基于遗传算法的磨削力模型系数优化及验证OACSTPCD

When solving problems in the grinding force model,most of the methods of segmental calculation or col-umn equations were used to calculate each coefficient directly,which not only demanded a large amount of calcula-tion but also could not guarantee its accuracy.In addition the traditional regression model was easy to fall into local optimal,difficult to describe the nonlinear relationship.Therefore,the genetic algorithm was introduced into the parameter optimization of the nonlinear fitting function,and the coefficient optimization method of the theoretical model of grinding force was studied based on the existing model data such as the model of cylindrical transverse grinding,the model of plane grinding and the model of cylindrical longitudinal grinding.Correlation analysis results showed that the predicted accuracy of grinding force of the three models was increased by 14.69％-42.54％.The average error of normal grinding force predicted by the three models was 5.9％,9.13％and 3.23％,respectively.The mean error of tangential force was 6.78％,8.36％and 3.69％,respectively.Through comparison,it could be concluded that the optimized model had a better fitting degree,and the prediction accuracy of the model was signifi-cantly improved.The nonlinear fitting function GA-LSQ algorithm optimized by genetic algorithm was more suitable for solving grinding force model and could provide reference for predicting grinding force and parameter optimization in actual production.