Multi-objective Optimization of Hard Milling Using Taguchi Based Grey Relational Analysis

• Published in: Tehnički vjesnik Vol. 27; no. 2; pp. 513 - 519
• Main Authors: Cica, Djordje, Caliskan, Halil, Panjan, Peter, Kramar, Davorin
• Format: Journal Article Paper
• Language: English
• Published: Slavonski Baod University of Osijek 01.04.2020; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek; Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
• Subjects: Analysis; Cold work tool steels; Cutting energy; Cutting parameters; Cutting speed; Design of experiments; grey relational analysis; hard milling; Legal fees; Machining; Milling (machining); Multiple objective analysis; Optimization; Orthogonal arrays; Principal components analysis; Production management; Setting (hardening); Setting (Literature); Surface properties; Taguchi; Teeth; Tool steel; Variance analysis
• ISSN: 1330-3651; 1848-6339
• DOI: 10.17559/TV-20181013122208

The influence of hard coatings and machining parameters, in particular cutting speed, feed per tooth and depth of cut on specific cutting energy, productivity and surface quality in milling of hardened cold work tool steel, were investigated in this paper. Taguchi's design of experiments was employed for planning of experiments using [L.sub.27] orthogonal array. Optimal setting of machining parameters for multi-objective characteristics was determined using grey relational analysis. The principal component analysis was used to define the corresponding weight factors of each quality characteristics under optimization. Analysis of variance was conducted and it was revealed that feed per tooth is the most significant parameter affecting quality characteristics. Finally, results of confirmation test with the optimal machining parameters settings have shown that the proposed model improves overall performance of hard milling process.