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This paper summarizes the outcome of an evaluation by the Formula and BAJA Society of Automotive Engineers (SAE) student design teams from the Oregon Institute of Technology of the Ecosystem for Design Assessment and Verification. The Ecosystem is a design decision support tool whose main goal is to identify design oversights early in the design process, guide designers through the design process, and foster high-quality design practices. The Ecosystem offers many features attractive to Formula and BAJA student design teams. It is capable of capturing and archiving design work, including the rationale for design decisions, and making searchable. It enables new design teams to rapidly get up-to-speed, and helps with knowledge transfer. In the case of Formula or BAJA SAE, the Ecosystem allows the design teams to quickly acquaint themselves with the work left behind by previous student teams, such as the opportunities for improvement identified.

This paper summarizes the outcome of an evaluation by capstone design teams from the Department of Mechanical and Materials Engineering at the University of Nebraska-Lincoln, of the Ecosystem for Design Assessment and Verification. The Ecosystem is a design decision support tool whose main goal is to identify design oversights, defined in terms of deviations from the design process or unfulfilled design requirements, early in the design process, guide designers through the design process, and teach proper design techniques. It is capable of automatically assessing students’ design work against ABET compliant learning outcomes. The Ecosystem offers many additional features found useful by capstone design teams, such as automatic generation of formatted project reports as well as interfaces to tools for team communications (Google Drive, Dropbox or OneDrive) or development (e.g., SolidWorks, CATIA, NX Unigraphics or AutoCAD).

This paper presents a high-level framework (vision) for utilizing big data analytics to harvest repositories of known good designs for the purpose of aiding mechanical product designs. The paper outlines a novel approach for applying artificial intelligence (AI) to the training of a mechanical design system model, assimilates the definition of meta-data for design containers (binders) to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with hierarchical structure. Design information can be harvested, for the purpose of improving design decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented.

Modern mechanical design is heavily supplemented by computer-aided design and engineering (CAD/CAE) tools. The predominance of these tools have been developed to augment the analysis efforts during the detailed phase of the design process. Yet, many design oversights and inefficiencies are the result of inadequate vetting of engineering requirements, and vague accountability to those requirements during conceptual design. The Ecosystem for Engineering Design is developed herein as an immersive CAE tool for comprehensive design process support that facilitates the elimination of these sources of design inefficiency. In addition, the Ecosystem promotes rigid adherence to phase-appropriate design process activities increasing productivity. Many time-consuming administrative and information management tasks are automated to further increase designer efficiency.