基于改进鲸鱼优化算法的低碳车削参数优化

Low Carbon Turning Parameter Optimization Based on Improved Whale Optimization Algorithm

摘要: 随着环境问题日益凸显，绿色制造成为制造业可持续发展的关键策略。在这一背景下，本文旨在研究车削过程中的工艺参数优化，并建立了一个以低碳排放为目标的销轴加工优化模型。针对传统鲸鱼优化算法存在的问题，如易陷入局部最优解、缺乏多样性和收敛速度慢等，进行了改进。通过改进的鲸鱼优化算法(WOA)算法，本文应用于某型号销轴车削加工工艺，搜索求解最优解，并获得对应的最佳工艺参数。实验结果显示，与未优化前相比，采用改进的WOA算法得到的优化结果碳排放量降低了16.1%，加工成本降低了22.3%。这一结果充分验证了本文所提出的模型方法和工艺参数优化方法的有效性，并为数控车床制造提供了可行的理论指导。

Abstract: With the escalating concern over environmental issues, green manufacturing has emerged as a cru-cial strategy for the sustainable development of the manufacturing industry. In this regard, this study aims to investigate the optimization of process parameters in the turning process and devel-op an optimization model for the machining of a sales shaft, targeting low carbon emissions. To ad-dress the limitations of the conventional Whale Optimization Algorithm (WOA), such as susceptibil-ity to local optima, lack of diversity, and slow convergence, enhancements have been made. Utilizing the improved WOA algorithm, this paper applies it to the machining process of a specific model of sales shaft, searching for and determining the optimal solution while obtaining the corresponding process parameters. Experimental results demonstrate that the optimized results using the im-proved WOA algorithm effectively reduce carbon emissions by 16.1% and decrease processing costs by 22.3% compared to pre-optimization outcomes. This outcome convincingly validates the efficacy of the proposed model and process parameter optimization technique, offering practical theoretical guidance for CNC lathe manufacturing.