NDLI: An ANN Approach on the Optimization of the Cutting Parameters During CNC Plasma-Arc Cutting

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Kechagias, John Pappas, Menelaos Karagiannis, Stefanos Petropoulos, George Iakovakis, Vassilis Maropoulos, Stergios
Copyright Year	2010
Abstract	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.
Sponsorship	International
Starting Page	643
Ending Page	649
Page Count	7
File Format	PDF
ISBN	9780791849187
DOI	10.1115/ESDA2010-24225
e-ISBN	9780791838778
Volume Number	ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 4
Conference Proceedings	ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis
Language	English
Publisher Date	2010-07-12
Publisher Place	Istanbul, Turkey
Access Restriction	Subscribed
Subject Keyword	Amperes Design Cutting Computer numerical control machine tools Artificial neural networks Machining Feedforward control Plates (structures) Steel Pressure Electric arcs Optimization
Content Type	Text
Resource Type	Article

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