Int. J. of Data Analysis Techniques and Strategies   »   2014 Vol.6, No.3

 

 

Title: Machining parameter optimisation by genetic algorithm and artificial neural network

 

Authors: Nafis Ahmad; Tomohisha Tanaka; Yoshio Saito

 

Addresses:
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
Department of Mechanical and Control Engineering, Tokyo Institute of Technology, Tokyo 152-8550, Japan
Department of Mechanical and Control Engineering, Tokyo Institute of Technology, Tokyo 152-8550, Japan

 

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.

 

Keywords: machining parameters; cutting parameters; ball end milling; genetic algorithms; GAs; artificial neural networks; ANNs; parameter optimisation.

 

DOI: 10.1504/IJDATS.2014.063061

 

Int. J. of Data Analysis Techniques and Strategies, 2014 Vol.6, No.3, pp.261 - 274

 

Available online: 25 Jun 2014

 

 

Editors Full text accessAccess for SubscribersPurchase this articleComment on this article