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FMSG: ARM4MOD: AI-powered and Robot-assisted Manufacturing for Modular Construction

FMSG：ARM4MOD：用于模块化结构的人工智能和机器人辅助制造

基本信息

批准号：
2036870
负责人：
Semiha Ergan
金额：
$ 49.97万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036870&HistoricalAwards=false
关键词：
FMSG ARM4MOD AI powered Robot

项目摘要

Modular construction is a revolutionary way to transform the construction industry with established records of accelerating projects and reducing costs as compared to the traditional processes. However, new construction capabilities are needed to perform modular construction at scale, where the industry suffers from the dependency on skilled labors, which is a well-acknowledged challenge at manufacturing factories as well. This project focuses on the facts that (a) every project is unique and necessitates efficiency and accuracy in recognition and handling workpieces, (b) design and production line changes are common, and necessitate design standardization and optimization of modules, and (c) production lines are complex in space and time, and necessitate the guidance of workers while processing design and installation information accurately.This project is a unique attempt in studying modular construction within the context of Future Manufacturing (FM). It exploits opportunities at the intersection of AI/robotics/building information modeling and manufacturing, with the potential to increase the scalability of modular construction. This research will pioneer initial formulations to enable (a) high throughput in manufacturing through the definition and evaluation of processes that embrace real-time workpiece semantic grounding and in-situ AR-robotic assistance, (b) feasibility studies of optimizing and standardizing the design of modules, and utilization of a cyberinfrastructure for their standardization, (c) prototyping cyberinfrastructures as both novel ways of forming academia and industry partnerships, and data infrastructures to accelerate data-driven adaption in FM for modular construction, and (d) synergistic activities with a two-year institution to train and educate FM workforce for the potential of FM and technologies evaluated. While the evaluations of technologies will focus on the modular construction, the proposed technologies will improve the competitiveness of manufacturing industries, particularly heavy manufacturing industries that share similar challenges such as agricultural, mining, and ship building. The project will enhance the US competitiveness in production, bolster economic growth, educate students, and influence workforce behavior towards efficiency and accuracy with the skills required for leadership in FM.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.

模块化施工是一种革命性的方式来改变建筑行业，与传统工艺相比，它在加速项目和降低成本方面取得了良好的记录。然而，需要新的建造能力来大规模进行模块化建造，该行业依赖熟练劳动力，这也是制造工厂公认的挑战。本项目的重点在于以下事实：（a）每个项目都是独特的，需要在识别和处理工件方面的效率和准确性，（B）设计和生产线的变化是常见的，需要设计标准化和模块优化，以及（c）生产线在空间和时间上是复杂的，本项目是对模块化建筑研究的一次独特尝试，未来制造（FM）。它利用了人工智能/机器人/建筑信息建模和制造交叉点的机会，有可能提高模块化建筑的可扩展性。这项研究将开创初始配方，以实现（a）通过定义和评估包含实时工件语义基础和现场AR机器人辅助的过程来实现制造的高吞吐量，（B）优化和标准化模块设计的可行性研究，以及利用网络基础设施进行标准化，（c）将网络基础设施原型化，既作为建立学术界和工业界伙伴关系的新方法，又作为数据基础设施，以加速FM中数据驱动的模块化建设，（d）与一个为期两年的机构开展协同活动，培训和教育森林管理工作人员，使其了解森林管理的潜力和所评估的技术。虽然对技术的评估将集中在模块化结构上，但拟议的技术将提高制造业的竞争力，特别是农业、采矿和造船等面临类似挑战的重工业。该项目将提高美国在生产方面的竞争力，促进经济增长，教育学生，并通过FM领导所需的技能影响劳动力行为的效率和准确性。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。