Main content area

Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment

Tian, Changle, Zhou, Guanghui, Zhang, Junjie, Zhang, Chao
Journal of cleaner production 2019 v.226 pp. 706-719
algorithms, carbon, case studies, climate change, cutting, emissions, models, sustainable manufacturing, system optimization
Carbon emissions have drawn widely attention due to the worsening climate changes. In a machining process, a reasonable selection of cutting parameters can not only save the production cost and time, but also reduce the production carbon emissions. In addition, the wear conditions of different cutting tools also have great influence on cutting parameters selection as they could cause huge difference on carbon emissions. However, in traditional optimization methods of cutting parameters, the tool wear conditions are always ignored. Thus, in order to overcome this limitation, an optimization method of cutting parameters considering the tool wear conditions is developed. Firstly, the quantified relationships among cutting parameters, tool wear and production indexes (production carbon emissions, cost and time) are analysed. Then, a multi-objective cutting parameters optimization model is established based on the above production indexes to determine the optimal cutting parameters and tools. Thirdly, a modified NSGA-II algorithm is used to resolve the proposed model. Finally, a case study is designed to demonstrate the advantages and feasibility of the proposed approach. The results show that (i) the optimal cutting parameters change with the tool wear conditions; (ii) For the same type of cutting tools with different wear conditions, the optimal values of production carbon emissions, cost and time increase along with the raise of the tool wear conditions; (iii) For different available cutting tools with different tool wear conditions, it is necessary to apply a multi-objective optimization method to decide the optimal production carbon emissions, cost and time.