采用改进人工蜂群算法的低碳铣削参数优化

Optimization of Low Carbon Milling Parameters Using Improved Artificial Bee Colony Algorithm

摘要: 出于对国家双碳号召的响应，有效推动制造车间节能减排，本文对铣削加工过程中工艺参数优化进行研究，建立以最低碳排放和最低加工成本为目标的铣削加工优化模型。在此基础上，针对标准人工蜂群算法存在过多无用迭代、易过早陷入局部最优解的缺点，引入贴近最优思想，并将其与粒子群算法结合，增强粒子的全局搜索能力，改善求解效率与寻优精度。结果表明，实验加工工艺的碳排放减少了11.4%，加工成本减少了7%，表明了该模型的准确性和高效性。铣削加工模型为加工工艺的碳排放量化和加工成本最小化问题提供了一种高效的可行方案。

Abstract: In response to the national call for “double carbon” and to effectively promote energy conservation and emission reduction in manufacturing workshops, this paper studies the optimization of process parameters in the milling process, and establishes a milling optimization model with the goal of minimum carbon emissions and minimum processing costs. On this basis, in view of the shortcom-ings of the standard artificial bee colony algorithm, which has too many useless iterations and is easy to fall into the local optimal solution prematurely, the idea of close to the optimal solution is introduced and combined with particle swarm optimization to enhance the global search ability of particles and improve the solution efficiency and optimization accuracy. The results show that the carbon emission of the experimental processing technology is reduced by 11.4%, and the pro-cessing cost is reduced by 7%, indicating the accuracy and efficiency of the model. The milling mod-el provides an efficient and feasible scheme for the carbon emission quantification and processing cost minimization of the processing process.