权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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、特定于具体情况的，因此难以获取、形式化并与他人交流。这不仅使新机械师的培训变得复杂，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，这些隐性知识也面临着完全丧失的风险。为了应对这些挑战，项目团队将研究在人工智能 (AI) 和虚拟/增强现实 (XR) 的帮助下，可以在多大程度上以及如何识别、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机械师的隐性知识以及如何将其应用到他们的工作中。它还将为识别隐性知识和基于 XR 的培训系统和评估指标的人工智能技术产生新的想法；这些将构成未来研究的基础，支持美国围绕为制造业劳动力做好准备的战略计划。该项目将扩大一个由来自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。拟议的研究围绕三个基本主旨构建：（1）将开发一个以人为对象的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隐性知识获取和利用的方式。（2）探索人工智能和XR技术来理解、识别和转移隐性知识。（3）建立认知模型来评估人类在XR训练中的表现以及训练的可信度和接受度。项目团队将特别关注开发想法，使目前在该领域代表性不足的人群更容易从事机械加工职业，并与附近一所向聋哑学生教授机械加工的学校合作。这项研究的总体目标是加深对加工隐性知识的理解，支持加工培训的新方向，同时开发可应用于加工以外领域培训的通用技术。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。