An attempt was made in the present work to study the influence of machining parameters on wear of different types of cutting tools during turning of hardened die steel. Multilayer CVD (chemical vapor deposition) coated, uncoated and PVD (physical vapor deposition) coated ceramic inserts were employed. Response surface method and artificial neural network (ANN) models were employed to predict tool wear. The machining parameters considered in the study were feed rate, cutting speed, depth of cut and cutting time. Central composite design (CCD) technique was utilized to plan and carry out the trials in a systematic manner. The ANN and RSM (response surface methodology) models were developed. Models have exhibited higher degree of accuracy (R² > 98.5% and MSE < 0.2%) ensuring better feasibility for prediction. ANOVA analysis revealed that cutting speed, cutting time, feed rate and depth of cut as individual were statistically significant influencing parameters on tool wear. Scanning electron microscope images have illustrated that multilayer coated cutting tool exhibited principal wear mechanisms such as abrasion, crater wear and edge chip-off. Cutting tools demonstrated adhesive wear at low machining parameters range and abrasive wear at greater machining parameter range.

在本工作中，试图研究加工参数对淬硬模具钢车削过程中不同类型切削刀具磨损的影响。使用了多层CVD（化学气相沉积）涂覆，未涂覆和PVD（物理气相沉积）涂覆的陶瓷刀片。响应面法和人工神经网络（ANN）模型被用来预测工具磨损。研究中考虑的加工参数为进给速度，切削速度，切削深度和切削时间。中央复合设计（CCD）技术被用来系统地计划和进行试验。开发了ANN和RSM（响应面方法）模型。模型表现出更高的准确性（R ² > 98.5％，MSE <0.2％），确保更好的预测可行性。方差分析表明，切削速度，切削时间，进给速度和切削深度是单独的，在统计上是影响刀具磨损的重要参数。扫描电子显微镜图像表明，多层涂层切削刀具表现出主要的磨损机理，例如磨损，月牙洼磨损和边缘碎裂。切削工具在较低的加工参数范围内表现出粘合剂磨损，而在较大的加工参数范围内表现出磨料磨损。