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

Purdue University, West Lafayette IN

Investigators

Abstract

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.