武煜航, 张华, 鄢威. 面向低能耗高质量的数控加工参数优化与决策方法[J]. 制造技术与机床, 2022, (7): 101-108. DOI: 10.19287/j.mtmt.1005-2402.2022.07.018
引用本文: 武煜航, 张华, 鄢威. 面向低能耗高质量的数控加工参数优化与决策方法[J]. 制造技术与机床, 2022, (7): 101-108. DOI: 10.19287/j.mtmt.1005-2402.2022.07.018
WU Yuhang, ZHANG Hua, YAN Wei. Optimization and decision-making method for low energy consumption and high quality of NC machining parameters[J]. Manufacturing Technology & Machine Tool, 2022, (7): 101-108. DOI: 10.19287/j.mtmt.1005-2402.2022.07.018
Citation: WU Yuhang, ZHANG Hua, YAN Wei. Optimization and decision-making method for low energy consumption and high quality of NC machining parameters[J]. Manufacturing Technology & Machine Tool, 2022, (7): 101-108. DOI: 10.19287/j.mtmt.1005-2402.2022.07.018

面向低能耗高质量的数控加工参数优化与决策方法

Optimization and decision-making method for low energy consumption and high quality of NC machining parameters

  • 摘要: 为了实现数控加工系统的绿色制造,提出了一种面向低能耗高质量的数控加工参数优化方法。首先对数控加工系统能耗子系统在不同工作状态下的能量消耗特性进行分析;然后以低能耗和低表面粗糙度为目标,选择铣削速度和进给量为优化变量,建立了铣削加工系统多目标优化模型,使用NSGA-Ⅱ遗传算法实现多目标优化求解,并利用Topsis法从等价解中决策出最符合加工需求的参数方案。最后以某汽缸端面加工案例验证了优化模型的有效性,并通过对优化结果进行解析,进一步阐述了优化模型的可行性。

     

    Abstract: In order to realise the green manufacturing of CNC machining system, a CNC machining parameter optimisation method oriented to low energy consumption and high quality is proposed. Firstly, the energy consumption characteristics of the CNC machining system and its subsystems under different operating conditions are analysed; then, with low energy consumption and low surface roughness as the objectives, the milling speed and feed amount are selected as the variables, a multi-objective optimisation model of the milling machining system is established, and the NSGA-II genetic algorithm is employed to solve the proposed model, then the most suitable machining parameters are decided from the equivalent solutions using the Topsis method. Finally, the validity of the proposed approach is verified with a cylinder stator endface machining case, and the feasibility of the optimisation model is further illustrated by the analysis of the results.

     

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