A comparison between parametric and non-parametric soft computing approaches to model the temperature of a metal cutting tool. Issue 1 (2nd January 2016) - Faris, Hossam; Sheta, Alaa
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.