A statistical analysis applied for optimal cooling system selection and for a superior surface quality of machined magnesium alloy parts

Abstract

When using magnesium for industrial-scale production, a series of aspects must be taken into consideration, such as the ignition risk (due to magnesium reaction with water resulting hydrogen), the cooling fluids representing up to 16%–20% of the manufacturing costs as well as being environmentally harmful and the costs of waste disposal. Therefore, the selection of an adequate cooling system is a very important factor, which may eliminate all the above-mentioned disadvantages. This research investigates the influences that cooling systems have on surface quality of magnesium alloy parts. An experimental analysis for milling operations was carried out using three cooling methods: dry cutting, minimum quantity lubrication and compressed air. Surface quality was assessed according to three aspects: surface roughness, material microhardness and residual stress variation. A statistical analysis of the results was performed in order to emphasize the effects of the machining parameters and cooling methods on surface quality. Furthermore, an adaptive neuro-fuzzy inference system, capable to predict surface roughness based on machining conditions, was developed. A very good agreement was found between the experimental values and the estimated ones. The results have shown that in general, the minimum quantity lubrication cooling system generates a superior surface quality compared to other systems.