Collaborative Research: An Extended Reality Factory Innovation for Adaptive Problem-solving and Personalized Learning in Manufacturing Engineering

协作研究：制造工程中自适应问题解决和个性化学习的扩展现实工厂创新

• 批准号: 2302833
• 负责人: Faisal Aqlan
• 金额: $ 47万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302833&HistoricalAwards=false
• 关键词: Collaborative; Research; Extended; Reality; Factory

Rapid advances in technology increase the complexity and dynamic characteristics of problems and their solutions in industry. Problem solving is understood as the process required to achieve a goal in an uncertain environment. Understanding problem-solving entails exploring the processes used in conceptualizing the problem and in moving from the initial state to the goal. Traditional problem-solving approaches focus more on developing solutions in static situations, and are less concerned about the pace of dynamic changes and technological disruptions, which require adaptive problem-solving skills (APS). Thus, to succeed in this environment, future engineers should be equipped with APS skills. This project will design and develop a virtual factory, with physical sensors, to investigate the impact of technological advances on problem-solving skills and develop a personalized learning platform for manufacturing education to meet the needs of learners and educators. First, adaptive problem situations that relate to the past, present, and future of manufacturing will be designed. Second, extended reality (xR) environments will be developed and integrated with eye and motion tracking to provide real-time monitoring of learning behavior and dynamics. Third, analytical models will be created to enhance the proficiency of APS abilities. Research outcomes will be evaluated and disseminated via scholarly publications and educational outreach programs.This project integrates physical, virtual reality and augmented reality manufacturing simulations with sensing technology to characterize and quantify APS skills. The project will have a direct positive impact on teaching and learning of APS by simulating the industrial evolutions and dynamical changes in manufacturing settings. Rich data collected through eye, facial and motion tracking will be utilized to analyze nonlinear human behaviors, thereby providing dynamic models to improve the user learning experience and optimize APS skills. The research will be guided by the following questions: (1) To what extent do heterogeneous learning modes (physical, virtual, mixed) enhance the problem-solving experience? (2) What is the impact of integrating artificial intelligence and virtual agents on personalized learning? and, (3) How to leverage sensor signals to model and analyze the development process of APS skills? This research will also characterize multiple pathways of analyzing and solving problems, as well as the factors driving these pathways. The project will provide practical tools for xR simulations that will complement classroom instruction and help educators diagnose and tailor instruction to learner needs. Project outcomes will provide hands-on and immersive experiences to diverse learners, including undergraduate and graduate students, and better prepare them for the next industrial revolution. Integration of sensing technologies with xR environments will also facilitate communication and problem-solving for a diversity of learners.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.

技术的快速进步增加了问题的复杂性和动态特征及其在行业中的解决方案。解决问题被理解为在不确定环境中实现目标所需的过程。 理解解决问题的过程需要探索用于概念问题并从初始状态转移到目标的过程。传统的问题解决方法更多地集中于在静态情况下开发解决方案，而不太关心动态变化和技术破坏的步伐，这些变化和技术破坏需要适应性解决问题的技能（AP）。因此，为了在这种环境中取得成功，未来的工程师应配备APS技能。该项目将设计和开发具有物理传感器的虚拟工厂，以调查技术进步对解决问题技能的影响，并为制造教育提供个性化的学习平台，以满足学习者和教育者的需求。首先，将设计与过去，现在和未来相关的自适应问题情况。其次，将开发扩展现实（XR）环境，并与眼睛和运动跟踪集成，以提供对学习行为和动态的实时监控。第三，将创建分析模型以提高APS能力的能力。研究成果将通过学术出版物和教育外展计划进行评估和传播。该项目将物理，虚拟现实和增强现实制造模拟与传感技术相结合，以表征和量化APS技能。该项目将通过模拟工业发展和制造环境的动态变化来直接对AP的教学和学习产生积极影响。通过眼睛，面部和运动跟踪收集的丰富数据将用于分析非线性人类行为，从而提供动态模型来改善用户学习体验并优化APS技能。该研究将以以下问题为指导：（1）异质学习模式（物理，虚拟，混合）在多大程度上增强了解决问题的经验？ （2）整合人工智能和虚拟代理对个性化学习的影响是什么？ （3）如何利用传感器信号来建模和分析APS技能的开发过程？ 这项研究还将表征分析和解决问题的多种途径以及推动这些途径的因素。该项目将为XR模拟提供实用的工具，这些工具将补充课堂教学，并帮助教育者诊断和量身定制学习者需求。项目成果将为包括本科生和研究生在内的各种学习者提供动手和沉浸式的经验，并为下一届工业革命做好准备。将传感技术与XR环境的集成还将促进多样化的学习者的沟通和解决问题。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。