Modeling pulsed laser micromachining of micro geometries using machine-learning techniques

A wide range of opportunities are emerging in the micro-system technology sector for laser micro-machining systems, because they are capable of processing a various types of materials with micro-scale precision. However, few process datasets and machine learning techniques are optimized for this industrial task. This article aims to show how the process parameters of micro-laser milling influence the final features of the microshapes that are produced and aims to identify, at the same time, the most accurate machine learning technique for the modelization of this multivariable process. We studied the capabilities of laser micromachining by performing experiments on hardened steel with a pulsed Nd:YAG laser. Arrays of micro-channels were manufactured using various scanning speeds, pulse intensities and pulse frequencies. The results are presented in terms of the main industrial requirements for any manufactured good: dimensional accuracy (in our case, depth and width of the channels), surface roughness and material removal rate (which is a measure of the productivity of the process). Different machine learning techniques were then tested on the datasets to try to build high accuracy models for each output variable. The selected techniques were: k-Nearest Neighbours, neural networks, decision trees and linear regression models. Our analysis of the correlation coefficients and the mean absolute error of all the generated models show that neural networks are better at modelling channel width and that decision trees are better at modelling surface roughness; both techniques are similar for depth and material removal rate. In all cases these two techniques are more accurate than the other two. It can be concluded that decision trees can be used for modelling laser micro-machining of micro geometries, if the dimensional accuracy of the workpiece is the main industrial requirement, while neural networks are better in the other cases.