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

该基金将支持控制不同金属之间焊接界面产生的相的基础研究。在许多行业中，连接异种金属对于制造轻质、高性能和经济的结构变得越来越重要。然而，在焊接过程中，两种不同金属之间的化学反应会产生脆性金属间化合物等有害相。该奖项旨在解决控制焊缝中冶金相形成的科学和技术挑战，通过引入合适的插入金属来创建贯穿接头厚度的非线性合金成分路径。铝和铜的激光焊接（LW）说明了这种情况。这两种金属是电池组件的主要材料，电动汽车对电池的需求量很大。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，比局限于两种贱金属时具有更大的自由度。反过来，该奖项可以扩大不同金属接头在汽车、航空航天、发电、船舶应用、医疗设备和信息技术等行业的采用。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对研究的参与，并对本科和研究生教育产生积极影响。研究人员将利用数据驱动的范式设计和实现最佳结合相，以了解和控制不同金属接头中的冶金相。研究小组将进行多尺度模拟，以建立数据驱动模型，提供高保真的焊接预测。结合界面上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正和激光焊接工艺的逆设计，仿真和设计将通过实验验证。本研究将填补在理解不同LW条件下LW能量输入、钥匙孔动力学、相形成和从液体到固体转变之间相互作用的知识空白。它将建立一个有效的方法，通过热力学和动力学，预测优选相和性能，以满足关节的要求。该奖项反映了美国国家科学基金会的法定使命，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。

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.