关键词: 低碳低噪, 螺纹车削, 切削参数, 多目标优化, 自适应小生境遗传算法

Abstract: To realize the low carbon and low noise in thread turning process,the noise objective function model and the carbon emissions objective function model included electric power carbon emissions,cutting tool carbon emissions,cutting fluid carbon emissions based on orthogonal experiment and General Regression Neural Network (GRNN) were established.By considering the constraints of machine tool specification and workpiece surface quality,a multi-objective optimization model was established,which took the cutting speed and feed as the optimization variables.The weight coefficients were introduced to transform multi-objective optimization model into single objective optimization model.A self-adaptive niching genetic algorithm was applied to solve the single objective optimization model,and the results showed that noise and carbon emissions presented apparent negative correlativity and electric power carbon emissions was the biggest part in carbon emissions.

Key words: low carbon and low noise, thread turning, cutting parameters, multi-objective optimization, self-adaptive niching genetic algorithm