Title: Study on characteristics of AlTiN and TiCN coating layers deposited on carbide cutting tools in hard turning of steel: experimental, simulation and optimisation

Authors: Armansyah Ginting; Che Hassan Che Haron; Issam Bencheikh; Mohammed Nouari

Addresses: Laboratory of Machining Processes, Department of Mechanical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Jalan Almamater, Building J17.01.01, Medan 20155, Indonesia ' Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, National University of Malaysia, 43600 Bangi, Selangor, Malaysia ' LEM3 UMR CNRS 7329, Institut Mines-Telecom GIP-InSIC, University of Lorraine, 27 Rue d'Hellieule, 88100 Saint-Dié-des-Vosges, France ' LEM3 UMR CNRS 7329, Institut Mines-Telecom GIP-InSIC, University of Lorraine, 27 Rue d'Hellieule, 88100 Saint-Dié-des-Vosges, France

Abstract: Objective of the present work is focused to study the characteristics of monolayer PVD-coated carbide AlTiN and TiCN cutting tools. Some features related to machinability such as tool wear, tool life, and surface roughness were adopted to study the tools characteristics. Moreover, effort was also paid to determine the cutting condition for both cutting tools that subjected to another feature, namely volume of material removal (VMR). The results of experiment showed that AlTiN gained higher cutting condition than TiCN due to higher usage temperature of its coating material. However, TiCN produced higher VMR than AlTiN and longer tool life. Flank wear and chipping were observed as the wear modes of both cutting tools. Surface roughness was resulted at the quality of medium finish. The finite element method was utilised to provide an orthogonal cutting simulation for resulting the map of cutting temperature distribution at the tool-chip interface. Finally, multi-objective genetic algorithm optimisation was employed for obtaining the optimum yield of VMR.

Keywords: volume of material removal; VMR; orthogonal cutting; cutting temperature; finite element method; multi-objective genetic algorithm; MOGA.

DOI: 10.1504/IJMMM.2021.112721

International Journal of Machining and Machinability of Materials, 2021 Vol.23 No.1, pp.88 - 112

Received: 29 Oct 2019
Accepted: 08 May 2020

Published online: 12 Jan 2021 *

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