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.

Thermal spray materials used in power generation applications are required to function within a challenging array of requirements. The coatings must be applied over typically large surface areas cost effectively, the coating must be resistant to extreme erosion and/or corrosion, the coatings must function at high temperatures and, if possible, the thickness of the coatings should be readable with standard equipment such as an Elcometer gauge. Simultaneously meeting all these requirements and advancing the alloy technology is a daunting if not impossible task if done via experiments alone or through scientific intuition. However, the design of new alloys which must possess a variety of attributes simultaneously is well suited for big data techniques. The calculation of millions of alloys and advanced data mining techniques help to quickly identify the best alloy for the application. This paper details how this process was used to design Metco 8294, a proprietary alloy. Metco 8294 is unique in that it is a Fe-based alloy of high hardness, coating adhesion, and erosion resistance, and is readable in the as-sprayed condition and after high temperature exposure.

Chromium containing metals are an industry staple due to unparalleled corrosion resistance and durability. Unfortunately, many if not all Cr-containing alloys can produce Hexavalent Chromium (Cr 6+ ), a known carcinogen. Studies conducted by the California Air Resources Board (CARB) showed that 30-60 percent of chromium emissions produced in thermal spray processing can contain Cr 6+ . In addition, several independent studies found that Cr 6+ emissions produced from Twin Wire Arc Spray (TWAS) can be up to 3,000 times greater than the legal limits established by the CARB. This study details efforts to develop the next generation of high performance thermal spray alloys which are Chromium free, thereby resulting in zero Cr 6+ emissions. In order to meet these objectives high throughput computational metallurgy was employed. The initial results have shown that next generation alloys can be developed to meet or exceed the performance of incumbent Cr-bearing alloys currently in service, and that the future of Cr-free alloys is on the horizon.