Modelling the surface roughness of steel after laser hardening by using 2D visibility network, convolutional neural networks and genetic programming

The surface characterization of materials after Robot Laser Hardening (RLH) is a technically demanding procedure. RLH commonly used to harden parts, especially when subject wear. By changing their properties, this treatment can offer several benefits such as lower costs for additional machining, no use cooling agents or chemicals, high flexibility, local hardening, minimal deformation, accuracy, and automated integrated process in the production process. However, roughness strongly depends on heat parameters This article network theory approach (i.e., visibility 2D space) analyze tool steel EN100083-1 upon RLH. Specifically, two intelligent methods were merged investigation. Firstly, genetic algorithm was applied derive relationship between robot laser cell topological properties. Furthermore, convolutional neural networks allowed assessment based photographic images.