Machining parameter optimisation by genetic algorithm and artificial neural network Online publication date: Sat, 26-Jul-2014
by Nafis Ahmad; Tomohisha Tanaka; Yoshio Saito
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 6, No. 3, 2014
Abstract: Machining operations are used for creating surfaces by cutting away unwanted materials from workpieces. These operations are highly constrained and non-linear in nature. As a result traditional techniques are not suitable for machining parameter optimisation. Turning and milling are the two most commonly used machining operations where machining time or cost is minimised by optimising cutting parameters. The important constraints are maximum cutting force, machine power, available rotational speed, tool deflection, required surface finish cusp height etc. Here, a genetic algorithm (GA) and artificial neural network (ANN)-based hybrid approach is presented. The proposed approach gives more emphasis on searching optimum cutting parameters near boundaries of feasible and infeasible solution spaces. The optimum solution obtained by this method also does not violate constraints for a specific machining operation. An example of ball end milling operation is presented to explain this technique.
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