Minimizing burr size in drilling using artificial neural network (ANN)-particle swarm optimization (PSO) approach
The burrs at the hole exit degrade the performance in precision part and affect the reliability of the product. Hence, it is essential to select the optimal process parameters for minimal burr size at the manufacturing stage so as to reduce the deburring cost and time. This paper illustrates the application of particle swarm optimization (PSO) to select the best combination values of feed and point angle for a specified drill diameter in order to simultaneously minimize burr height and burr thickness during drilling of AISI 316L stainless steel. The burr size models required for the PSO optimization were developed using artificial neural network (ANN) with the drilling experiments planned as per full factorial design (FFD). The PSO optimization results clearly indicate the importance of larger point angle for bigger drill diameter values in controlling the burr size.
burr size drilling particle swarm optimization (pso) artificial neural network (ann) fitness function