Multi-objective analysis and optimization of energy aspects during dry and MQL turning of unreinforced polypropylene (PP): an approach based on ANOVA, ANN, MOWCA, and MOALO

Abstract

This paper evaluates the energy criteria for machinability during CNC turning of unreinforced polypropylene. The aspects considered are tangential force (F-Z), cutting power (P-c), material removal rate (MRR), cutting energy (E-c), and specific cutting energy (E-cs). More specifically, the study examines the effect of turning with minimum quantity lubrication (MQL) and dry cutting, as well as cutting parameters (cutting speed V-c, feed rate f, and depth of cut a(p)) and tool nose radius (r(epsilon)). Analysis of variance, artificial neural network, multi-objective water cycle algorithm, and multi-objective ant lion optimizer are mathematical tools used to analyze, model, and optimize responses effectively. The models are validated both experimentally and through the K-fold cross-validation method. Particular emphasis is placed on optimizing the specific cutting energy (SCE) due to its correlation with electricity consumption. Until now, the importance of MQL on SCE consumption has not been reported in the literature regarding polymer machining.