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

高通量材料的开发需要用短时间的方法对数千个样品进行合成、表征、数据分析和建模。结构材料是由它们的成分，特别是它们的微观结构来定义的，源于零件在制造过程中的特定热机械历史。在这个项目中，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，用于合成符合定制热处理条件的不同成分的许多离散样品。拟议的项目将涉及LMD工艺链的整体，整合关键步骤的基于物理和基于数据的建模，并为制造过程中材料转变的过程监控开发新的解决方案。将仔细评估粉末动态混合的重复性和可靠性。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，以可调的流速“按需”调整成分。通过这种方式，我们将生产出新颖的、特定于项目的合金粉料库。微合金化元素的含量将进行调整，同时将粉末的流动速度保持在常规水平。预合金粉的应用将允许使用高熔点元素，这些元素在焊接过程中很难在溶液中均匀。此外，我们的目标是在不同成分的混合过程中实现对工艺参数的加速识别。从项目开始，将通过高速记录和测温/热像技术进行现场工艺表征，并逐步扩展更多的传感能力。合成后，将通过使用二次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，扩大对新材料的搜索范围。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来验证，这些样品用于成熟的机械加工、热处理和机械测试。这种高通量方法将由LMD工艺路线的计算预测和通过自动化、机器学习算法评估的获取的数据来支持，从而能够在高通量设计中快速评估工艺-结构-性能关系。这里获得的大数据集将通过结构化的、面向样本的数据存储概念进行合理的处理。最后，将从样品的高重复性、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是否适合于短时表征和块体性能预测等方面对合成过程进行评估。

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.