On-line monitoring, neural networks-based modelling, and reinforcement learning control of laser remelting process
Abstract
Laser polishing (LP) is a novel advanced surface finishing process aimed to address major shortcomings of traditional polishing processes used in the tool and die industry. In LP, a concentrated energy source (laser) is used to create a localized melt pool allowing material redistribution under surface tension and various thermodynamic phenomena. The faster speed, higher areal locating precision, and the lack of consumable tooling for surface polishing by laser remelting (SP-LRM) process makes this technology highly attractive. The primary factor limiting wide-range industrial use of the LRM is complex process parameter optimization and process instabilities. In this work, reinforcement learning is applied to achieve in-situ monitoring and control of the melt pool stability using co-axially mounted high-speed near-infrared (NIR) camera and dynamically varying the laser power. The controller autonomously achieved a 20% reduction of standard deviation value of melt pool intensity over time and a 70% reduction in resultant surface roughness (Sa) variations.