Prediction of multi-response parameters in material removal processes using soft computing — A review

Advances in soft computing reshape the manufacturing industries to develop an integrated, self-adjusting manufacturing systems into dynamically scalable and highly distributed cost-efficient business model. Due to presence of uncertainty and inaccuracy in manufacturing processes, the various soft computing algorithms i.e. neural networks, fuzzy sets, genetic algorithms, ant colony optimization, adaptive neural fuzzy inference system, swarm optimization technique and simulated annealing have been applied for anticipating the performance of the metal cutting processes and optimizing them. The paper presents the state-of-the-art review on the soft computing techniques applied for the prediction of multi-response parameters in material removal processes.