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

3D打印，也称为增材制造，为个人和公司提供了设计和生产定制零件的可能性，这些零件的数量小到一个单位。 然而，与传统的大规模生产不同，生产机器通过定期测量生产的零件来调整以保持质量，3D打印中生产的小批量不提供以相同方式调整生产机器的机会。 EARLY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零件的经验调整3D打印机的生产质量。 这种工具可以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。工程过程之间知识转移的根本障碍在于潜在变量，这些变量是由于测量不可行或知识不足而无法观察到的过程变量。该项目将根据可观察的控制变量对潜在变量的影响进行建模和缓解，以实现增材制造系统的新型灰箱模型转移策略。 研究任务包括：（1）建立效应等效的理论公式，以量化潜在变量的效应;（2）探索学习效应等效的统计基础;（3）验证效应等效模型及其鲁棒性;（4）从效应等效中获得工程见解并开发多分辨率测量策略。该项目预计将产生工程效果等效的数学公式，演示预测增材制造机器构建的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。

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.