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CNC Corner Milling Parameters Optimization Based on Variable-Fidelity Metamodel and Improved MOPSO Regarding Energy Consumption
International Journal of Precision Engineering and Manufacturing-Green Technology ( IF 4.2 ) Pub Date : 2021-05-17 , DOI: 10.1007/s40684-021-00338-3
Yang Yang, Yuan Wang, Qianfeng Liao, Jiongliang Pan, Junyu Meng, Hao Huang

In the corner milling process, processing energy consumption is a very important objective, since the energy efficiency of CNC machine is barely above 14.8%. Meanwhile, the excessive processing temperature will increase the thermal deformation of the product and leads to quality decline. Improper process parameters will lead to unnecessary high temperature and energy consumption. By optimizing the process parameters, the appropriate temperature and Specific Energy Consumption can be obtained. This study investigated into modeling Specific Energy Consumption and temperature in corner milling process using variable-fidelity metamodels. The adopted variable-fidelity metamodels are constructed by Hierarchical Kriging, in which 48 sets of low-fidelity data obtained from the AdvantEdge software simulation are used to reflect the trends of the metamodels, and 16 sets of high-precision data obtained from physical experiments are used to calibrate the trends. The experimental cost is reduced and the prediction accuracy is increased by making full use of both sets of data. An improved K-means Multi-objective Particle Swarm Optimization algorithm was adopted and applied on the multi-objective corner milling parameters optimization problem to find satisfactory specific energy consumption and temperature. The obtained Pareto solutions can provide guidance for selecting process parameters according to different requirements, such as reducing energy consumption or temperature.



中文翻译:

基于可变保真度元模型和改进的关于能耗的MOPSO的CNC角铣削参数优化

在角铣削过程中,加工能耗是一个非常重要的目标,因为CNC机床的能源效率仅高于14.8%。同时,过高的加工温度将增加产品的热变形并导致质量下降。不正确的工艺参数将导致不必要的高温和能耗。通过优化工艺参数,可以获得适当的温度和比能耗。本研究调查了使用可变逼真度元模型对角铣削过程中的比能耗和温度进行建模的过程。采用的可变保真度元模型是由Hierarchical Kriging构建的,其中使用了AdvantEdge软件模拟获得的48组低保真度数据来反映元模型的趋势,从物理实验获得的16组高精度数据用于校准趋势。通过充分利用两组数据,可以降低实验成本并提高预测精度。采用改进的K-均值多目标粒子群算法,并将其应用于多目标角铣削参数优化问题,以求得满意的单位能耗和温度。所获得的帕累托解决方案可以为根据不同要求(例如降低能耗或降低温度)选择工艺参数提供指导。采用改进的K-均值多目标粒子群算法,并将其应用于多目标角铣削参数优化问题,以求得满意的单位能耗和温度。所获得的帕累托解决方案可以为根据不同要求(例如降低能耗或降低温度)选择工艺参数提供指导。采用改进的K-均值多目标粒子群算法,并将其应用于多目标角铣削参数优化问题,以求得满意的单位能耗和温度。所获得的帕累托解决方案可以为根据不同要求(例如降低能耗或降低温度)选择工艺参数提供指导。

更新日期:2021-05-17
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