PFI:BIC Next Generation Real-Time Distributed Manufacturing Service Systems Using Digital Process Planning and GPU-Accelerated Parallel Computing

该研究项目将在智能、以人为中心的制造服务系统中扩展新型制造分析应用。这种制造服务系统将使一系列用户，包括服务消费者（如设计师、企业家、制造商）和服务生产者（如制造商）之间的关系能够高度运作。由于长期存在的障碍，设计组织和新企业家获得创新所需的先进制造工艺的机会有限：(1)先进制造平台的成本和可用性；(2)在按结构正确的框架中设计可制造产品所需的技术技能和经验。虽然3D打印等直接打印模式增加了用户直接从数字产品设计中制造零件的能力，但制造功能部件的资本设备仍主要位于私营企业设施中。此外，准确指定产品制造需求的挑战仍然是促进大规模、消费者驱动的制造服务的障碍。解决这些持续挑战的智能制造服务系统将通过消除功能制造的技术障碍，极大地加速多个工业部门的创新。制造业服务的消费化还将使生产能力过剩的制造商能够接触到更广泛的客户群，通过分散服务需求促进生产能力的稳定。获得制造能力的民主化以及相关的共享经济利益将为美国经济带来产品实现方面的变革性进步。为了促进这些合作，该研究项目利用基于新型混合动态树表示和图形处理单元（GPU）加速并行计算的快速数字工艺规划的先前发现。为了在分布式服务系统中实现这一新发现，数字化流程规划方法将为每个用户类提供产品和流程智能。人类受试者实验和用户需求建模将为用户界面功能、服务消费者认知处理、服务提供者信息需求和消费者对生产者交互需求的系统设计提供信息。服务系统架构将被设计为与用户交互，方便用户交易，接收和塑造输入数据，并启用存档功能。为了在以人为中心的新型服务系统中实现基础性的数字化工艺规划发现，将建立制造相关数据和服务提供商生产能力的新信息模型。先进的计算算法也将衍生出来，用于在多用户、基于云的环境中扩展gpu加速的过程分析。全面的系统测试将告知底层用户需求模型、服务系统架构、制造信息模型和通用以人为中心的制造服务系统的gpu加速计算的有效性。该研究项目汇集了一个由制造、设计、计算和心理学领域的专家组成的协作团队，以及各种工业合作伙伴和非营利机构，以实现下一代制造服务系统。领头的机构是佐治亚理工学院，拥有机械工程、计算机和心理学的教员。工业合作伙伴Tucker Innovations（小型企业）、Mazak（大型企业）、Morris South（大型企业）代表了制造技术开发商和平台构建者，他们对促进企业级技术向消费者终端用户的广泛访问感兴趣。合作伙伴工业联盟，国家制造科学中心（非营利），国家国防制造与加工中心（非营利）和数字制造与设计创新研究所（非营利），提供了跨越许多工业部门的强大会员基础。佐治亚理工学院与教学促进中心、垂直整合项目计划和VentureLab的合作将促进研究成果与广泛的创客社区的教育成果之间的联系。

The research project will enable scaling of novel manufacturing analysis applications in a smart, human-centered manufacturing service system. This manufacturing service system will enable highly functioning relationships between a range of users, including service consumers (e.g., designers, entrepreneurs, makers) and service producers (e.g., manufacturers). Design organizations and new entrepreneurs have limited access to the advanced manufacturing processes needed for innovation due to longstanding barriers associated with: (1) cost and availability of advanced manufacturing platforms and (2) technical skill and experience needed to design manufacturable products in a correct-by-construction framework. While direct-to-print paradigms, such as 3D printing, have increased the capabilities for users to make parts directly from digital product designs, the capital equipment for manufacturing functional components are still housed in mostly private enterprise facilities. Additionally, challenges to accurately specify product manufacturing requirements remain as barriers toward facilitating large-scale, consumer-driven manufacturing services. Smart manufacturing service systems that address these persistent challenges will dramatically accelerate innovation across multiple industrial sectors by removing technological barriers for functional manufacture. The consumerization of manufacturing services will also enable manufacturers with excess production capacity to access a significantly broader customer base, facilitating stabilization of production capacity through decentralization of service demand. Democratization of access to manufacturing capabilities and the associated sharing economy benefits will lead to transformative advances in product realization for the US economy.To facilitate these collaborations, this research project leverages prior discovery for rapid, digital process planning based on novel hybrid dynamic tree representations and graphics processing unit (GPU) accelerated parallel computing. To implement this novel discovery in a distributed service system, the digital process planning method will provide product and process intelligence for each user class. Human subjects experiments and user requirements modeling will inform system design for user interface functionality, service consumer cognitive processing, service provider informational needs, and consumer-to-producer interaction requirements. The service system architecture will be designed to interface with users, facilitate user transactions, receive and shape input data, and enable archival functionality. To realize the foundational digital process planning discovery in a novel human-centered service system, new information models for manufacturing-related data and service provider production capabilities will be established. Advanced computational algorithms will also be derived for scaling GPU-accelerated process analyses in a many-user, cloud-based environment. Comprehensive system testing will inform efficacy of the underlying user requirement models, service system architecture, manufacturing information models and GPU-accelerated computing for generalized human-centered manufacturing service systems.The research project assembles a collaborative team of experts in the fields of manufacturing, design, computing and psychology and a diverse range of industrial partners and nonprofit agencies to realize a next-generation manufacturing service system. The lead institution is Georgia Tech with faculty from mechanical engineering, computing and psychology. The industrial partners, Tucker Innovations (small business), Mazak (large business), Morris South (large business), represent manufacturing technology developers and platform builders with interest in facilitating broad access of enterprise-level technologies to consumer end users. The partner industrial consortia, National Center for Manufacturing Sciences (non-profit), National Center for Defense Manufacturing and Machining (non-profit) and Digital Manufacturing And Design Innovation Institute (non-profit), provide access to a strong membership base spanning many industrial sectors. Collaborations at Georgia Tech with The Center for the Enhancement of Teaching and Learning, Vertically Integrated Projects Program and VentureLab will facilitate connections of research outcomes to educational outcomes for the broad community of makers.