PREDICTION AND OPTIMIZATION OF SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN TURNING OF SKD 61 USING MINIMUM QUANTITY LUBRICATION METHOD

Abstract

This study presents a new algorithm for multi-objective optimization of the
SKD61 steel turning process under minimum lubrication (MQL) conditions and the
application of ANOVA analysis to predict the influence of cutting parameters on
surface roughness Ra (µm) and machining productivity MRR (cm3
/min) in turning
of SKD 61 mould steel. The experimental research was carried out by combining
the MQL-IoT system with the ability to adaptive control capabilities based on data
collected during the experiment. The goal of the study is to maximize the MRR
machining productivity while ensuring that the value of surface roughness Ra does
not exceed the limitation by using Chemical Reaction Optimization(CRO).