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The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots. [ABSTRACT FROM AUTHOR]

This paper reports the development of two intelligent models for the electric discharge machining (EDM) process using adaptive-neuro-fuzzy-inference system (ANFIS) and genetic programming (GP). Experiments were conducted by varying the pulse duration and discharge current and the corresponding values of surface roughness (Ra) were measured. The values of surface roughness predicted by these models are then compared. Both models show good agreement with experimental results. The results indicate that the genetic programming technique gives slightly smaller deviation of the measured values of model than neuro-fuzzy model. [ABSTRACT FROM AUTHOR]