Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption

Shun Jia, Shang Wang, Jingxiang Lv, Wei Cai, Na Zhang, Zhongwei Zhang and Shuowei Bai
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Shun Jia: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Shang Wang: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jingxiang Lv: Institute of Smart Manufacturing Systems, School of Construction Machinery, Chang’an University, Xi’an 710064, China
Wei Cai: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Na Zhang: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Zhongwei Zhang: School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Shuowei Bai: School of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266071, China

Sustainability, 2021, vol. 13, issue 24, 1-23

Abstract: Energy-saving and emission reduction are recognized as the primary measure to tackle the problems associated with climate change, which is one of the major challenges for humanity for the forthcoming decades. Energy modeling and process parameters optimization of machining are effective and powerful ways to realize energy saving in the manufacturing industry. In order to realize high quality and low energy consumption machining of computer numerical control (CNC) lathe, a multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption is proposed. By analyzing the energy consumption characteristics in the process of machining and introducing practical constraints, such as machine tool equipment performance and tool life, a multi-objective optimization model with turning process parameters as optimization variables and high quality and low energy consumption as optimization objectives is established. The model is solved by non-dominated sorting genetic algorithm-II (NSGA-II), and the pareto optimal solution set of the model is obtained. Finally, the machining process of shaft parts is studied by CK6153i CNC lathe. The results show that 38.3% energy consumption is saved, and the surface roughness of workpiece is reduced by 47.0%, which verifies the effectiveness of the optimization method.

Keywords: energy saving; multi-objective optimization; transient steady state energy consumption; process parameters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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