Protective coatings against wear and corrosion play a critical role in many industrial fields. However, extant technologies have considerable sustainability drawbacks. A prime example is electroplated Cr, a safe material whose deposition however involves highly toxic and carcinogenic Cr6+ compounds included in the REACH list of Substances of Very High Concern (Annex XIV). Thermal spray processes are intrinsically “cleaner”: they do not employ baths with large amounts of chemicals. However, they also use materials like WC-Co-based hardmetals and Stellite alloys that contain carcinogenic elements (Co) and/or critical raw materials (CRMs) – Co, W.
Therefore, more sustainable coating formulations are needed, while still exploiting the versatility and low environmental impact of thermal spray. However, almost one century of efforts in this respect have been met with scarce success. Therefore, CoBRAIN aims to harness the as-yet largely unexplored compositional space of High-Entropy and Multi-Principal Component Alloys (HEAs, MPEAs) to devise novel compositions. The HEA/MPEA concept offers unique features, precluded to conventional alloys based on only one main element. Further, the sheer vastity of the HEA/MPEA compositional space offers more latitude to optimise the coating properties.
The CoBRAIN project specifically seeks to develop HEAs/MPEAs with “simple” metallic elements like Cr, Mn, Fe, Ni, Cu, Al, Ti, Si, to minimize the amount of expensive and/or critical materials. Some of these elements are also considered critical (e.g. Mn) or strategic (e.g. Ni) by the EU, but their criticality is less significant than Co and W, and in a HEA none of them will ever be the sole main constituent, thus reducing the alloy's sensitivity to the criticality of one constituent. Hardmetals are formulated by adding TiC, a hard phase obtained from much more widely available elements than WC.
The number of possible combinations in the HEA/MPEA space is extremely wide and cannot be probed by experiments alone; even physical models would not suffice. Therefore, CoBRAIN uses high-throughput physical modelling to create databases onto which machine learning (ML) is trained (Figure 1: WP2), with validation from experimental testing (Figure 1: WP3). In particular, after creating a first iteration of the ML model, its “weak areas” are identified and explored systematically with further experimental activities (Figure 1: WP4). Integration of data(and metadata) from multiple sources is enabled by creating a semantic representation through an overarching ontology of thermal spraying (Figure 1: WP1).
The final ML model is integrated, together with a life cycle performance analysis (LCPA) module, into a sustainable decision support system (SDSS) - Figure 1: WP5. The SDSS system and its outputs will be tested in real case-studies (Figure 1: WP6).
