Book Description

Modern production is faced with the challenges in reducing the environmental impacts related to machining processes. Turning process is a manufacturing process widely used with a vast application for creating engineering components. In this context, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This paper considers the quality of the products (surface finish) and the productivity rate of the turning manufacturing process to be both optimized. Product quality is quantified using surface roughness (R_a) and the productivity rate using material removal rate (MRR). We develop a predictive and optimization model by coupling artificial neural networks (ANN) and the Particle Swarm Optimization (PSO), a multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). To obtain the data, Aluminum is used to perform the turning process experiments, considering the cutting speed, feed rate, depth of cut and nose radius of the cutting tool as our design factors. We used the gathered data to train and develop the ANN model. The results predicted by the proposed models indicate good agreement between the predicted and experimental values, proving that the proposed ANN model is capable of predicting the surface roughness accurately. Then, the optimization model PSO has provided a Pareto Front for the optimal solution, determining the optimum machining parameters for minimum R_a and maximum MRR. This study has application in the real industry where the selection of optimal machining parameters helps to complete and manage conflicting objectives that constitute hurdles in the decision-making of the manufacturing plans.