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This paper presents an approach to the determination of the optimal cutting parameters to create minimum surface roughness levels in the face milling of X20Cr13 stainless steel. The proposed approach is to use a particle swarm optimization (PSO)-based neural network to create a predictive model for the surface roughness level that is based on experimental data collected on X20Cr13. The optimization problem is then solved using a PSO-based neural network for optimization system (PSONNOS). A good agreement is observed between the predicted surface roughness values and those obtained in experimental measurements performed using the predicted optimal machine settings. The PSONNOS is compared to the genetic algorithm optimized neural network system (GONNS).

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During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite.