The paper presents an intelligent control system for self-adjusting on-line cutting condition for high speed machining (self-HSM) with considering the tool-wear amount to keep the machined product’s quality in allowable limit. For realizing the self-HSM, the empirical analysis of variance (ANOVA) and artifical neural network (ANN) are used. The ANOVA is used for generating the empirical functions which are used as the boundary condition as well as constraint evaluation. The ANN is used for generating the new optimal cutting condition. Then, the self-HSM updates this cutting condition on the real machine — HS Super MC500. The new optimal cutting parameter is sent to the controller for updating the new machining condition to keep the machined part’s quality. The integration of the empirical analysis and ANN enables generating the optimal cutting parameters correctly and efficiently for high-speed milling.

本文提出了一种智能控制系统，用于自动调整高速加工的在线切削条件（self-HSM），同时考虑了刀具磨损量，以将加工产品的质量保持在允许的范围内。为了实现自我HSM，使用了方差的经验分析（ANOVA）和人工神经网络（ANN）。ANOVA用于生成经验函数，这些函数用作边界条件以及约束评估。ANN用于生成新的最佳切削条件。然后，自我HSM在实际机器HS Super MC500上更新此切削条件。新的最佳切削参数被发送到控制器，以更新新的加工条件，以保持加工零件的质量。