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 (R 2), 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: R 2 > 99%, MSE < 0.3%, and APE < 6%.
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.
During machining processes, cutting temperature directly affects cutting performances, such as surface quality, dimensional precision, tool life, etc. Thus, evaluation of cutting temperature rise in the tool–chip interface by reliable techniques can lead to improved cutting performances. In this paper, we present the modeling of cutting temperature during facing process by using time series approach. The experimental data were collected by conducting facing experiments on a Computer Numerical Control lathe and by measuring the cutting temperature by an infrared camera. The collected data were used to test several Autoregressive Integrated Moving Average (ARIMA) models by using Box–Jenkins time series procedure. Then, the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 1, 1) and it was tested by conducting a new facing operation under the same cutting conditions (spindle speed, feed rate, depth of cut, and nose radius). It was clearly seen that there is a good agreement between experimental and simulated temperatures, which reveals that this approach simulates the evolution of cutting temperature in facing process with high accuracy (average percentage error [Formula: see text] 0.57%).
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