Optimization of Machining Parameters Based on Principal Component Analysis and Artificial Neural Network

In this paper, a method to optimize the cutting parameters based on the principal component analysis (PCA) and artificial neural network (ANN) is proposed. Experiments have been designed with four input machining process parameters at four different levels. The Taguchi Method is used to conduct the experimental design. The PCA is used to construct the model for characterizing the parameter's impact, with the Grey relational analysis (GRA) applied to search the near optimal combination of the parameters for multiple response characteristics. Furthermore, the artificial neural network (ANN) model is used for prediction of responses as reference. The ranking order for each response by prediction of ANN model is the same as the machining experiments do, which verify the feasibility of the proposed method.