A comparison between parametric and non-parametric soft computing approaches to model the temperature of a metal cutting tool

Abstract

Modelling the temperature of cutting tools in industry has become a vital assignment for manufacturing process modelling. The quality and the cost of the produced products are significantly affected if the process is inaccurate. However, the process of modelling the temperature is complex because the relationship between the model variables of the temperature is nonlinear. In this article, five soft computing techniques are used to develop mathematical models for two different cutting tools. Genetic Algorithm, Particle Swarm Optimisation and Cuckoo Search are used to estimate the parameters of a common empirical model for the temperature of the cutting tools. This empirical model is also nonlinear model of the parameters. Thus, traditional modelling techniques such as least square estimation will have trouble finding the optimal set of model parameters. The challenging problem which is also tackled in this article is the development of nonlinear model of the cutting tool using Artificial Neural Networks and Multigene Symbolic Regression Genetic Programming (GP). In the case of ANNs, the model structure is hidden inside the network, whereas in the case of Multigene Symbolic Regression GP, the developed model equation shall reveal the relationship between model variables. Obtained models will be validated based on many evaluation criteria.

Keywords:

• genetic algorithm
• particle swarm optimisation
• genetic programming
• cuckoo search
• artificial neural networks
• optimisation
• cutting tools

Notes

1. HeuristicLab is used for GA and PSO optimisation. HeuristicLab is a framework for heuristic and evolutionary algorithms that is developed by the members of the Heuristic and Evolutionary Algorithms Laboratory, http://dev.heuristiclab.com

2. The MATLAB code of Cuckoo Search algorithm provided in (Yang and Deb 2009) was used as an implementation in the experiments.

3. GPTIPS MATLAB toolbox is used for developing Multigene GP model. GPTIPS is an open source genetic programming toolbox for Multigene symbolic regression Searson, Leahy, and Willis (2010a)