Modelling and Optimization of Energy Consumption for Feature Based Milling

Abstract

Energy consumption is increasing along with the world's population and industrialization level; thus, energy and resource efficiency in manufacturing is of vital importance. In order to increase energy and resource efficiency, the amount of consumed energy must first be accurately quantified for each manufacturing process. Milling is one of the most common machining operations. In this study, a prediction model for estimating theoretical energy consumption involved in milling of prismatic parts is presented. The prediction model relies on the STEP Application Protocol 224 features for volumetric information and material properties of prismatic parts. Verification tests exemplify how engineers can utilize the presented prediction model and approach to measuring machine tool energy consumption. Test results show that the prediction model runs with 5 % accuracy. Also, effect of cutter path for prismatic milling is investigated for certain features. Furthermore, response surface methodology is utilized in order to determine optimal milling parameters of slot feature in order to minimize energy consumption when machining AISI 304 stainless steel.