Computational Analysis and Artificial Neural Network Optimization of Dry Turning Parameters—AA2024-T351

Abstract

In dry turning operation, various parameters influence the cutting force and contribute in machining precision. Generally, the numerical cutting models are adopted to establish the optimum cutting parameters and results are substantiated with the experimental findings. In this paper, the optimal turning parameters of AA2024-T351 alloy are determined through Abaqus/Explicit numerical cutting simulations by employing the Johnson-Cook thermo-viscoplastic-damage material model. Turning simulations were verified with published experimental data. Considering the constrained and nonlinear optimization problem, the artificial neural networks (ANN) were executed for training, testing, and performance evaluation of the numerical simulations data. Two feedforward backpropagation neural networks were developed with ten hidden neutrons in each hidden layer. The Log-Sigmoid transfer function and the Levenberg-Marquardt algorithm were applied in the model. The ANN models were studied with four input parameters: the cutting speed (200, 400, and 800 m/min), tool rake angle (5 • , 10 • , 14.8 • , and 17.5 •), cutting feed (0.3 and 0.4 mm), and the contact friction coefficients (0.1 and 0.15).The two target parameters include the tool-chip interface temperature and the cutting reaction force. The performance of the trained data was evaluated using root-mean-square error and correlation coefficients. The ANN predicted values were compared both with the Abaqus simulations and the published experimental findings. All of the results are found in good approximation to each other. The performance of the ANN models demonstrated the fidelity of solving and predicting the optimum process parameters.

Effects of Tool Edge Geometry on Chip Segmentation and Exit Burr: A Finite Element Approach

“…The developed regression model (Equation 14using Minitab software (Minitab, 16.2, Minitab-LLC, State College, PA, USA, 2010). The predicted value for burr length (for optimal cutting parameters: V C = 800 m/min, f = 0.3 mm/rev, r eq = 5 µm) to generate minimum burr using Equation (14) matches the value acquired through finite element simulation (Table 6).…”

“…All of this necessitates the optimization of cutting parameters, tool materials, and angles and edge geometries to improve machined component quality, improve tool life, and eventually increase productivity. Worthy analytical, experimental, and numerical efforts have been carried out in this context to comprehend the chip formation process [8][9][10][11][12] and optimize cutting parameters to control surface quality and residual stresses [13][14][15]. Most recently, an integrated finite element and finite volume numerical model was presented by Hegab et al [16] to analyze nano-additive-based minimum quantity lubrication (MQL) effects on machining forces, temperatures, and residual stresses.…”

Finite Element Analysis and Statistical Optimization of End-Burr in Turning AA2024

“…To model the material's behavior, Johnson-cook thermo-elasto-visco-plastic model, Equation (1), is used [26]. Promising results [7,12] show the effectiveness of the chosen stress model.…”

“…Researchers have also used heuristic optimization techniques to optimize various machining parameters like cutting speed, feed rate, depth of cut, tool angles, tool nose radius, etc., to minimize burr formation. Saleem et al [12] have used artificial neural network (ANN) and Dong et al [13] have used Taguchi's design of experiment (DOE) techniques to minimize burr size. Response surface methodology (RSM) and analysis of variance (ANOVA) have been employed by Niknam and Songmene [14] to perform statistical investigation on burrs thickness during milling of aluminum alloys.…”

A Damage Identification Approach for Offshore Jacket Platforms Using Partial Modal Results and Artificial Neural Networks

“…Padil et al [42] proposed to use nonprobabilistic ANNs to address the problem of uncertainty in vibration damage detection. ANNs were also used in others research areas [43][44][45][46].…”