DMREF: Adaptive Control of Microstructure from the Microscale to the Macroscale

对加工条件的控制，包括材料和部件制造过程中的温度和加载顺序，对于优化强度和性能至关重要。这些性能又取决于材料的详细内部结构或微结构。从历史上看，最优的加工条件主要是通过经验和反复试验来确定的，这需要很长的制造和上市周期。这项旨在革新和设计我们的未来的设计材料奖(DMREF)支持专注于建立必要的方法来大幅缩短这些开发周期的研究。这将通过开发实验、计算和过程控制方法的新组合来实现，以积极控制先进金属的加工，实现预测的优化性能的目标组织。最初的实施将使用定制的设备，从而可以在实验室规模复制制造条件，同时以非常高的放大倍数成像材料的微观结构如何演变。然后，将在加工过程中将演变的微观结构与所需微观结构的计算模拟进行比较，并使用主动控制技术根据需要进行调整。然后，最优加工条件将扩展到与一组工业合作者一起工作的制造条件。因此，这项工作将开发出对美国金属制造业具有显著竞争优势的新方法。从事这项研究的学生将接受应用基础材料研究以应对加速制造开发周期的挑战的具体技术和整体观点的培训。本研究将发展必要的理解和方法，以生成和解释热机械加工过程中微观组织变化的实验和模拟描述，然后应用必要的工艺条件控制，以在Ti-6%Al-4%合金中生成指定的组织。热机械变形过程中的实时扫描电子显微镜观察和机械性能测量将与微观结构演变的预测模拟相结合。然后，通过使用基于模型的前馈控制和基于测量的反馈控制，对温度和应变历史进行自适应控制，从而实现对演变组织的实时调整。此外，通过与一组既定的行业合作伙伴合作，这些新知识将转化为宏观应用。因此，这项工作的总体智力意义在于综合了实验表征、工艺控制和微观结构模拟，以预测、监测和控制热机械加工过程中的微观结构演变，并将实验室测试扩展到宏观应用。主要的更广泛的重点将是培训下一代劳动力，使其熟练掌握以材料基因组倡议为代表的综合实验-模拟-数据方法。

The control of processing conditions, including the temperature and load sequences during manufacturing of materials and parts, is critical to optimize strength and performance. These properties depend in turn upon the detailed internal structure, or microstructure, of the material. Historically, the optimum processing conditions have been determined largely through experience and trial-and-error, requiring long lead times to manufacture and to market. This Designing Materials to Revolutionize and Engineer our Future (DMREF) award supports research focused on establishing the methodologies necessary to substantially shorten these development cycles. This will be achieved by developing new combinations of experimental, computational and process control methods to actively control processing of advanced metals, to achieve the target microstructures that are predicted for optimized performance. Initial implementations will employ custom-built equipment whereby manufacturing conditions can be replicated at the laboratory scale, while imaging at very high magnification how the microstructure of the material evolves. The evolving microstructure will then be compared during processing to computational simulations of the desired microstructure, and adjusted as required using active control technologies. The optimum processing conditions will then be extended to manufacturing conditions working with a set of industrial collaborators. This work will thus develop new methodologies that can be of significant competitive advantage to the US metals manufacturing industry. The set of students engaged in this research will be trained in both the specific techniques and in the overall outlook of applying fundamental materials research to the challenge of accelerating manufacturing development cycles.This research will develop the understanding and methods necessary to generate and interpret experimental and simulated descriptions of microstructural transformation during thermo-mechanical processing, and then to apply the necessary control of processing conditions to generate a prescribed microstructure in the alloy Ti-6%Al-4%. Real-time scanning electron microscope observations and mechanical property measurements during thermo-mechanical deformation will be coupled to predictive simulations of microstructural evolution. Real-time adjustment of the evolving microstructure will then be enabled through adaptive control of the temperature and strain history using model-based feed-forward control and measurement-based feedback control. Furthermore, through working with an established set of industrial collaborators, this new knowledge will be translated into macro scale applications. The overall intellectual significance of this work is thus the synthesis of experimental characterization, process control, and microstructure simulation to predict, monitor and control microstructural evolution during thermo-mechanical processing, and the scaling of laboratory tests to macro-scale applications. The major broader focus will be on training the next-generation workforce to be skilled in the integrated experiment-simulation-data approach epitomized by the Materials Genome Initiative.