Application of Machine Learning to Spattering Phenomena in Laser Cutting

Abstract

Reducing spatter, i.e., melt droplets flown out of the melt pool, is one of the critical issues when laser cutting is employed as a machining tool for radioactive wastes because the ejected droplets can lead to radioactive contamination with potential human exposure. The spattering phenomena are complicated processes that involve multiple physical phenomena, causing difficulty in the determination of laser parameters to minimize the amount of spatter. Here we observe the spatter ejected from 316L stainless steel plates using a high-speed camera and apply a machine learning technique to these captured images on the basis of three distinctive behaviors appeared at specific time intervals of the process of spattering phenomena: (I) a vapor, (II) a liquid film and breakup into droplets, and (III) a liquid capillary. The numerical model established through the machine learning technique predicts the spattering phenomena with an accuracy of 89% and can be used to determine the laser power and beam diameter that reduce the spatter eruption during laser irradiation.