Impact of Cutting Parameters on Surface Roughness in Milling Aluminum Alloy 6061 Using ANN Models
The surface roughness is difficult to estimate in machining, especially for weak stiffness workpiece. So, prediction model of surface roughness using artificial neural network (ANN) is developed. This model investigates the effects of cutting parameters during milling Aluminum alloy 6061. The experiments are planned with four factors and four levels for developing the knowledge base for ANN training. Three-dimensional surface plots are generated using ANN model to study the effects of cutting parameters on surface roughness. The analysis reveals that cutting speed and feed rate have significant effects in reducing the surface roughness, while the axial and radial depth of cut has less effect. And the variations of surface roughness are highly non-linear with the cutting parameters.
Helen Zhang and David Jin
Y. M. Liu et al., "Impact of Cutting Parameters on Surface Roughness in Milling Aluminum Alloy 6061 Using ANN Models", Applied Mechanics and Materials, Vols. 63-64, pp. 412-415, 2011