Since its inception, polishing and structuring by laser remelting (P-LRM and S-LRM) technologies have been receiving an increasing attention as a plausible alternative to the conventional surface engineering processes. LRM is a “green” process that does not involve adding or removing material as surface geometry is formed by redistributing workpiece material in a molten state by a moving laser beam. It is a highly complex, non-linear thermodynamic process where material rapid melting, reallocation, and rapid solidification is controlled by the parameters of the applied continuous wave laser irradiation. Considering a wide range of potential applications of the P-LRM and S-LRM in automotive, aerospace, and optical industries, the Automotive and Surface Transportation Research Center of the National Research Council (NRC), Canada has been actively engaged in the development of the LRM technology and its applications in polishing and structuring of tooling and functional surfaces. Building on this activity, the main objective of the present report is to introduce some of the achievements and developments of LRM technology at NRC over the past five years.

This presentation will focus on detailed description of the LRM process, its advantages and disadvantages, and examples of technical implementations for polishing tooling and structuring functional surfaces, e.g. for controlled surface quality, wettability, friction, adhesion, drag, and hydro-/aerodynamics. Then phenomenological understanding the LRM as a statistical thermodynamic process and modelled as a statistical digital twin will be represented by a thermodynamic transfer function with an associated thermophysical model of the rapid melting-solidification of H13 tool steel by continuous wave laser irradiation. In addition, examples of implementation of AI methods (e.g., feed-forward, recurrent, and convolutional neural networks) will be demonstrated using built-in sensing capabilities of the laser processing system (e.g. high-speed thermographic imaging) towards the further additions of machine learning for multi-objective self-optimization, predictive control, and other aspects of the smart laser-based Industry 4.0 manufacturing. Special attention will be placed on technical applications of the LRM process in manufacturing tooling, molds and dies, additive manufactured parts, optics, and others. The presentation will conclude with an outlook on the future of the LRM technology, and technical and knowledge gaps that still need to be filled.