.1109/induseng.2016.7519346

Khalilpourazari, S., & Pasandideh, S. H. R. (2020). Sine–cosine crow search algorithm: Theory and applications. Neural Computing and Applications, 32,32, 7725–7742.CrossRef

Khalilpourazari, S., Pasandideh, S. H. R., & Niaki, S. T. A. (2019). Optimizing a multi-item economic order quantity problem with imperfect items, inspection errors, and backorders. Soft Computing, 23(22), 11671–11698.CrossRef

Khalilpourazari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020c). Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Annals of Operations Research, 289, 1–30.CrossRef

Khishtandar, S. (2019). Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design. Applied Energy, 236, 183–195.CrossRef

Kropat, E., Weber, G. W., Alparslan-Gök, S. Z., & Özmen, A. (2014). Inverse problems in complex multi-modal regulatory networks based on uncertain clustered data. In A. Pinto & D. Zilberman (Eds.), Modeling, dynamics, optimization and bioeconomics I (pp. 437–451). Cham: Springer.CrossRef

Kumar, R., Bilga, P. S., & Singh, S. (2017). Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. Journal of Cleaner Production, 164, 45–57.CrossRef

Lalmazloumian, M., Wong, K. Y., Govindan, K., & Kannan, D. (2016). A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Annals of Operations Research, 240(2), 435–470. https://doi.org/10.1007/s10479-013-1421-5.CrossRef

Li, C., Cui, L., Liu, F., & Li, L. (2013). Multi-objective NC machining parameters optimization model for high efficiency and low carbon. Jixie Gongcheng Xuebao (Chinese Journal of Mechanical Engineering), 49(9), 87–96.CrossRef

Liu, S., & Forrest, J. Y. L. (2010). Grey systems: Theory and applications. Berlin: Springer.CrossRef

Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy Sets and Systems, 94(2), 227–237. https://doi.org/10.1016/S0165-0114(96)00236-9.CrossRef

Liu, Z., Li, X., Wu, D., Qian, Z., Feng, P., & Rong, Y. (2019). The development of a hybrid firefly algorithm for multi-pass grinding process optimization. Journal of Intelligent Manufacturing, 30(6), 2457–2472.CrossRef

Lu, C., Gao, L., Li, X., & Chen, P. (2016). Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm. Journal of Cleaner Production, 137, 1516–1531. https://doi.org/10.1016/j.jclepro.2016.07.029.CrossRef

McParland, D., Baron, S., O’Rourke, S., Dowling, D., Ahearne, E., & Parnell, A. (2019). Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models. Journal of Intelligent Manufacturing, 30(3), 1259–1270.CrossRef

Mogale, D. G., Cheikhrouhou, N., & Tiwari, M. K. (2020). Modelling of sustainable food grain supply chain distribution system: A bi-objective approach. International Journal of Production Research, 58(18), 5521–5544. https://doi.org/10.1080/00207543.2019.1669840.CrossRef

Mogale, D. G., Ghadge, A., Kumar, S. K., & Tiwari, M. K. (2019). Modelling supply chain network for procurement of food grains in India. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1682707.CrossRef

Mohammadi, M., & Khalilpourazari, S. (2017, February). Minimizing makespan in a single machine scheduling problem with deteriorating jobs and learning effects. In Proceedings of the 6th international conference on software and computer applications (pp. 310–315).

Onwubolu, G. C., & Kumalo, T. (2001). Optimization of multipass turning operations with genetic algorithms. International Journal of Production Research, 39(16), 3727–3745. https://doi.org/10.1080/00207540110056153.CrossRef

Phuc, P. N. K., Vincent, F. Y., & Tsao, Y. C. (2017). Optimizing fuzzy reverse supply chain for end-of-life vehicles. Computers & Industrial Engineering, 113, 757–765. https://doi.org/10.1016/j.cie.2016.11.007.CrossRef

Pishvaee, M. S., & Khalaf, M. F. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling, 40(1), 407–418. https://doi.org/10.1016/j.apm.2015.04.054.CrossRef

Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20. https://doi.org/10.1016/j.fss.2012.04.010.CrossRef

Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., & Farrokhi-Asl, H. (2020). A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. International Journal of Systems Science: Operations & Logistics, 7(1), 60–75.

Radovanović, M. (2019). Multi-objective optimization of multi-pass turning AISI 1064 steel. The International Journal of Advanced Manufacturing Technology, 100(1–4), 87–100.CrossRef

Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108–120. https://doi.org/10.1016/j.knosys.2014.01.016.CrossRef

Rao, R. V., & Kalyankar, V. D. (2013). Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Scientia Iranica, 20(3), 967–974.

