Mathematical Modeling using Artificial Neural Networks for Quality Evaluation in the Machining of Fe-Al Alloy with PCBN Tools
Abstract
Machining experiments are conducted on Fe-Al alloy with PCBN inserts of different tool geometry. The corresponding roughness values of the machined surface are measured. Mathematical model is developed using artificial neural networks to study the influence of tool geometry on the surface roughness. The equation thus formulated evaluates the machining performance. It would be useful in the selection of suitable tool geometry to improve the machining quality. The analytical and experimental values of surface roughness are found to be in good order that confirms the fitness of the model. It would be useful in evaluation of the machining quality.
References
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