Highly flexible material synthesis and microstructure adjustment through combined laser deposition welding and short-term heat treatment for high-throughput materials development

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High-throughput materials development requires short time methods for the synthesis, characterization, data analysis and modelling of thousands of samples. Structural materials are defined by their composition and especially their microstructure, originating from the specific thermo-mechanical history of the parts during their manufacturing In this project, we propose to acquire, use and expand a unique, custom-made laser metal deposition (LMD) equipment for the synthesis of many, discrete samples of different compositions subjected to customized heat treatment conditions. The proposed projects will address the entirety of the LMD process chain, integrate physical-based as well as data-based modelling of crucial steps, and develop new solutions for the in-process monitoring of the materials transformations during fabrication.Dynamic blending of powders will be evaluated carefully regarding reproducibility and reliability. Compositions will be tuned “on demand” by mixing of up to six powders or powder blends, including both metal alloys and pre-synthesised carbo-nitride particles, at adjustable flow rates. In this way, we will produce novel and project-specific alloy powders libraries. The content of micro alloying elements will be adjusted while maintaining powder flow rates at a conventional level. The application of pre-alloyed powders will allow the use of high-melting elements which are difficult to homogenize in solution during welding. Also, we aim to achieve an accelerated identification of process parameters during the blending process of different compositions. In-situ process characterization by means of high-speed recordings and pyrometry/thermography will be implemented from the project start and progressively extended with further sensing capabilities.After synthesis, the search domain for novel materials will be extended by heat treatment using both a second laser and an inductive heating and active cooling or quenching for adjusting the thermal history and microstructure of the samples. The synthesis of many discrete samples will allow for an independent thermal history of each sample. The high-throughput approach will be validated by manufacturing larger, conventional materials samples for well-established machining, heat treatment and mechanical testing. This high-throughput method will be supported by the computationally prediction of LMD process routes and the acquired data evaluated by automated, machine-learning algorithms enabling a rapid evaluation of process-structure-property relationships in high throughput design. The here obtained large datasets will be rationally handed by means of structured, sample-oriented data storage concepts. Finally, the synthesis process will be evaluated regarding the high reproducibility of samples, flexibility of composition and heat treatment, accordance of aimed and achieved sample characteristics, and sample suitability for short-time characterization and prediction of bulk properties.