EAGER/Collaborative Research: Explore the Theoretical Framework of Engineering Knowledge Transfer in Cybermanufacturing Systems

EAGER/协作研究：探索网络制造系统中工程知识转移的理论框架

• 批准号: 1833195
• 负责人: Matthew Plumlee
• 金额: $ 2.99万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1833195&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Explore; Theoretical

3D Printing, also known as additive manufacturing, offers the potential for individuals and companies to design and produce customized parts in quantities as small as a single unit. However, unlike conventional mass production, for which production machines are adjusted to maintain quality by periodically measuring the parts that are produced, the small lots produced in 3D Printing do not provide the opportunity to tune the production machines in the same way. This EArly-concept Grant for Exploratory Research (EAGER) will investigate a statistical method for adjusting the production quality of a 3D Printing machine based on experience in producing parts of different part types. Such a tool has the capability to be offered to manufacturers as a cloud-based utility, with possible extension to aggregating data from similar machines at different sites.The fundamental barrier to knowledge transfer between engineering processes lies in lurking variables, which are process variables that are unobserved due to infeasibility of measurement or insufficient knowledge. The project will model and mitigate the effects of lurking variables in terms of observable control variables to enable a novel gray-box model transfer strategy for additive manufacturing systems. The proposed research tasks include: (1) establishing a theoretical formulation of effect equivalence to quantify effects of lurking variables, (2) exploring a statistical foundation for learning effect equivalence, (3) verifying effect equivalence models and their robustness, and (4) obtaining engineering insight from effect equivalence and developing a multi-resolution measurement strategy. The project is expected to produce a mathematical formulation of engineering effect equivalence, demonstrate the formulation in predicting geometric shape deformation of 3D products built by additive manufacturing machines, and a multi-resolution measurement strategy for equivalence-based additive manufacturing process control.

3D 打印，也称为增材制造，为个人和公司提供了设计和生产小至单个单元的定制零件的潜力。 然而，与传统的大规模生产不同的是，传统的大规模生产通过定期测量生产的零件来调整生产机器以保持质量，而 3D 打印中生产的小批量生产则无法以同样的方式调整生产机器。 这项早期概念探索性研究资助 (EAGER) 将研究一种统计方法，用于根据生产不同零件类型的零件的经验来调整 3D 打印机的生产质量。 这种工具能够作为基于云的实用程序提供给制造商，并可能扩展到聚合来自不同站点的类似机器的数据。工程流程之间知识转移的根本障碍在于潜伏变量，这些变量是由于测量不可行或知识不足而未被观察到。该项目将根据可观察控制变量对潜伏变量的影响进行建模并减轻其影响，从而为增材制造系统提供一种新颖的灰盒模型转移策略。 拟议的研究任务包括：（1）建立效果等价的理论公式来量化潜伏变量的影响，（2）探索学习效果等价的统计基础，（3）验证效果等价模型及其稳健性，以及（4）从效应等效中获得工程洞察并开发多分辨率测量策略。该项目预计将产生工程效应等效的数学公式，演示预测增材制造机器制造的3D产品几何形状变形的公式，以及基于等效的增材制造过程控制的多分辨率测量策略。