Eager: characterizing design knowledge generation during prototype testing for advanced manufacturing and AI/ML-enabled product innovation

The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded the Regents of the University of Minnesota $199,601 on January 1, 2027, under the Engineering program (CFDA 47.041) to characterize how prototype testing sessions generate actionable design knowledge during early-stage product innovation.

The research establishes an empirical framework linking prototype testing practices to the type, depth, and impact of design knowledge generated during conceptual design, with application to advanced medical device development and broader complex product innovation challenges shaped by advanced manufacturing and AI/ML-enabled design support. The project builds on an ongoing industry study of up to 45 prototype testing sessions across five medical device projects. Data collection includes observations of authentic prototype testing sessions, post-session interviews with designers and engineers, session impact surveys, and longitudinal design documentation including evolving requirements. The work addresses the gap in evidence-based guidance for planning prototype testing sessions; poorly aligned testing can cause engineering teams to miss critical stakeholder insights, make weak assumptions about manufacturability or feasibility, and carry uncertainty into later development stages.

Performance occurs at Minneapolis, Minnesota with a period of performance from January 1, 2027 through December 31, 2028. The award is classified as a Project Grant, an assistance type supporting discrete, time-limited research efforts.

Abstract

The objective of this early-concept grant for exploratory research (EAGER) project is to characterize how prototype testing sessions generate actionable design knowledge during early-stage product innovation. Engineering teams need better ways to learn during early-stage design to support advanced manufacturing and complex product innovation. Prototype testing is widely used to evaluate assumptions, engage stakeholders, and guide design decisions before product architectures, requirements, materials, and manufacturing pathways become costly to change. Yet teams often lack evidence-based guidance for planning prototype testing sessions that generate useful knowledge. When testing is poorly aligned with the questions a team needs to answer, designers can miss critical stakeholder insights, make weak assumptions about manufacturability or feasibility, and carry uncertainty into later stages of development. This project is carried out in the context of advanced medical device development, where teams must coordinate stakeholder needs, usability, technical feasibility, manufacturability, regulatory constraints, and evolving requirements. Insights from this context will inform broader complex product innovation challenges shaped by advanced manufacturing and future AI/ML-enabled design support. The research specifically establishes an empirical framework for linking prototype testing practices to the type, depth, and impact of design knowledge generated during conceptual design. The project builds on an ongoing industry study of up to 45 prototype testing sessions across five medical device projects. Data sources include observations of authentic prototype testing sessions, post-session interviews with designers and engineers, session impact surveys, and longitudinal design documentation, including evolving requirements. The study will examine how testing strategies, prototype attributes, stakeholder interactions, session structure, and manufacturing-related considerations influence what designers learn and how that learning shapes subsequent design decisions. The project will also develop an AI-ready schema that represents session goals, prototype attributes, testing strategies, stakeholder interactions, manufacturing considerations, learning outcomes, and downstream design changes. Expected outcomes include empirical evidence about how prototype testing generates actionable design knowledge, a methodological framework for studying knowledge generation in engineering design, practical guidance for prototype testing in industry and education, and a foundation for future human-in-the-loop AI/ML-enabled tools that support prototype testing planning. Non-confidential findings will inform educational materials such as prototype testing case studies, planning templates, decision guides, and examples of effective testing strategies derived from professional practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.