Abstract
In the present study, forward modeling of high-speed finish milling process has been solved using soft computing. Two different approaches, namely neural network (NN) and fuzzy logic (FL), have been developed to solve the said problem. The performance of NN and FL systems depends on the structure (i.e. number of neurons in the hidden layer, transfer functions, connection weights, etc.) and knowledge base (i.e. rule base and data base), respectively. Here, an approach is proposed to optimize the above-mentioned parameters of NN and FL systems. A binary coded genetic algorithm (GA) has been used for the said purpose. Once optimized, the NN and FL-based models will be able to provide optimal machining parameters online. The developed approaches are found to solve the above problem effectively, and the performances of the developed approaches have been compared among themselves and with that of the results of existing literature.
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