The objective of the present study is to develop an Artificial Neural Network (ANN) in order to predict the bevel angle (response variable) during CNC plasma-arc cutting of St37 mild steel plates. The four (4) input parameters (plate thickness, cutting speed, arc ampere, and torch standoff distance) of the ANN was selected following the results (relative importance) of the Analysis Of Variance (ANOVA) performed based on seven (7) factors (plate thickness, cutting speed, arc ampere, arc voltage, air pressure, pierce height, and torch standoff distance) in a previous study. A multi-parameter optimization was carried out using the robust design. An L18 (21 × 37) Taguchi orthogonal array experiment was conducted and the right bevel angle was measured, aiming at the investigation of the influence of plasma-arc cut process parameters on right side bevel angle of St37 mild steel cut surface. The selection of quality characteristics, material, plate thickness and other process parameter levels and experimental limits was based on the experience and current needs of the Greek machining industry. A feed-forward backpropagation (FFBP) neural network was fitted on the experimental data. The results show that accurate predictions of the bevel angle can be achieved inside the experimental region, through the trained FFBP-ANN. The developed ANN model could be further used for the optimization of the cutting parameters during CNC plasma-arc cutting.
An ANN Approach on the Optimization of the Cutting Parameters During CNC Plasma-Arc Cutting
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Kechagias, J, Pappas, M, Karagiannis, S, Petropoulos, G, Iakovakis, V, & Maropoulos, S. "An ANN Approach on the Optimization of the Cutting Parameters During CNC Plasma-Arc Cutting." Proceedings of the ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 4. Istanbul, Turkey. July 12–14, 2010. pp. 643-649. ASME. https://doi.org/10.1115/ESDA2010-24225
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