Cut Quality Estimation in Industrial Laser Cutting Machines: A Machine Learning Approach

Abstract

The use of machine learning models to improve industrial production quality is becoming more popular year after year. The main reason is the huge data availability and the impressive boost of performance of such methods achieved in the last decade. In this work we propose an adaptation of three well known machine learning algorithms to estimate the quality of cut in industrial laser cutting machines. The challenge here is to use a pool of multimodal parameters coming from different sensors and fuse them in order to detect the cutting status of the machine in a near-online modality. We analyze then generative and discriminative approaches based on Gaussian Mixture Models, Recurrent Neural Networks, and Convolutional Neural Networks in a supervised setting. Results are computed on a brand-new dataset that is freely available for reference.