NRT: Technology-Human Integrated Knowledge Education and Research (THINKER)

新的数字技术在制造业中的普及正在改变数据的生成、解释和通过机器、机器人和软件系统网络共享的方式。这种“工业物联网”为提高美国制造业的质量和生产率带来了巨大的希望。然而，人类工人与这类数字系统有效交互的能力是有限的，可能会导致认知中断，这可能会对产出和工作满意度产生负面影响。这一国家科学基金会研究培训(NRT)奖培养克莱姆森大学的硕士和博士学位学生，通过融合人类因素、机器人学、认知科学、人工智能、系统工程、教育、制造和社会行为科学，推动人类、技术、工作和健康之间的联系的发现。这将通过设计和整合人类数字技术来实现，这些技术增强了人类在制造环境中的物理和认知互动和能力。该项目预计将培训五十(50)名硕士和博士生，其中包括二十二(22)名资助的实习生，他们来自电气工程、工业工程、计算机科学、制造、系统集成、心理学和社会学。这些学生将在受控的制造环境中与本科生和技术学院的学生进行平行项目的交互，以测试跨多个学术水平的部署和集成。该项目通过专注于新的数字和智能制造、自动化和相关数据系统的创新课程，满足了帮助塑造和更好地准备未来STEM研究生的迫切需求。培训和研究在新兴的数字制造企业(即工业物联网)中采用以人为中心的设计方法，通过量化物理和人类认知并开发增强技术(例如，增强现实辅助工具以增强工人的能力)来改善制造企业中的工人行为和态度。该项目将侧重于汽车行业的示范(即车辆组装操作)，采用包括零部件制造、结构和组件操作、机器人、套件、物流和全尺寸车辆装配线的工厂设置，以及本科和技术学院课程中的平行课程。这种多层次的教育方法预计将推动改善团队沟通，产生关于工人行为和态度的知识，并为学生在制造业和其他行业领导实施正在研究的技术做好准备。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革意义的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科研究领域对STEM研究生进行有效培训。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。

The pervasiveness of new digital technologies in manufacturing is changing the way that data are generated, interpreted and shared over networks of machines, robotics and software systems. This "industrial internet of things" holds great promise for improving the quality and productivity of manufacturing in the United States. However, the ability of human workers to effectively interface with such digital systems is limited, potentially leading to disruptions in cognition that may negatively affect output and job satisfaction. This National Science Foundation Research Traineeship (NRT) award prepares master's and doctoral degree students at Clemson University to advance discoveries at the nexus of humans, technology, work, and health, through the convergence of human factors, robotics, cognitive sciences, artificial intelligence, systems engineering, education, manufacturing and social behavioral sciences. This will be achieved through the design and integration of human digital technologies that enhance humans' physical and cognitive interaction and abilities in manufacturing environments. The project anticipates training fifty (50) M.S. and Ph.D. students, including twenty-two (22) funded trainees, from electrical engineering, industrial engineering, computer science, manufacturing, systems integration, psychology, and sociology. These students will interface with a parallel program of undergraduate and technical college students in a controlled manufacturing environment to test deployment and integration across multiple academic levels.This project responds to the critical need to help shape and better prepare the STEM graduate student of tomorrow through an innovated curriculum that focuses on the new digital and smart manufacturing, automation, and associated data systems. The training and research takes a human-centered design approach in the emerging digital manufacturing enterprise (i.e., Industrial Internet of Things), by quantifying physical and human cognition and developing augmented technologies (e.g. augmented reality aids for worker empowerment) to improve worker behaviors and attitudes in the manufacturing enterprise. This project will focus on an automotive industry exemplar (i.e., vehicle assembly operation), employing a factory setting which includes parts manufacture, structural and subassembly operations, robotics, kitting, logistics, and a full-scale vehicle assembly line, together with parallel programs in undergraduate and technical college curricula. The multi-level educational approach is expected to drive improved team communication, generate knowledge on worker behaviors and attitudes, and prepare students for leading implementation of the technologies under study in manufacturing and other industries. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.