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

模块化建筑是一种变革建筑业的革命性方法，与传统工艺相比，它在加快项目进度和降低成本方面有着既定的记录。然而，需要新的建筑能力来进行大规模的模块化建筑，该行业对熟练工人的依赖，这在制造工厂也是一个众所周知的挑战。该项目的重点是：(A)每个项目都是独一无二的，需要识别和处理零件的效率和准确性；(B)设计和生产线的变化很常见，需要对模块进行设计标准化和优化；(C)生产线在空间和时间上都很复杂，在准确处理设计和安装信息时需要工人的指导。本项目是在未来制造(FM)背景下研究模块化结构的独特尝试。它利用了人工智能/机器人/建筑信息建模和制造的交叉点上的机会，有可能增加模块化建筑的可扩展性。这项研究将开创初步方案，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-Robotic辅助的工艺来提高制造业的生产能力，(B)优化和标准化模块设计的可行性研究，并利用网络基础设施实现其标准化，(C)将网络基础设施作为形成学术界和行业伙伴关系的新颖方式的原型，以及为加快FM中数据驱动的适应模块化建设而建立的数据基础设施，以及(D)与一个为期两年的机构开展的协同活动，以培训和教育FM工作人员，了解FM和评估的技术的潜力。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，特别是面临类似挑战的重型制造业，如农业、采矿和造船。该项目将提高美国在生产方面的竞争力，支持经济增长，教育学生，并利用FM领导所需的技能影响劳动力对效率和准确性的行为。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。

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.