Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligen
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Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence Soheyl Khalilpourazari1,5
· Saman Khalilpourazary2 · Aybike Özyüksel Çiftçioglu ˘ 3 · Gerhard-Wilhelm Weber4,6
Received: 8 May 2020 / Accepted: 13 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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 intelligencebased 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. Keywords Multi-pass machining · Turning process · Robust optimization · Energy efficiency · Multi-objective optimization · Evolutionary computation
Introduction
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Soheyl Khalilpourazari [email protected] Saman Khalilpourazary [email protected] Aybike Özyüksel Çiftçio˘glu [email protected] Gerhard-Wilhelm Weber [email protected]
1
Department of Mechanical, Industrial and Aerospace Engineering (MIAE), Concordia University, Montreal, Canada
2
Faculty of Mechanical Engineering, Urmia University of Technology, Urmia, Iran
3
Faculty of Engineering, Department of Civil Engineering, Manisa Celal Bayar University, 45140 Manisa, Turkey
4
Faculty of Engineering Management, Poznan University of Technology, ul. Strzelecka, 1160-965 Poznan, Poland
5
Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada
6
Institute of Applied Mathematics, Middle East Technical University, 06800 Ankara, Turkey
One of the most crucial parts of the industrial sector is manufacturing, which is referred to as a process that takes raw materials and turns that material into final products. Fang et al. (2011) claimed that one-half of the global energy consumption is related to manufacturing. In the last decade, industrialization made an enormous increase in
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