Integrated optimization of cutting parameters and tool path for cavity milling considering carbon emissions

Cutting parameters and tool path significantly affect processing time, carbon emissions and processing cost for cavity milling. However, most current researches optimized cutting parameters and tool path independently and ignored their comprehensive effects on carbon emissions. To bridge the gap, this paper proposes a novel multi-objective optimization model to realize low-carbon-oriented integrated optimization of cutting parameters and tool path for cavity milling, which takes processing time, carbon emissions and processing cost as its objectives. A two-layer interactive solution is designed to solve the model, which fist utilizes Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for upper layer optimization of cutting parameters, and then takes its results as the input for under layer optimization of tool path using an improved genetic algorithm (GA), and finally gives feedbacks to the upper layer in each successful iteration. Rough cavity milling of a workpiece made of # 45 steel is taken as an example to illustrate the feasibility and effectiveness of the approach. Experimental results show that the proposed approach could reduce the indicators of low-carbon manufacturing and lead to a 15.38% and 1.92% decrease in average carbon emissions when compared with the traditional approaches and serial optimization approach, respectively.