Optimization of Burr size, Surface Roughness and Circularity Deviation during Drilling of Al 6061 using Taguchi Design Method and Artificial Neural Network

• Autor(a) principal: Sreenivasulu, Reddy
• Data de Publicação: 2015
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Independent Journal of Management & Production
• Texto Completo: http://www.ijmp.jor.br/index.php/ijmp/article/view/254
• topic: Al 6061 Alloy; Drilling; Taguchi Design method; S/N ratio; ANOVA; Artificial Neural Network

This paper presents the influence of cutting parameters like cutting speed, feed rate, drill diameter, point angle and clearance angle on the burr size, surface roughness and circularity deviation of Al 6061 during drilling on CNC vertical machining center. A plan of experiments based on Taguchi technique has been used to acquire the data. An orthogonal array, signal to noise (S/N) ratio and analysis of variance (ANOVA) are employed to investigate machining characteristics of Al 6061 using HSS twist drill bits of variable tool geometry and maintain constant helix angle of 45 degrees. Confirmation tests have been carried out to predict the optimal setting of process parameters to validate the used approach, obtained the values of 0.2618mm, 0.1821mm, 3.7451µm, 0.0676mm for burr height, burr thickness, surface roughness and circularity deviation respectively. Finally, artificial neural network has been applied to compare the predicted values with the experimental values, good agreement was shown between the predictive model results and the experimental measurements.