TY - JOUR
T1 - A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning
AU - Xiao, Qinge
AU - Li, Congbo
AU - Tang, Ying
AU - Li, Lingling
AU - Li, Li
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (No. 51475059 ) and the National Key R&D Program of China (No. 2017YFF0207903 ). The authors would like to thank Prof. Liping Duan for her careful proof reading and grammar checking.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Selection of optimum process parameters is often regarded as an effective strategy for improving energy efficiency during computer numerical control (CNC) turning. Previous optimization methods are typically developed for specific machining configurations. To generalize the energy-aware parametric optimization for multiple machining configurations, we propose a two-stage knowledge-driven method by integrating data mining (DM) techniques and fuzzy logic theory. In the first stage, a modified association rule mining algorithm is developed to discover empirical knowledge, based on which a fuzzy inference engine is established to achieve preliminary optimization. In the second stage, with the knowledge obtained by investigating the effects of parameters on specific energy consumption covering a variety of configurations, an iterative fine-tuning is carried out to realize Pareto-optimization of turning parameters for minimizing specific energy consumption and processing time. The simulation results show that the method has a high potential for enhancing energy efficiency and time efficiency in turning system. Furthermore, compared with three heuristic optimization techniques, i.e. Genetic Algorithm, Ant Colony Algorithm and Particle Swarm Algorithm, the proposed method demonstrates certain superiority.
AB - Selection of optimum process parameters is often regarded as an effective strategy for improving energy efficiency during computer numerical control (CNC) turning. Previous optimization methods are typically developed for specific machining configurations. To generalize the energy-aware parametric optimization for multiple machining configurations, we propose a two-stage knowledge-driven method by integrating data mining (DM) techniques and fuzzy logic theory. In the first stage, a modified association rule mining algorithm is developed to discover empirical knowledge, based on which a fuzzy inference engine is established to achieve preliminary optimization. In the second stage, with the knowledge obtained by investigating the effects of parameters on specific energy consumption covering a variety of configurations, an iterative fine-tuning is carried out to realize Pareto-optimization of turning parameters for minimizing specific energy consumption and processing time. The simulation results show that the method has a high potential for enhancing energy efficiency and time efficiency in turning system. Furthermore, compared with three heuristic optimization techniques, i.e. Genetic Algorithm, Ant Colony Algorithm and Particle Swarm Algorithm, the proposed method demonstrates certain superiority.
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U2 - 10.1016/j.energy.2018.09.191
DO - 10.1016/j.energy.2018.09.191
M3 - Article
AN - SCOPUS:85055979413
VL - 166
SP - 142
EP - 156
JO - Energy
JF - Energy
SN - 0360-5442
ER -