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

Soheyl Khalilpourazari (), Saman Khalilpourazary (), Aybike Özyüksel Çiftçioğlu () and Gerhard-Wilhelm Weber ()
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Soheyl Khalilpourazari: Concordia University
Saman Khalilpourazary: Urmia University of Technology
Aybike Özyüksel Çiftçioğlu: Manisa Celal Bayar University
Gerhard-Wilhelm Weber: Poznan University of Technology

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 6, No 8, 1647 pages

Abstract: 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.

Keywords: Multi-pass machining; Turning process; Robust optimization; Energy efficiency; Multi-objective optimization; Evolutionary computation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-020-01648-0

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