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Data-Driven Leaning and Controlling Metallurgy Matters in Dissimilar Metal Joints

数据驱动的学习和控制异种金属接头中的冶金问题

基本信息

批准号：
2226976
负责人：
Jingjing Li
金额：
$ 67.25万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226976&HistoricalAwards=false
关键词：
Data Driven Leaning Controlling Metallurgy

项目摘要

This grant will support fundamental research on controlling phases generated at the welded interface between dissimilar metals. Joining dissimilar metals has become increasingly important for creating lightweight, high-performance, and economic structures in various industries. However, the chemical reactions between two dissimilar metals during welding can generate harmful phases like brittle intermetallic compounds. This award aims to address scientific and technical challenges in controlling the metallurgical phase formation in the weld by introducing a suitable interposing metal to create a nonlinear alloy composition pathway through the joint thickness. The situation is illustrated by the laser welding (LW) of aluminum and copper. These two metals are major materials in the assembly of battery cells, which are in high demand for electric vehicles. This research will enable engineers to design and transform the metallic phases in the weld in a controllable fashion with more freedom than when limited to the two base metals. In turn, this award can broaden the adoption of dissimilar metal joints in industries such as automotive, aerospace, power generation, marine application, medical devices, and information technology. This research involves several disciplines, including manufacturing, materials science, multiscale simulations, and machine learning. The multi-disciplinary approach will broaden the participation of underrepresented groups in research and positively impact both undergraduate and graduate education. The investigators will design and realize optimal bonding phases with a data-driven paradigm to learn and control metallurgic phases in dissimilar metal joints. The research team will conduct multiscale simulations for data generation to establish data-driven models which provide high-fidelity welding predictions. Metallurgical reactions at the bonding interface will be explained using calculation of phase diagrams (CALPHAD)-based analysis, correlation analysis and molecular dynamics simulations. Machine learning will be used to provide inverse design of the nonlinear modification and laser welding processes, and simulation and designs will be validated experimentally. This research will fill the knowledge gap in understanding the interactions between LW energy inputs, keyhole dynamics, phase formation, and transition from liquids to solids under different LW conditions. It will build an efficient methodology, via thermodynamics and kinetics, to predict preferred phases and properties to meet a joint’s requirements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该补助金将支持控制异种金属焊接界面处产生的相的基础研究。异种金属的连接对于在各个行业中制造轻质、高性能和经济的结构变得越来越重要。然而，焊接过程中两种不同金属之间的化学反应会产生有害相，如脆性金属间化合物。该奖项旨在通过引入合适的插入金属来控制焊缝中冶金相的形成，以通过接头厚度创建非线性合金成分路径，从而解决科学和技术挑战。铝和铜的激光焊接（LW）说明了这种情况。这两种金属是组装电池单元的主要材料，电动汽车对电池单元的需求很高。这项研究将使工程师能够以可控的方式设计和转换焊缝中的金属相，比仅限于两种基底金属时更自由。反过来，该奖项可以扩大异种金属接头在汽车、航空航天、发电、船舶应用、医疗器械和信息技术等行业的应用。这项研究涉及多个学科，包括制造，材料科学，多尺度模拟和机器学习。多学科方法将扩大代表性不足的群体参与研究，并对本科和研究生教育产生积极影响。研究人员将设计和实现最佳的键合阶段与数据驱动的范例，以学习和控制异种金属接头的焊接阶段。研究团队将进行多尺度模拟以生成数据，从而建立数据驱动的模型，提供高保真的焊接预测。结合界面处的冶金反应将使用基于相图计算（CALPHAD）的分析、相关分析和分子动力学模拟来解释。机器学习将用于提供非线性修改和激光焊接过程的逆向设计，并将通过实验验证模拟和设计。这项研究将填补知识空白，了解LW能量输入，锁孔动力学，相形成之间的相互作用，并在不同的LW条件下从液体到固体的过渡。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。