Fuzzy rule-based to predict the minimum surface roughness in the laser assisted machining (LAM)

Abstract

Laser Assisted Machining (LAM) has been used to predict the surface roughness of Aluminium Oxide (Al2O3) workpiece using solid neodymium-doped yttrium aluminum garnet (Nd:YAG) laser cutting. The rule-based reasoning and fuzzy logic are used to develop a model to predict the surface roughness values. The process parameters considered in this study are depth of cut, rotational speed, feed, and pulsed frequency, each has three linguistic values. The fuzzy rule-based model is developed using MATLAB fuzzy logic toolbox. Nine IF-THEN rules are created for model development. The relationship between experimental results, predicted results of the proposed model and statistical results gave a good agreement with the correlation 0.994. The differences between experimental results and predicted results have been proven with estimation error value 0.072.The findings indicate that the best predicted value is located at combination 0.2mm (depth of cut), 1500rpm (rotational speed), 0.02mm/rev (feed) and 40 (pulsed frequency). © 2014 Springer-Verlag Berlin Heidelberg.