In the pursuit to lighter, less consuming products, manufacturers, especially in aviation and automotive industries, are turning more and more to using lightweight alloys such as the ones based on magnesium. Higher requirements for increased productivity have led to concepts like high-speed machining (HSM), high feed machining (HFM) or high-efficiency machining. Tighter regulations concerning requiring for more environmentally friendly industrial processes led to limitations in the use of cooling liquids and a search for cooling methods with less impact (dry cutting, cryogenic cooling, near dry machining and others). Better machining processes can only be achieved by modelling and optimization. This paper briefly presents the results obtained by our research team concerning the modelling and optimization attempts on face milling of magnesium alloys using different methods: design of experiments (e.g. factorial design, response surface method), fuzzy logic or neural networks.
An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye
