Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach

摘要: Abstract From the perspective of energy conservation, notion modelling consumption as a vital element environmental sustainability in any manufacturing industry remains current and important focus study for climate change experts across globe. Among operations, machining is widely performed. Extensive studies by peer researchers reveal that was on optimizing aspects (e.g. surface roughness, tool wear rate, dimensional accuracy) operations computational intelligence methods such analysis variance, grey relational analysis, Taguchi method, artificial neural network. Alternatively, an evolutionary based multi-gene genetic programming approach can be applied but its effective functioning depends complexity measure chosen fitness function. This proposes new complexity-based orthogonal basis functions compares performance to standardized milling process. The hidden relationships between input process parameters are unveiled conducting sensitivity parametric analysis. these relationships, optimum set settings obtained which will conserve greater amount from operations. It found cutting speed has highest impact followed feed rate depth cut.