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

DMREF：从微观到宏观的微观结构的自适应控制

• 批准号: 1729336
• 负责人: Robert Hull
• 金额: $ 152.43万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1729336&HistoricalAwards=false
• 关键词: DMREF; Adaptive; Control; Microstructure; Microscale

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.

加工条件的控制，包括材料和部件制造过程中的温度和载荷顺序，对于优化强度和性能至关重要。 这些性质又取决于材料的详细内部结构或微观结构。 从历史上看，最佳加工条件主要是通过经验和试错来确定的，需要很长的生产和销售周期。 设计材料以革命和工程我们的未来（DMREF）奖支持研究重点是建立必要的方法，大大缩短这些开发周期。 这将通过开发实验，计算和过程控制方法的新组合来实现，以主动控制先进金属的加工，以实现预测的目标微观结构以优化性能。 最初的实施将采用定制的设备，从而可以在实验室规模上复制制造条件，同时以非常高的放大率成像材料的微观结构如何演变。 然后，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，并根据需要使用主动控制技术进行调整。最佳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，这项工作将开发新的方法，可以显着的竞争优势，美国金属制造业。 参与本研究的学生将接受应用基础材料研究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结构转变的实验和模拟描述，然后对加工条件进行必要的控制，以在Ti-6%Al-4%合金中产生预定的显微组织。热机械变形期间的实时扫描电子显微镜观察和力学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，能够实时调整不断变化的微观结构。 此外，通过与一系列既定的工业合作者合作，这些新知识将转化为宏观规模的应用。因此，这项工作的总体智力意义是综合实验表征，过程控制和微观结构模拟，以预测，监测和控制在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尺度的应用。更广泛的主要重点将是培训下一代劳动力，使其熟练掌握材料基因组计划所体现的综合实验-模拟-数据方法。

项目成果

SERIAL SECTIONING OF Ti-6Al-4V USING AN FIB-SEM DUAL BEAM SYSTEM

使用 FIB-SEM 双束系统对 Ti-6Al-4V 进行连续切片

• 发表时间: 2022
• 期刊: Rensselaer Polytechnic Institute Thesis
• 作者: Alexander Brodsky

Influence of Controlled Cooling Rates During Thermal Processing of Ti 6% Al 4% V Alloys Using In-Situ Scanning Electron Microscopy

影响%20of%20受控%20冷却%20速率%20期间%20热%20加工%20of%20Ti%206%%20Al%204%%20V%20合金%20使用%20原位%20扫描%20电子%20显微镜

• DOI: 10.1557/adv.2020.190
• 发表时间: 2020
• 期刊: MRS Advances
• 影响因子: 0.8
• 作者: Kane, Genevieve A;Frey, M. David;Hull, Robert
• 通讯作者: Hull, Robert

Adaptive characterization of microstructure dataset using a two stage machine learning approach

• DOI: 10.1016/j.commatsci.2020.109593
• 发表时间: 2020-05-01
• 期刊: COMPUTATIONAL MATERIALS SCIENCE
• 影响因子: 3.3
• 作者: Baskaran, Arun;Kane, Genevieve;Lewis, Daniel
• 通讯作者: Lewis, Daniel

Numerical modeling of Ti-6Al-4V microstructure evolution for thermomechanical process control

用于热机械过程控制的 Ti-6Al-4V 微观结构演化数值模拟

• 发表时间: 2019
• 期刊: Proceedings of NUMIFORM 2019: The 13th International Conference on Numerical Methods in Industrial Forming Processes
• 作者: Bhatt, S;Baskaran, A;Lewis, D;Maniatty, A
• 通讯作者: Maniatty, A

Distributed Temperature Control in Laser-Based Manufacturing

• DOI: 10.1115/1.4046154
• 发表时间: 2020-06
• 期刊: Journal of Dynamic Systems Measurement and Control-transactions of The Asme
• 影响因子: 1.7
• 作者: C. Zheng;J. Wen;M. Diagne