Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Artificial Neural Networks
A finite element model of exit burrs for drilling of metals
Finite Elements in Analysis and Design
Design and Analysis of Experiments
Design and Analysis of Experiments
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
Journal of Intelligent Manufacturing
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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.