Genetically evolved radial basis function network based prediction of drill flank wear

The most important factor that governs the performance of a radial basis function network (RBFN) is the optimization of the network architecture, i.e. determining the exact number of radial basis functions (RBFs) in the hidden layer that can best minimize the error between the actual and network outputs. This work presents a genetic algorithm (GA) based evolution of optimal RBFN architecture and compares its performance with the conventional RBFN training procedure employing a two stage methodology, i.e. utilizing the k-means clustering algorithm for the unsupervised training in the first stage, and using linear supervised techniques for subsequent error minimization in the second stage. The validation of the proposed methodology is carried out for the prediction of flank wear in the drilling process following a series of experiments involving high speed steel (HSS) drills for drilling holes on mild-steel workpieces. The genetically grown RBFN not only provides an improved network performance, it is also computationally efficient as it eliminates the need for the error minimization routine in the second stage training of RBFN.