China Mechanical Engineering ›› 2022, Vol. 33 ›› Issue (21): 2547-2553.DOI: 10.3969/j.issn.1004-132X.2022.21.004

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Energy Footprint Modeling and Parameter Optimization in Workshop Manufacturing Processes

TIAN Ying;SHAO Wenting;WANG Taiyong   

  1. School of Mechanical Engineering,Tianjin University,Tianjin,300350
  • Online:2022-11-10 Published:2022-11-23

车间生产过程的能量足迹建模与加工参数协同优化

田颖;邵文婷;王太勇   

  1. 天津大学机械工程学院,天津,300350
  • 作者简介:田颖,女,副教授。研究方向为智能化制造系统、生产车间节能调度与优化、数字孪生、工业物联网等。发表论文20余篇。E-mail:tianying@tju.edu.cn。
  • 基金资助:
    国家自然科学基金(51975407)

Abstract:  To reduce the energy consumption during the parts manufacturing processes, an energy-saving focused multi-equipment machining parameter collaborative optimization method was proposed. Taking the workshop manufacturing processes with machines and robots as target, the energy footprint models considering cutting tool degradation processes were setup for the workshop system. Considering the cost index function of tool life and stability of robotic transportation, a multi-objective collaborative optimization model of machining parameters with multi-equipment systems was established. Taking the processing time as constraint, the artificial bee colony algorithm was used to obtain the global optimal parameters. Experimental results show that the optimized parameters may reduce the energy consumption of CNC machines by 17.97%, and reduce the energy consumption of the robotic transportation by 18.13%. 

Key words: workshop manufacturing process, energy footprint, multi-objective optimization, tool life

摘要: 为降低零件加工过程的生产能耗,提出一种面向节能的车间生产过程多装备加工参数协同优化方法。以包含机床与机器人的定制化生产车间为研究对象,建立了考虑刀具退化动态过程的生产车间系统能量足迹模型。考虑刀具寿命、机器人运输平稳性的成本指标函数,建立了多装备系统的加工参数协同多目标优化模型。以加工时间为约束条件,使用蜂群算法获取了最优参数。实验表明,以节能目标为主的优化方案可降低机床加工能耗17.97%,降低机器人运输能耗18.13%。

关键词: 车间生产过程, 能量足迹, 多目标优化, 刀具寿命

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