earticle

영어

Determining the temperature of the cutting tool is important due to its influence on the quality and cost of the production process. However, the process of modeling the temperature of the tool is a complex process; this due to the nonlinear relationship between the system variables. In this paper, a symbolic regression approach via genetic programming (GP) is used to model a cutting machine temperature and compared to other approaches which based on estimating the parameters of the nonlinear regressive curve of the cutting tool. The developed GP model shows an promising results compared to models developed based on parameter estimation such as Least Squares regression (LS) , Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).