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.
Highlights
The aluminum alloys have gained the prime significance in diverse engineering applications.Machining characteristics of these alloys depend upon the appropriate cutting parameters, such as the cutting speed, cutting feed, cutting tool geometry, clamping scheme, and the tool wear rate
Reliable simulation data can further be optimized by employing some heuristic optimization technique. This concept is implemented in this research by performing the numerical cutting simulations through Abaqus/Explicit (Abaqus, 6.16, Dassault Systemes, Johnston, RI, USA, 2016)
The work piece was developed into three sections: the chipped off material, the damage zone at tool-chip interface, and the uncut material
Summary
The aluminum alloys have gained the prime significance in diverse engineering applications. Dahlman et al [6] investigated the effect of tool rake angle on material residual stresses during the turning process. Studied the optimized turning parameters by integrating the genetic algorithm with support vector regression and the artificial neural networks. Bruni et al [19] worked on the surface roughness modeling of finish face milling under dry cutting conditions by applying the artificial neural networks. Researchers have identified the efficient use of ANN models in other metal machining processes; for example, the prediction of cutting forces, machining vibrations, tool wear rate [20], milling and drilling [21,22,23], the skin pass rolling [24], etc. The architecture of neural networks (ANN) consist of three discrete layers: the input layer, data processing or hidden layers and the output layer. One hidden layer is used for the networks that involve some approximate functions [41] and two layers are used for the networks involving some learning functions
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