FW-HTF-P: Towards Preparing Future Machinists: Exploring Tacit Knowledge in Machining with Artificial Intelligence and Extended Reality

FW-HTF-P：培养未来机械师：利用人工智能和扩展现实探索加工中的隐性知识

基本信息

批准号：
2222853
负责人：
YUNBO ZHANG
金额：
$ 15万
依托单位：
Rochester Institute of Tech
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222853&HistoricalAwards=false
关键词：
FW HTF Towards Preparing Future

项目摘要

This project investigates how to capture and pass on tacit knowledge in machining. Experienced machinists have a great deal of tacit knowledge that is essential for their decision-making, but such knowledge is personal, context-specific, and consequently difficult to acquire, as well as to formalize and communicate to others. This both complicates training new machinists and risks losing this tacit knowledge entirely as experienced machinists born in the post World War II baby boom retire. To address these challenges, the project team will investigate to what extent, and how, tacit knowledge in machining can be identified, understood, and transferred with the help of Artificial Intelligence (AI) and Virtual/Augmented Reality (XR). The work in this project development grant will lead to a better understanding of experienced machinists’ tacit knowledge and how it is applied in their work. It will also generate new ideas for AI techniques that recognize tacit knowledge and XR-based training systems and evaluation metrics; these will form the basis of future research that supports U.S. strategic plans around preparing the workforce for manufacturing industries.The project will expand a convergent team of researchers from multiple disciplinary backgrounds, including engineering, learning sciences, computer science, game design/gamification, psychology, and workforce development. The proposed research is structured around three fundamental thrusts: (1) A human-subject research pipeline will be developed and applied to key stakeholders to understand the types of tacit knowledge and the ways in which tacit knowledge is acquired and utilized. (2) AI and XR techniques will be explored to understand, identify, and transfer tacit knowledge. (3) Cognitive models will be established to evaluate human performance in XR training and the credibility and acceptance of the training. The project team will pay special attention to developing ideas that can make the machining occupation more accessible to populations that are currently under-represented in this field, working with partners at a nearby school that teaches machining to deaf and hard of hearing students. The overarching goal of this research is to deepen the understanding of tacit knowledge in machining and support new directions for machining training, while developing the techniques in general ways that might be applied to training in domains beyond machining.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.

该项目研究如何捕获和传递加工中的隐性知识经验丰富的机械师拥有大量对其决策至关重要的隐性知识，但这些知识是个人的、特定于环境的，因此也很难获取。这不仅使培训新机械师变得复杂，而且随着二战后婴儿潮时期出生的经验丰富的机械师退休，可能会面临完全失去这些隐性知识的风险。为了应对这些挑战，项目团队将调查什么。以及如何借助人工智能 (AI) 和虚拟/增强现实 (XR) 识别、理解和转移加工中的隐性知识。该项目开发资助的工作将有助于更好地理解经验丰富的人。机械师的隐性知识及其在工作中的应用方式还将产生识别隐性知识的人工智能技术和基于 XR 的培训系统和评估指标的新想法；这些将构成支持美国的未来研究的基础。围绕为制造业做好劳动力准备的战略计划。该项目将扩大一个由多个学科背景的研究人员组成的融合团队，包括工程学、学习科学、计算机科学、游戏设计/游戏化、心理学和劳动力发展。三个基本主旨：（1）将开发人类受试者研究管道并将其应用于关键利益相关者，以了解隐性知识的类型以及获取和利用隐性知识的方式（2）将探索人工智能和XR技术。去理解、识别、 (3) 将建立认知模型来评估 XR 培训中的人员表现以及培训的可信度和接受度。目前在该领域的代表性不足，与附近一所学校的合作伙伴合作，该学校向失聪和听力困难的学生教授机械加工。这项研究的总体目标是加深对机械加工隐性知识的理解并支持机械加工的新方向。培训，同时以通用方式开发可应用于机械加工以外领域培训的技术。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。