Beyond Fingerprinting: Process Exploration and Optimization via High-Throughput and Machine Learning

Material properties are governed by composition and associated microstructure dictated by the thermodynamics and kinetics of manufacturing processes. Often, the connectivity between process conditions and the resulting structure and properties is complex, evading full predictivity via high-fidelity modeling. In this work, we are exploring three manufacturing processes where material properties are difficult to predict directly from process settings: electroplating, physical vapor deposition, and additive manufacturing (laser powder bed fusion). Each of the three processes offer unique challenges and opportunities. Across these three exemplars, we are augmenting traditional process-structure-property investigations with an accelerated workflow to detect material structure/composition, prognose associated properties, and adapt the associated process to achieve improved product outcomes. This accelerated detect-prognose-adapt cycle is aided by three key elements: (1) automated combinatorial synthesis to enable rapid parameter sweeps, (2) high-throughput evaluation of both conventional and surrogate indicators of material chemistry, structure, and properties, and (3) machine learning algorithms to unravel correlations in high-dimensional spaces beyond expert cognition. In each of these three domains, we take advantage of previously developed capabilities, or where such capabilities are insufficient, we develop novel techniques. For example, in the domain of electroplating synthesis, we have employed an existing robotic pipetting system for formulation of solution chemistries while developing a custom 12-cell parallel electroplating system that enables hundreds of unique conditions to be explored in about a day. While we consider purely data-driven ML algorithms for some correlation analysis, a more interpretable and robust solution includes physical models based on established governing equations. In this regard, we have developed a physics-informed multimodal autoencoder that fuses data from multiple modalities alongside physical models to provide a deeper fingerprint of material state, enabling unsupervised detection of high-dimensional clusters and cross-modal correlations. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.