Rodić, D., Sekulić, M., Gostimirović, M., Pucovsky, V., & Kramar, D. (2020). Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01555-4.CrossRef

Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2017). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2533-0.CrossRef

Savku, E., & Weber, G. W. (2018). A stochastic maximum principle for a markov regime-switching jump-diffusion model with delay and an application to finance. Journal of Optimization Theory and Applications, 179(2), 696–721.CrossRef

Shin, Y. C., & Joo, Y. S. (1992). Optimization of machining conditions with practical constraints. The International Journal of Production Research, 30(12), 2907–2919. https://doi.org/10.1080/00207549208948198.CrossRef

Srinivas, J., Giri, R., & Yang, S. H. (2009). Optimization of multi-pass turning using particle swarm intelligence. The International Journal of Advanced Manufacturing Technology, 40(1–2), 56–66. https://doi.org/10.1007/s00170-007-1320-5.CrossRef

Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193–214. https://doi.org/10.1016/j.fss.2007.08.010.CrossRef

Tosarkani, B. M., Amin, S. H., & Zolfagharinia, H. (2019). A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network. International Journal of Production Economics, 224, 107557.CrossRef

Toufik, A. (2020). Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning. Journal of King Saud University-Engineering Sciences. https://doi.org/10.1016/j.jksues.2020.05.001.CrossRef

Tsao, Y. C., & Thanh, V. V. (2019). A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transportation Research Part E: Logistics and Transportation Review, 124, 13–39.CrossRef

Vijayakumar, K., Prabhaharan, G., Asokan, P., & Saravanan, R. (2003). Optimization of multi-pass turning operations using ant colony system. International Journal of Machine Tools and Manufacture, 43(15), 1633–1639. https://doi.org/10.1016/S0890-6955(03)00081-6.CrossRef

Wang, X. K. (2006). Mechanical processing handbook. Beijing: China Machine Press.

Wang, Q., Liu, F., & Wang, X. (2014). Multi-objective optimization of machining parameters considering energy consumption. The International Journal of Advanced Manufacturing Technology, 71(5–8), 1133–1142. https://doi.org/10.1007/s00170-013-5547-z.CrossRef

Wang, X., Tian, J., Wang, R., Xu, J., Chen, S., Wang, J., et al. (2019). Multi-objective economic dispatch of cogeneration unit with heat storage based on fuzzy chance constraint. Energies, 12(1), 103.CrossRef

Weber, G. W., Gök, S. Z. A., & Kropat, E. (2011). Recent advances on ellipsoidal cooperative Games. In GAME THEORY AND MANAGEMENT. Collected abstracts of papers presented on the fifth international conference game theory and management/editors Leon A. Petrosyan and Nikolay A. Zenkevich.–SPb.: Graduate School of Management SPbU, 2011.–268 p. The collection contains abstracts of papers accepted for the fifth international (p. 255).

Weber, G. W., Kropat, E., & Alparslan Gök, S. Z. (2008, May). Semi-infinite and conic optimization in modern human life and financial sciences under uncertainty. In ISI proceedings of 20th mini-EURO conference (pp. 180–185). Neringa: Continuous Optimization and Knowledge-Based Technologies.

Weiss, E. B. (1992). United Nations conference on environment and development. International Legal Materials, 31(4), 814–817.CrossRef

Xu, S., Wang, Y., & Huang, F. (2017). Optimization of multi-pass turning parameters through an improved flower pollination algorithm. The International Journal of Advanced Manufacturing Technology, 89(1–4), 503–514. https://doi.org/10.1007/s00170-016-9112-4.CrossRef

Yang, S. H., & Natarajan, U. (2010). Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches. The International Journal of Advanced Manufacturing Technology, 49(5–8), 773–784.CrossRef

Yıldırım, M. H., Özmen, A., Bayrak, Ö. T., & Weber, G. W. (2012). Electricity price modelling for Turkey. In D. Klatte, H. J. Lüthi, & K. Schmedders (Eds.), Operations research proceedings 2011 (pp. 39–44). Berlin: Springer.CrossRef

Yildiz, A. R. (2012). A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210, 81–88. https://doi.org/10.1016/j.ins.2012.03.005.CrossRef

Yildiz, A. R. (2013). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Sciences, 220, 399–407. https://doi.org/10.1016/j.ins.2012.07.012.CrossRef

Yuan, C., Zhai, Q., & Dornfeld, D. (2012). A three dimensional system approach for environmentally sustainable manufacturing. CIRP Annals-Manufacturing Technology, 61(1), 39–42. https://doi.org/10.1016/j.cirp.2012.03.105.CrossRef

Zhao, G. Y., Liu, Z. Y., He, Y., Cao, H. J., & Guo, Y. B. (2017). Energy consumption in machining: Classification, prediction, and reduction strategy. Energy, 133, 142–157. https://doi.org/10.1016/j.energy.2017.05.110.CrossRef

Zhong, Q., Tang, R., & Peng, T. (2017). Decision rules for energy consumption minimization during material removal process in turning. Journal of Cleaner Production, 140, 1819–1827.CrossRef

Zhou, Y., Ahn, S., Chitturi, M., & Noyce, D. A. (2017). Rolling horizon stochastic optimal control strategy for ACC and CACC under uncertainty. Transportation Research Part C: Emerging Technologies, 83, 61–76.CrossRef

Title: Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence
Authors: Soheyl Khalilpourazari
Saman Khalilpourazary
Aybike Özyüksel Çiftçioğlu
Gerhard-Wilhelm Weber
Publication date: 01-09-2020
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 6/2021
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-020-01648-0

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