现代制造工程 ›› 2024, Vol. 520 ›› Issue (1): 80-88.doi: 10.16731/j.cnki.1671-3133.2024.01.012

• 制造技术/工艺装备 • 上一篇    下一篇

基于改进白鲸优化算法的低碳柔性工艺规划*

孔云1, 周学良1, 冷杰武2   

  1. 1 湖北汽车工业学院机械工程学院,十堰 442002;
    2 广东工业大学机电工程学院,广州 510006
  • 收稿日期:2023-06-05 出版日期:2024-01-18 发布日期:2024-05-29
  • 通讯作者: 周学良,教授,硕士研究生导师,主要研究方向为数字化设计与制造、制造系统调度与优化。E-mail:zhouxl@huat.edu.cn
  • 作者简介:孔云,硕士研究生,主要研究方向为智能制造过程规划、车间调度。E-mail:744746619@qq.com
  • 基金资助:
    *国家自然科学基金资助项目(52075107);湖北省高等学校优秀中青年科技创新团队计划项目(T2020018)

Low-carbon flexible process planning based on improved beluga whale optimization algorithm

KONG Yun1, ZHOU Xueliang1, LENG Jiewu2   

  1. 1 School of Mechanical Engineering,Hubei University of Automotive Technology,Shiyan 442002,China;
    2 School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China
  • Received:2023-06-05 Online:2024-01-18 Published:2024-05-29

摘要: 针对数控加工过程的低碳柔性工艺规划问题,建立了以加工过程中机床、刀具和装夹的转换次数和碳排放为目标的优化模型,提出了基于改进白鲸优化算法的求解方法。该算法通过引入变异操作增强其全局搜索能力,并在种群初始化中采用以加工资源为导向的启发式规则选择策略与随机生成相结合的方式,以提高初始种群的质量,加快算法的收敛速度。最后,以一个零件的加工信息为测试实例,验证所提模型的可行性,并与其他3种算法进行对比。试验结果表明,所提出的算法能够获得更优解,且具有更快的收敛速度。

关键词: 柔性工艺规划, 碳排放, 转换次数, 白鲸优化算法

Abstract: Aiming at the low-carbon flexible process planning problem of CNC machining process,an optimization model aiming at the conversion times of machine tools,tools and clamping and carbon emissions in the machining process was established,and a solution method based on the improved beluga whale optimization algorithm was proposed. The algorithm enhances its global search ability by introducing mutation operations,and adopts a heuristic rule selection strategy oriented to processing resources combined with random generation in population initialization to improve the quality of the initial population and accelerates the convergence speed of the algorithm. Finally,taking the machining information of a part as a test example,the feasibility of the proposed model was verified,and compared with the other three algorithms. The experimental results show that the proposed algorithm can obtain a better solution and has a faster convergence speed.

Key words: flexible process planning, carbon emissions, number of conversions, beluga whale optimization algorithm

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