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

该学院早期职业发展（CAREER）资助的重点是一种创新的超声辅助丝弧增材制造工艺，用于大规模自由制造金属基纳米复合材料结构。金属基纳米复合材料是一类很有前途的轻质材料，其上级机械性能归因于纳米颗粒在本体内的良好分散。丝弧增材制造基于电弧焊接原理，其中连续供给的金属丝被熔化并逐层沉积成期望的复杂形状。该方法能够直接制造金属基质纳米复合材料功能部件，并且与基于粉末的增材制造方法相比，具有明显高的沉积速率和低成本的优势。该项目将促进金属基纳米复合材料在轻质结构中的广泛应用，从而提高能源效率，降低燃料消耗，并使各种运输行业受益，从而为国家经济和安全做出贡献。多学科和现实世界的问题为基础的学生培训在不同层次上很好地融入这个项目。研究成果转化为多个外联举措，提高年轻一代和代表性不足的少数民族的制造业职业意识。虚拟实验室工具促进了远程教育和继续教育。所有这些都有助于发展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金属基纳米复合材料的超声辅助电弧增材制造。虽然在这些材料中轻质、高强度的部件是可能的，但是由于在重复的熔化循环中纳米颗粒的团聚、凝固缺陷、孔隙率和较差的铸态微观结构，实现上级机械性能是具有挑战性的。为了改善电弧增材制造，本研究利用叠加超声振动分散纳米颗粒，细化组织，减少缺陷。具体目标是（1）理解声场和电磁场以及纳米颗粒分散对熔池流体力学的相互作用，（2）揭示声场和纳米颗粒在重复熔化和凝固循环中对微观结构演变的耦合原理，以及（3）集成数据驱动和基于物理的方法以进行高保真建模和分析。超声波辅助电弧增材制造系统配备了多个传感器，用于在线热机械声学分析，以进行过程监测和控制。在不同条件下使用这种混合工艺制造的零件将进行综合评估和多尺度微观结构表征。为了建立工艺参数、沉积轮廓、微观结构和机械性能之间的关系，利用新兴的数据科学工具，这些工具通过基于物理的熔池、凝固和相变模型进行规范化。该模型框架使计算和数据高效的工具，用于分析复杂的非线性物理涉及到各种制造过程。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.