CAREER: Dynamic Process-Attribute-Data-Performance Modeling to Enable Smart Ultrasonic Metal Welding

学院早期职业发展(CALEAR)补助金将支持超声波金属焊接(UMW)的基础研究。与传统熔焊技术相比，UMW的优势之一是能够连接异种金属、能源效率、焊接周期短和环境友好，使其成为电动和轻量化汽车先进制造的一种有前途的连接技术。然而，UMW的运行窗口相对较窄，对不可预测、无法控制的环境条件非常敏感。这种潜在工艺机制中的长期知识差距使得对接头质量的预测和控制变得困难，这限制了它的使用。该项目将通过利用过程物理、微观结构分析和数据科学的进步，利用制造科学以信息为中心的新兴变革。通过在工艺条件、显微组织焊接属性、在线传感数据和焊接性能之间建立动态、随机的关系，该研究将促进对UMW工艺机理的基本理解。所获得的知识将用于建立一套基于机器学习的决策工具，最终实现智能UMW。这笔赠款还将支持各种教育和推广活动，这些活动有助于美国智能制造劳动力的教育。人们普遍认为，UMW工艺条件通过微尺度焊接属性的动态演变来影响连接性能，而焊缝成形过程会产生一个特征，这反映在可以在线检测的参数中。然而，到目前为止，还没有研究充分地模拟或量化内在的动态、随机的过程-属性-数据-性能(PADP)关系。这项研究的首要目标是创建一个由创新的机器学习和统计模型组成的PADP建模框架。该框架将分两步完成。首先，将建立包含不确定性量化的时空模型来表征过程-属性-绩效关系。其次，将进行基于张量的相关和回归分析，以调查属性与数据的关系。这一框架将进一步用于开发一系列物理感知的机器学习工具，用于过程控制，包括过程优化、在线质量监测和实时控制。最后，该项目将调查转移学习方法的使用，以提供一种具有成本效益的方式，为相关产品或产品系列建立PADP模型和决策战略。这种学习能力将是实现智能制造范例的云智能的重要组成部分。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 will support fundamental research on ultrasonic metal welding (UMW). Among the advantages of UMW over conventional fusion welding techniques are the ability to join dissimilar metals, energy efficiency, short welding cycles, and environmental friendliness, making it a promising joining technology for the advanced manufacturing of electrified and lightweight vehicles. Nevertheless, UMW has a relatively narrow operating window and is very sensitive to unpredictable, uncontrollable environmental conditions. This longstanding knowledge gap in the underlying process mechanisms makes the prediction and control of joint quality difficult, which limits its use. This project will take advantage of the emergent information-centric transformation of manufacturing science by leveraging advances in process physics, microstructural analysis, and data science. By establishing dynamic, stochastic relationships between process conditions, microstructural weld attributes, online sensing data, and weld performance, the research will advance the fundamental understanding of process mechanisms in UMW. The knowledge gained will be used to establish a suite of machine learning-based decision-making tools that will ultimately enable smart UMW. This grant will also support diverse educational and outreach activities that contribute to the education of the U.S. smart manufacturing workforce. It is a widely accepted hypothesis that UMW process conditions influence the joining performance via the dynamic evolution of micro-scale weld attributes and the weld formation process generates a signature, as reflected in parameters that can be sensed online. Nonetheless, there exist no studies to date that adequately model or quantify the inherent dynamic, stochastic process-attribute-data-performance (PADP) relationship. The overarching goal of this research is to create a PADP modeling framework that consists of innovative machine learning and statistical models. The framework will be completed in two steps. First, spatiotemporal models incorporating uncertainty quantification will be built to characterize the process-attribute-performance relationship. Second, a tensor-based correlation and regression analysis will be performed to investigate the attribute-data relationship. This framework will be further employed to develop a series of physics-aware, machine learning tools for process control, including process optimization, online quality monitoring, and real-time control. Finally, the project will investigate the use of a transfer learning methodology to provide a cost-effective way to build PADP models and decision-making strategies for related products or product families. This learning capability will be an essential component in the cloud intelligence that enables the smart manufacturing paradigm.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.