Application of PSO-based ANN in Knowledge Acquisition for the Selection of Optimal Milling Parameters

In NC milling processes, optimal cutting parameters have a great influence on reducing the production cost and time and improving the product quality. In this paper, a particle swarm optimization (PSO) based artificial neural network (ANN) is proposed to obtain optimal milling-parameter matching knowledge. An optimization model is established to gain patterns for training the network. In order to improve training performance, a new method is introduced to optimize the network´s structure at the same time as evolutionary computation. To demonstrate the procedure and performance of the proposed approach, an illustration is discussed in detail. The result shows that the modified PSO-trained ANN has a better knowledge-recovering performance than conventional PSO-based ANN, BP ANN and genetic algorithm-based ANN. The developed approach can found a base for realization of knowledge acquisition in building milling-parameter selection expert system