Utilizing Big Data Informatics for Thermal Spray Materials Design

Abstract Utilizing big data to govern decisions is becoming increasingly valuable and the thermal spray process is no exception. The thermal spray process is unique as a material process in its capability to employ a wide range of advanced materials technologies: metals, ceramics, cermets, and oxides among others. Like any process, the thermal spray technology is most effective when utilizing material feedstock which is specifically designed for thermal spray. This paper will discuss how big data techniques can be employed to design disruptive materials technology. The thermal spray process presents unique challenges to modelling and simulation work due to the inherent complexity of the process. However, these challenges offer the opportunity to develop materials tailored for specific thermal spray processes to yield improved coating performance. Furthermore, big data material informatics can significantly accelerate the discovery of new alloy solutions. More than 100 years of experimental research underpins the science employed, but modern computational tools and materials informatics principles enable new decision strategies to be utilized. The big data approach relies on calculations which predict the microstructure of millions of alloy compositions and utilizing proprietary data mining algorithms to identify unique materials spaces which would never be discovered experimentally.