Modeling of Cutting Performances in Turning Process Using Multiple Regression Method

Abstract

This paper presents the modeling of cutting performances in turning of 2017A aluminium alloy at four turning parameters: cutting speed, feed rate, depth of cut, and tool nose radius. These performances include: surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a Computer Numerically Controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop multiple regression models for the pre-cited cutting performances and investigate the effects of turning parameters and their interactions on responses. To evaluate the accuracy of the developed models, two performance criteria were used: Correlation Coefficient (R²) and Average Percentage Error (APE). It was clearly seen that the multiple regression models estimate the cutting performances with high accuracy: R²>94% and APE<7%. Therefore, this method is an effective tool for modeling the cutting performances in turning process.

Investigation of Surface Roughness and Resulting Cutting Force for Dry Turning Operation of Different Types of Material Using CVD Coating Carbide Tool

“…This is due to its high performance in terms of dimension accuracy, time saved, cost saved and a high-quality surface. In particular, it can be used to replace the grinding process for surface finishing [1][2][3][4]. SCM420, SCM420H, SCM440 and SM45C steels are widely used in automobile, aircraft and machine structures (gear, shaft, etc.)…”