Intelligent parameters optimization for laser cutting of highly reflective and thermally conductive materials using artificial neural network

Abstract

High energy density laser beam is a best approach to cut difficult- to- laser cut materials. Laser is most suitable to cut highly reflective and thermal conductive materials i.e. Aluminium and its alloy which is difficult to laser cut material. The optical and thermal characteristics of material are the main proficiency of laser beam machining. The industries as automobiles and aircraft which need complicated design or difficult portrait forward with inflexible model condition adopt that types of material extensively. The quality of laser cutting mainly possible with the appropriate selection of input process parameters. For optimal setting of process parameters, the effect of their variation on different quality characteristics of interest is required to be investigated. In this paper the simulation test results show that the simulated values of material removal rate (MRR) using neural network model are quite close to the values calculated with theoretical formulae. To express the relation between cutting quality and cutting specification the artificial neural network (ANN) approach is established for vertical laser cutting position. For optimizing the parameters, concluding cutting results and surmising new cutting information the artificial neural network (ANN) provides outstanding outcomes.