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

高通量材料开发需要短时间的方法来合成，表征，数据分析和数千个样品的建模。结构材料由其组成，特别是其微观结构定义，源于其制造过程中零件的特定热机械历史在该项目中，我们建议获得，使用和扩展一种独特的定制激光金属沉积（LMD）设备，用于合成许多不同成分的离散样品，并进行定制的热处理条件。拟议项目将涉及整个LMD工艺链，整合关键步骤的物理和数据建模，并开发新的解决方案，用于在制造过程中监测材料转变。粉末的动态混合将在再现性和可靠性方面进行仔细评估。通过混合多达六种粉末或粉末混合物，包括金属合金和预合成的碳氮化物颗粒，以可调节的流速，将“按需”调整成分。通过这种方式，我们将生产出新颖的、针对特定项目的合金粉末库。微合金化元素的含量将被调整，同时将粉末流速保持在常规水平。预合金粉末的应用将允许使用在焊接过程中难以在溶液中均匀化的高熔点元素。此外，我们的目标是在不同组合物的混合过程中实现工艺参数的加速识别。通过高速记录和高温测量/热成像技术进行的现场工艺表征将从项目开始实施，并逐步扩展到进一步的传感能力。合成后，新材料的搜索领域将通过使用第二激光和感应加热的热处理以及主动冷却或淬火来调整样品的热历史和微观结构。许多离散样品的合成将允许每个样品的独立热历史。高通量方法将通过制造更大的常规材料样品进行验证，以进行完善的加工，热处理和机械测试。这种高通量方法将得到LMD工艺路线的计算预测和自动化机器学习算法评估的采集数据的支持，从而能够快速评估高通量设计中的工艺-结构-性能关系。通过结构化的、面向样本的数据存储概念，这里获得的大型数据集将被合理地处理。最后，将对合成工艺进行评价，包括样品的高重现性、组成和热处理的灵活性、目标和实现的样品特性的一致性以及样品对短期表征和本体性质预测的适用性。

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.