CAREER: Ultrasonically Assisted Wire Arc Additive Manufacturing of Metal Matrix Nanocomposites for High-strength, Lightweight Structures

这项学院早期职业发展(Career)补助金专注于一种创新的超声辅助焊丝电弧添加剂制造工艺，用于制造自由形式和大规模的金属基纳米复合材料结构。金属基纳米复合材料是一类具有优异力学性能的轻质材料，其良好的力学性能归功于纳米颗粒在块体中的均匀分散。焊丝电弧添加剂制造是基于弧焊原理，即连续送丝的金属丝被熔化并逐层沉积成所需的复杂形状。该方法能够直接制造金属基纳米复合材料功能部件，与粉末基添加剂制造方法相比，具有明显的高沉积速率和低成本优势。该项目将促进金属基纳米复合材料在轻量化结构中的广泛应用，提高能源效率，降低燃料消耗，造福于各种交通行业，从而为国民经济和安全做出贡献。不同层次的多学科和以现实世界问题为基础的学生培训很好地融入了这个项目。研究成果被转化为多项外联举措，提高年轻一代和代表性不足的少数群体对制造业职业的认识。虚拟实验室工具促进了远程教育和继续教育。所有这些都有助于培养具有全球竞争力和多样化的STEM劳动力。本研究的目的是研究超声辅助焊丝电弧添加剂制备金属基纳米复合材料。虽然轻质、高强度的部件在这些材料中是可能的，但由于纳米颗粒在反复熔化循环中团聚、凝固缺陷、气孔和劣质铸态组织，实现优异的机械性能是具有挑战性的。为了改进焊丝电弧添加剂的制造，本研究利用叠加超声振动来分散纳米颗粒，细化组织，减少缺陷。具体目标是(1)了解声场、电磁场和纳米粒子分散对熔池流体动力学的相互作用，(2)揭示声场和纳米粒子对重复熔化和凝固循环中微观组织演变的耦合原理，(3)结合数据驱动和基于物理的方法进行高保真建模和分析。超声辅助焊丝添加剂制造系统配备了多个传感器，用于在线热-机械-声学分析，以实现过程监测和控制。对不同条件下采用该混合工艺制造的零件进行了综合评定和多尺度显微组织表征。为了建立工艺参数、沉积轮廓、显微组织和力学性能之间的关系，利用新兴的数据科学工具，这些工具由基于物理的熔池、凝固和相变模型来规律化。这一建模框架使计算和数据高效工具能够分析各种制造过程中涉及的复杂非线性物理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) grant focuses on an innovative ultrasonically assisted wire arc additive manufacturing process for fabricating metal matrix nanocomposite structures in freeform and at large scale. Metal matrix nanocomposites are a promising class of lightweight materials with superior mechanical performance attributed to well-dispersed nanoparticles within the bulk. Wire arc additive manufacturing is based on arc welding principles in which a continuously fed metal wire is melted and deposited into a desired complex shape, layer-by-layer. The process enables the direct manufacture of metal matrix nanocomposite functional parts and is advantageous in distinctly high deposition rate and low cost compared with powder-based additive manufacturing processes. This project would facilitate wide applications of metal matrix nanocomposites for lightweight structures, which improves energy efficiency, reduces fuel consumption and benefits various transportation industries, thus contributing to national economy and security. Multidisciplinary and real-world problem-based student training at different levels are well integrated into this project. Research results are transformed into multiple outreach initiatives that increase manufacturing career awareness in young generations and under-represented minorities. The virtual lab tools promote distance and continuing education. All of these contribute to development of globally competitive and diverse STEM workforce. The goal of this research is to investigate ultrasonically assisted wire arc additive manufacturing of metal matrix nanocomposites. While lightweight, high strength components are possible in these materials, achieving superior mechanical properties is challenging due to agglomeration of nanoparticles in the repeated melting cycles, solidification defects, porosity and inferior as-cast microstructure. To improve wire arc additive manufacturing, this research utilizes superimposed ultrasonic vibration to disperse the nanoparticles, refine the microstructure and minimize the defects. Specific objectives are to (1) understand the interaction of acoustic and electromagnetic fields and nanoparticle dispersions on melt pool hydrodynamics, (2) reveal coupling principles of acoustic field and nanoparticles on microstructure evolution in the repeated melting and solidification cycles, and (3) integrate data-driven and physics-based approaches for high fidelity modeling and analysis. The ultrasonically assisted wire arc additive manufacturing system is equipped with multiple sensors for online thermal-mechanical-acoustic analysis for process monitoring and control. Parts built with this hybrid process under different conditions are subject to comprehensive evaluation and multi-scale microstructure characterization. To establish relationships between process parameters, deposition profile, microstructure and mechanical properties, emerging data science tools are utilized, which are regularized by physics-based molten pool, solidification and phase transformation models. This modeling framework enables computational and data efficient tools for analyzing complex nonlinear physics involved in various manufacturing processes.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.