Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints

Benjie Li, Hualin Zheng, Xiao Yang, Liang Guo and Binglin Li
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Benjie Li: College of Electromechanic Engineering, Southwest Petroleum University, Chengdu 610500, China
Hualin Zheng: College of Electromechanic Engineering, Southwest Petroleum University, Chengdu 610500, China
Xiao Yang: Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing 400067, China
Liang Guo: College of Electromechanic Engineering, Southwest Petroleum University, Chengdu 610500, China
Binglin Li: College of Electromechanic Engineering, Southwest Petroleum University, Chengdu 610500, China

Energies, 2020, vol. 13, issue 22, 1-17

Abstract: Motorized spindle system is one of the crucial components affecting the machine tools energy performance. Many previous studies have examined its energy optimization problems, however, most such studies focused mainly on parameters optimization to improve material removal energy efficiency or reduce total energy consumption. A missing research area is energy optimization problem considering thermal stability and productivity constraints simultaneously. Against this background, an energy optimization approach of motorized spindle system is presented with consideration of thermal stability and productivity adequately, with the goal of maximization of energy efficiency and material removal rate, and minimization of spindle average temperature which is closely associated with thermal stability. Firstly, the energy characteristics of motorized spindle and its cooling system are mathematically modelled. Then, a multi-objective optimization model is established to take the maximum energy efficiency, minimum spindle average temperature, and maximum material removal rate as objectives. The optimal solution is obtained by solving the proposed optimization model with the Non-dominated Sorted Genetic Algorithm-II (NSGA-II). Finally, a case study is introduced to validate the proposed method and the results indicate that the proposed method is more effective to find optimal decision variables for balancing the considered objectives compared with the existing optimization method.

Keywords: motorized spindle system; energy efficiency; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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