Optimization of dry machining parameters for high-purity graphite in end-milling process by artificial neural networks: A case study

The machining factors affecting the tool wear and the surface roughness produced in the end-milling process are generally the cutting speed, the feed rate, the depth of cut, etc. This article focused on finding an optimal cutting parameter setting of high-purity graphite under dry machining conditions by an artificial neural network and the Sequential Quadratic Programming method [1]. This algorithm yielded better performance than the traditional methods such as the Taguchi method and the Design of Experiments (DOE) approach. Additionally, the tool worn surfaces after machining were examined with tool electron microscopy (TEM) to observe the tool wear mechanisms.