Digital transformation of thermal and cold spray processes with emphasis on machine learning

Abstract

Thermal spray technologies continuously evolve to meet new challenges arising from current and future market needs and requirements. This evolution has been well documented throughout the years with regards to equipment, processes and materials but the concurrent digital transformation of the sector is happening in a fragmented manner. The first objective of this article is to review the readily available digital tools and methods for coating deposition optimisation, equipment health monitoring, process control and metallographic analysis. The second objective is to identify the key challenges faced by industrialists and to provide some guidance and recommendations for the research and development required to meet these challenges. The third objective is to assess the performance of the most promising Machine Learning (ML) methods, as a key enabling technology, for thermal and cold spray. Novel approaches from different engineering disciplines, currently unexplored in the field of surface engineering, are discussed and several adoption strategies are proposed. Machine learning validation examples with their implementation codes are presented in this work in an effort to motivate new research that would eventually accelerate the digital transformation of the surface engineering sector. © 2022 Elsevier B.V.