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

新数字技术在制造业的普及，正在改变由机器、机器人和软件系统组成的网络生成、解释和共享数据的方式。这种“工业物联网”有望提高美国制造业的质量和生产率。然而，人类工人与这些数字系统有效交互的能力是有限的，这可能导致认知中断，从而可能对产出和工作满意度产生负面影响。这项美国国家科学基金会研究培训（NRT）奖旨在帮助克莱姆森大学的硕士和博士学位学生通过人的因素、机器人、认知科学、人工智能、系统工程、教育、制造和社会行为科学的融合，推进人类、技术、工作和健康关系的发现。这将通过设计和集成人类数字技术来实现，这些技术可以增强人类在制造环境中的身体和认知互动和能力。该项目预计将培养50名硕士和博士学生，包括22名受资助的学员，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学生的并行计划进行交互，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化和智能制造、自动化和相关数据系统的创新课程，响应了帮助塑造和更好地准备未来STEM研究生的关键需求。培训和研究在新兴的数字化制造企业（即工业物联网）中采用以人为本的设计方法，通过量化物理和人类认知，开发增强技术（例如增强现实辅助工人赋权）来改善制造企业中工人的行为和态度。该项目将以汽车行业为例（即汽车装配操作），采用工厂设置，包括零件制造，结构和子装配操作，机器人技术，配套，物流和全尺寸汽车装配线，以及本科和技术学院课程中的平行课程。多层次的教育方法有望推动改善团队沟通，产生有关工人行为和态度的知识，并为学生在制造业和其他行业中领导实施所研究的技术做好准备。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科研究领域对STEM研究生进行有效培训。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。

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.