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

PFI：BIC 使用数字流程规划和 GPU 加速并行计算的下一代实时分布式制造服务系统

• 批准号: 1631803
• 负责人: Thomas Kurfess
• 金额: $ 100万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631803&HistoricalAwards=false
• 关键词: PFI; BIC; Next; Generation; Real

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.

该研究项目将使新型制造分析应用在智能、以人为本的制造服务系统中得以扩展。这种制造服务系统将使一系列用户之间的关系高度运作，包括服务消费者（例如，设计师、企业家、制造商）和服务生产者（例如，制造商）。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，原因是与以下方面相关的长期障碍：（1）先进制造平台的成本和可用性，以及（2）在正确的结构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，但制造功能组件的资本设备仍然主要位于私营企业设施中。此外，准确指定产品制造要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性制造的技术障碍，大大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获得更广泛的客户基础，通过分散服务需求促进生产能力的稳定。制造能力的民主化和相关的共享经济利益将导致美国经济在产品实现方面的变革性进步。为了促进这些合作，本研究项目利用先前的发现，基于新颖的混合动态树表示和图形处理单元（GPU）加速并行计算进行快速数字工艺规划。为了在分布式服务系统中实现这一新发现，数字化工艺规划方法将为每个用户类提供产品和工艺智能。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与生产者交互需求的系统设计提供信息。服务系统的结构将设计成与用户接口，方便用户交易，接收和整理输入数据，并启用档案功能。为了实现以人为中心的新型服务系统中的基础数字工艺规划发现，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。还将推导出高级计算算法，用于在多用户、基于云的环境中扩展GPU加速过程分析。全面的系统测试将为以人为本的广义制造服务系统的底层用户需求模型、服务系统架构、制造信息模型和GPU加速计算的有效性提供信息。该研究项目汇集了制造、设计、计算和心理学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。牵头机构是格鲁吉亚理工学院，教师来自机械工程、计算机和心理学。工业合作伙伴Tucker Innovations（小型企业）、Mazak（大型企业）和Morris South（大型企业）代表了制造技术开发商和平台构建商，他们有兴趣促进企业级技术向消费者终端用户的广泛访问。合作伙伴工业联盟，国家制造科学中心（非营利性），国家国防制造和加工中心（非营利性）和数字制造和设计创新研究所（非营利性），提供了跨越许多工业部门的强大会员基础。格鲁吉亚理工学院与教学增强中心、垂直整合项目计划和VentureLab的合作将促进研究成果与广大创客社区的教育成果之间的联系。

项目成果

Model-free subtractive manufacturing from computed tomography data

• DOI: 10.1016/j.mfglet.2017.06.004
• 发表时间: 2017-08
• 期刊: Manufacturing letters
• 影响因子: 3.9
• 作者: Jing Yu;Roby Lynn;Thomas M. Tucker;C. Saldana;T. Kurfess

Multi-Axis Voxel-Based CNC Machining of Centrifugal Compressor Assemblies

基于多轴体素的离心压缩机组件 CNC 加工

• 发表时间: 2018
• 期刊: American Helicopter Society Forum 74
• 作者: Kurfess, T.R.

Thin wall deposition of IN625 using directed energy deposition

• DOI: 10.1016/j.jmapro.2020.04.032
• 发表时间: 2020-08-01
• 期刊: JOURNAL OF MANUFACTURING PROCESSES
• 影响因子: 6.2
• 作者: Kim, Myong Joon;Saldana, Christopher
• 通讯作者: Saldana, Christopher

Effects of spatial energy distribution-induced porosity on mechanical properties of laser powder bed fusion 316L stainless steel

• DOI: 10.1016/j.addma.2021.101875
• 发表时间: 2021-03-01
• 期刊: ADDITIVE MANUFACTURING
• 影响因子: 11
• 作者: Jost, Elliott W.;Miers, John C.;Saldana, Christopher
• 通讯作者: Saldana, Christopher

Conceptualization and Design of a Low-Cost MTConnect-Enabled Refractometer for Coolant Health Monitoring

用于冷却剂健康监测的低成本 MTConnect 折光仪的概念化和设计

• DOI: 10.1115/msec2019-2755
• 发表时间: 2019
• 期刊: ASME 2019 14th International Manufacturing Science and Engineering Conference
• 作者: Feldhausen, Thomas;Hirani, Asimm;King, Walter;Lynn, Roby;Kurfess, Thomas
• 通讯作者: Kurfess, Thomas