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PREDICTION THE EFFECT OF FLAME CUTTING PARAMETERS ON THE QUALITY OF METAL SURFACE IN CNC FLAME CUTTING MACHINE USING ARTIFICIAL NEURAL NETWORK | ||
| Iraqi journal of mechanical and material engineering | ||
| Article 1, Volume 10, Issue 3, 2010, Pages 406-416 | ||
| Authors | ||
| Dr. Osamah F. Abdulateef; Salah A. Salman; Dr. Faiz F. Mustafa | ||
| Abstract | ||
| Many metal-manufacturing industries include oxyfuel cutting among their manufacturing processes because cutting and welding are often required in metal-cutting processes, specifically in the fabrication of pressure vessels and storage tanks. The oxyfuel cutting process uses controlled chemical reactions to remove preheated metal by rapid oxidation in a stream of pure oxygen. Previous research has demonstrated that metal cutting surfaces varied depending on the gas used for the combustion as well as the cutting speed (Vc) used during the process. In this research, ASTM BN1323 carbon steel was cut using CNC flame cutting machine. The study constrained on the effect of cutting parameters (cutting speed Vc, Preheat time, and plate thickness) on the quality of the metal surface being cut. The Different tests, such as surface roughness and hardness were used to analyze the influence of these parameters. . The effect of cutting parameters on the surface quality was studied by implementing the experimental results obtained from cutting a non-Galvanized steel plate ASTM BN 1323 in different cutting parameters (cutting speed, preheat time, and plate thickness) followed by non-destructive (hardness and roughness of a cutting surface) tests to investigate the quality control on the cut specimens. The results showed, in general, better cut surfaces when using the optimum parameters Vc=300 mm/min. and preheat time =20 sec for cutting 20 mm thickness of non–Galvanized steel sheet ASTM BN1323. The experimental results obtained are then processed through the ANN model to control the cutting process and predict the level of quality for different cutting conditions. It has been deduced that the cutting conditions (cutting speed, preheat time, and plate thickness) had a dominant factors that affected the cut quality. Also we found that for certain cutting condition, there was an optimum cutting speed to obtain an optimum cutting quality. The system supports quality control procedures and cutting productivity without doing more periodic destructive mechanical test to dozens of samples. | ||
| Keywords | ||
| Artificial Neural Network; Flame cutting; oxy; fuel process | ||
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