Prediction of quality characteristics of micro-channels by deep learning algorithm during laser transmission ablation of PMMA

Abstract

Underwater laser transmission micro-channelling of thick transparent polymethyl methacrylate (PMMA) using a neodymium-doped yttrium aluminium garnet (Nd: YAG) laser is the focus of this study. Experiments are conducted to determine the effect of three significant variables, namely pulse frequency, lamp current, and cutting speed, on heat-affected zone (HAZ) width, cut depth, and kerf width. Experiments are designed systematically utilising the full factorial design method. The purpose of constructing empirical models, specifically non-linear ones, is to establish and clarify the relationship between control factors and corresponding responses. A feed forward backpropagation neural network (FF-BPNN) is employed for predicting the measured response. Additionally, an assessment is made between the FF-BPNN results and the values predicted by the model. On the basis of the anticipated results, it can be concluded that the FF-BPNN model is preferable in predicting both the depth of cut and kerf width. In contrast, when specifically contemplating the measurement of HAZ width, the application of a non-linear model is more advantageous. As a measure of accuracy, the mean absolute error is computed for both the feedforward backpropagation neural network (FF-BPNN) and the nonlinear modelling technique.