Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique

Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R-2 = 0.9653). The worst prediction was observed for dross area per unit length (R-2 = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter. (C) 2016 Elsevier B.V. All rights reserved.