Modeling of cutting performances in turning process using artificial neural networks:

International Journal of Engineering Business Management

SamyaDahbi¹ 

关键词: Turning;    surface roughness;    cutting forces;    cutting temperature;    material removal rate;    cutting power;    specific cutting pressure;    artificial neural networks;   

DOI  :  10.1177/1847979017718988

学科分类：工程和技术（综合）

来源: Sage Journals

【 摘 要 】

In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled 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 an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient (R2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R2 > 99%, MSE < 0.3%, and APE < 6%.

【 授权许可】

CC BY