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

通过组合激光沉积焊接和短期热处理进行高度灵活的材料合成和微观结构调整，以实现高通量材料的开发

• 批准号: 434424600
• 金额: --
• 依托单位: BIAS - Bremer Institut für Angewandte Strahltechnik GmbH
• 依托单位国家: 德国
• 项目类别: Major Instrumentation Initiatives
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/434424600?language=en
• 关键词: Highly; flexible; material; synthesis; microstructure

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.

高通量材料的开发需要短时间方法来进行数千样品的合成，表征，数据分析和建模。结构材料是由它们的组成，尤其是其微观结构来定义的，源自该项目的制造过程中零件的特定热机械历史，我们建议以多种构成受自定义的热处理条件的不同组合物的合成，以获取，使用和扩展一种独特的定制激光金属矿床（LMD）设备。拟议的项目将解决LMD过程链的全部，基于物理的集成以及基于数据的关键步骤的建模，并为制造过程中对材料转换的过程进行监测开发新的解决方案。将仔细评估粉末混合物的重复性和可靠性和可靠性。通过可调节的流速，最多包括六个粉末或粉末混合物，包括金属合金和合成前的碳氮颗粒，将成分“按需”调节。通过这种方式，我们将生产新颖和项目的合金粉库。微型合金元素的含量将在传统水平保持粉末流速时进行调整。预先合金粉末的应用将允许使用高融化元件，这些元件在焊接过程中很难在溶液中均匀化。同样，我们旨在在不同组成的混合过程中实现对过程参数的加速识别。通过高速记录和高温法/热量计学的表征，将从项目开始，并逐步扩展具有进一步的敏感性功能。合成后，新颖材料的搜索域将通过第二激光器进行热处理扩展，并使用第二激光和感应性暖气和激活的冷却或激活的冷却或Quench和Quench降温和Quince syprome syprome Comente and Microsstruce和Sirtal sampers same same same same same same same same same same same same。许多离散样品的合成将允许每个样品的独立热历史记录。高通量方法将通过制造较大的常规材料样品来验证，以实现良好的机械性，热处理和机械测试。这种高通量方法将由LMD过程路由的计算预测以及通过自动化的机器学习算法评估的获得的数据来支持，从而可以快速评估高吞吐量设计中的过程结构 - 核关系。此处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合理地传递。最后，将根据针对和实现的样品特征的样品的高可重现性，组成和热处理的灵活性以及对短时表征和预测散装特性的样本适用性进行评估。