基于遗传算法工具箱的精密车削表面粗糙度预测

Prediction of Surface Roughness in Precision Turning using Genetic Algorithm Toolbox

通过优化切削参数(切削速度、进给量和切削深度)可以获得精密车削马氏体3J33最佳表面粗糙度。切削试验采用二次旋转组合设计的方法,使用谢菲尔大学研究的遗传算法工具箱所获得的在特定约束条件下表面粗糙度预测模型,应用遗传算法对切削用量对表面粗糙度的优化计算,并将其结果和非线性优化计算结果进行比较,两种计算结果基本吻合。本文的目的在于在特定切削参数条件下预测表面粗糙度。

Optimum cutting parameters （cutting speed, feed rate and depth of cut） of maraging 3J33 are determined to enable minimum surface roughness under the certain constraints in precision turning maraging 3J33. Cutting experiments axe designed based on the quadratic rotary combination design technique. A predictive model with certain constraints for surface roughness is solved by an effective genetic algorithm toolbox developed hy University of Sheffield. The results with genetic algorithm toolbox are compared with non-linear optimal method＇s results and found to be in good agreement. The goal of this study is to predict surface roughness and to fred the optimum cutting conditions for the precision turning with good surface roughness.