FW-HTF-P: Training an Agile, Adaptive Workforce for the Future of Manufacturing with Intelligent Augmented Reality

FW-HTF-P:通过智能增强现实为未来的制造培训一支敏捷、适应性强的员工队伍

基本信息

  • 批准号:
    2026618
  • 负责人:
  • 金额:
    $ 15万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-01 至 2021-12-31
  • 项目状态:
    已结题

项目摘要

The future of the American manufacturing workforce faces a perfect storm of challenges: (1) a shortage of workers due to the retirement of the Baby Boom generation, (2) a shifting skillset due to the introduction of advanced technologies, and (3) a lack of understanding and appeal of manufacturing jobs among younger cohorts. Consequently, over 2.4 million U.S. manufacturing jobs are anticipated to be left unfilled by 2030 with a projected cost of $2.5 trillion on the U.S. manufacturing GDP. Augmented reality (AR) has been recently adopted for experiential training and upskilling of manufacturing workers. AR is proven to reduce new-hire training time by 50% through spatiotemporal alignment of instructions with worker experience. However, evidence suggests that overreliance of workers on AR scaffolds can cause brittleness of knowledge and deteriorate performance in adapting to novel situations. This project will investigate if and how AR can help manufacturing workers develop agility and adaptability on the shop floor while avoiding the risks associated with dependence on technology and stifled innovation. A new intelligent AR system will enable dynamic adjustment of AR instructions to worker task performance and enhance their ability to master complex tasks such as assembly and maintenance. This research will serve the national priority for rapid and lifelong upskilling of manufacturing workforce, especially underrepresented and under-served minority groups.A convergent team of learning scientists, labor economists, cognitive psychologists, computer scientists, and manufacturing engineers will investigate three fundamental research thrusts: (1) Future work: Labor market analyses of changes in employer skill requirements will be conducted to understand the degree to which AR technologies have been introduced in the U.S. and the skillsets workers will need in future factories. (2) Future technology: An intelligent AR system will be devised to understand, predict, and guide the behavior of AR-supported workers through adaptive scaffolding of instructions to their performance and level of expertise. (3) Future worker: Hypothesis-driven human-subjects research will be conducted to understand the impacts of adaptive AR scaffolds on worker performance, cognitive load, and learning. The overarching goal of this research is to balance the efficiency and innovation of future manufacturing workers by improving their ability to transfer the acquired knowledge and skills to new situations on the shop floor. Experts from industry, government, and academia will be convened in a multidisciplinary workshop to illuminate the potentials and risks of AR technology for training future workforce and bridging the skills gap in manufacturing.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.
美国制造业劳动力的未来面临着一场完美的挑战风暴:(1)由于婴儿潮一代的退休而导致的工人短缺,(2)由于先进技术的引入而出现的技能转移,以及(3)年轻群体对制造业工作缺乏理解和吸引力。因此,预计到2030年,美国制造业将有超过240万个工作岗位空缺,预计美国制造业GDP的成本将达到2.5万亿美元。增强现实(AR)最近被用于制造业工人的体验式培训和技能提升。事实证明,AR通过将指导与员工经验在时空上保持一致,可以将新员工培训时间减少50%。然而,有证据表明,工人对AR支架的过度依赖会导致知识的脆性,并降低适应新情况的能力。这个项目将调查AR是否以及如何帮助制造工人在车间培养敏捷性和适应性,同时避免与依赖技术和抑制创新相关的风险。新的智能AR系统将使AR指令能够动态调整工人的任务绩效,并增强他们掌握复杂任务的能力,如组装和维护。这项研究将服务于制造业劳动力快速和终身提高技能的国家优先事项,特别是代表不足和服务不足的少数群体。一个由学习科学家、劳动经济学家、认知心理学家、计算机科学家和制造工程师组成的汇聚团队将调查三个基本研究主题:(1)未来工作:将对雇主技能要求的变化进行劳动力市场分析,以了解AR技术在美国引入的程度以及工人在未来工厂将需要的技能集。(2)未来技术:将设计一个智能AR系统,通过根据他们的表现和专业水平自适应地构建指导,来理解、预测和指导AR支持的员工的行为。(3)未来工作者:将进行假设驱动的人类受试者研究,以了解适应性AR支架对工作者绩效、认知负荷和学习的影响。这项研究的总体目标是通过提高未来制造业工人将获得的知识和技能转移到车间新环境中的能力来平衡他们的效率和创新。来自行业、政府和学术界的专家将被召集在一个多学科的研讨会上,阐明AR技术在培训未来劳动力和弥合制造业技能差距方面的潜力和风险。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。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Exploring augmented reality for worker assistance versus training
  • DOI:
    10.1016/j.aei.2021.101410
  • 发表时间:
    2021-09-09
  • 期刊:
  • 影响因子:
    8.8
  • 作者:
    Moghaddam, Mohsen;Wilson, Nicholas C.;Marsella, Stacy C.
  • 通讯作者:
    Marsella, Stacy C.
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Mohsen Moghaddam其他文献

Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation
利用强化学习中的任务模块化实现适应性工业 4.0 自动化
  • DOI:
    10.1115/1.4049531
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Qiliang Chen;B. Heydari;Mohsen Moghaddam
  • 通讯作者:
    Mohsen Moghaddam
Best matching processes in distributed systems
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mohsen Moghaddam
  • 通讯作者:
    Mohsen Moghaddam
Real-time administration of tool sharing and best matching to enhance assembly lines balanceability and flexibility
  • DOI:
    10.1016/j.mechatronics.2014.10.001
  • 发表时间:
    2015-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Mohsen Moghaddam;Shimon Y. Nof
  • 通讯作者:
    Shimon Y. Nof
Augmenting Learning with Augmented Reality: Exploring the Affordances of AR in Supporting Mastery of Complex Psychomotor Tasks
通过增强现实增强学习:探索 AR 在支持掌握复杂精神运动任务方面的功能可供性
  • DOI:
    10.48550/arxiv.2305.09875
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Dong Woo Yoo;Sakib Reza;Nicholas Wilson;K. Jona;Mohsen Moghaddam
  • 通讯作者:
    Mohsen Moghaddam
Modeling Gaze Behavior for Real-Time Estimation of Visual Attention and Expertise Level in Augmented Reality
对注视行为进行建模,以实时估计增强现实中的视觉注意力和专业水平

Mohsen Moghaddam的其他文献

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{{ truncateString('Mohsen Moghaddam', 18)}}的其他基金

Accelerating Skill Acquisition in Complex Psychomotor Tasks via an Intelligent Extended Reality Tutoring System
通过智能扩展现实辅导系统加速复杂精神运动任务中的技能习得
  • 批准号:
    2302838
  • 财政年份:
    2023
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
Collaborative Research: From User Reviews to User-Centered Generative Design: Automated Methods for Augmented Designer Performance
协作研究:从用户评论到以用户为中心的生成设计:增强设计师性能的自动化方法
  • 批准号:
    2050052
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
FW-HTF-R: Fostering Learning and Adaptability of Future Manufacturing Workers with Intelligent Extended Reality (IXR)
FW-HTF-R:通过智能扩展现实 (IXR) 促进未来制造业工人的学习和适应能力
  • 批准号:
    2128743
  • 财政年份:
    2021
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant

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