Study On High Efficiency NC Machining Of Die Cavity

In modern die industry, NC machining is an important part of the manufacture of die and mold, and it has great influence on production period of die and mold. The quality and efficiency of NC machining mainly depend on the selection of machining strategy and parameters. Dealing with the current situation of die cavity NC machining, this paper focuses on the following aspects with the goal of efficiency improvement in die cavity NC machining.In NC machining, rough machining costs most of the machining time. Based on the analysis of die cavity roughing, a NC tool path pattern selection model for die cavity is constructed using artificial neural network (ANN) in this paper. A three-layer BP network is adopted , and the training sets are also designed. Experiments demonstrate the validity and efficiency of the method.The construction of cutting force model is the key technology of the NC machining parameter optimization. Considering the extensive use of ball-end milling cutters and the shortcomings of analytical cutting force model, ANN is used to set up a cutting force model for ball-end milling cutters. Cutting experiments are put in practice to get the training sets. The accuracy of the model is test at last by experiments.A method for the automatic feedrate optimization based on the mean cutting force surface model is introduced to optimize the feedrate according to the 2D chip-load at different cutter location points. A simplified mean cutting force model is also constructed based on the cutting force experiments data. Through this way, the machining efficiency can be improved, and the cutting force can be kept nearly constant. The cutters used in high speed machining(HSM) have poor rigidity, therefore the method fits to HSM of die cavity due to the unfluctuating cutting force characteristic.