
Operationalizing Downstream Product Data for Improved Early Design Processes
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Funded by the National Science Foundation Award 1826469
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Project Summary:
The design of new products and systems often requires focused and tailored design approaches that improve specific aspects of product performance. Some of these design aspects of interest are design for improved environmental sustainability, design for reduced manufacturing complexity, and design for increased reliability. These methods, called Design-for-X (DfX) approaches, are typically applied at the end of a traditional design cycle, after a chosen concept has been detailed. In this sense, DfX objectives are traditionally considered after-the-fact additions to the design process, requiring potentially costly iteration and further design refinement. Even as more companies elect to use DFX techniques to meet evolving customer needs and improve the design of their products, engineering designers are currently unable to take into account how their early concept-generation decisions affect DfX attributes of the final product. This research will operationalize data from these downstream DfX approaches to create smarter early-design-phase design processes, resulting in more informed, higher quality, and faster design decisions.
The objective of this research is to establish the relationships between design decisions, product function, and downstream DfX data of interest, and to use these relationships as a foundation for new data-driven design processes, thus encouraging smarter design processes. Objectives will be achieved by enabling the assessment of product functionality, creating both a methodology for automating functional modeling, as well as generating functional data for all products in Oregon State University’s Design Engineering Lab Product Repository. When Research Thrust 2 is completed, designers will be able directly quantify the downstream impact of design decisions made early in the design process. This will reduce costly and time-consuming iteration and provide insight into design choices that would not have been otherwise known. Research Thrust 3 will test our method by using a Design for the Environment dataset, and assess how well providing informed, upfront design decisions will reduce downstream environmental impact. Through adherence to open science doctrine, resources will be made widely available to increase their potential impact, including open-access/publicly accessible journal and conference papers, digital media, repository access, and data and code sharing. This work will advance fundamental knowledge of design by developing and applying design automation techniques to overcome a significant shortcoming in this relatively new area of research: the lack of knowledge about how quantifiable information related to DfX impacts (e.g., sustainability metrics) can be used throughout the early design process.
People
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Lead PI: Bryony DuPont
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Vincenzo Ferrero, PhD Candidate
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Melissa Tensa, MS Student
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Mohammed Alkharashi, UG Research Assistant
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Katherine Edmonds, MS (2020)
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Alex Mikes, MS (2020)
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Naser Alqseer, BS (2020)
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Nicolas Soria Zurita, PhD (2019)
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Heather Miller, BS (2019)
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Quinn Smith, BS (2019)
* former project researchers are italicized
Publications (directly funded by or related to this project)
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Data mining a design repository to generate linear functional chains: a step toward automating functional modeling (Edmonds, Mikes, DuPont, and Stone; 2020. Design Computing and Cognition Conference (DCC). 14–16 DEC. Atlanta, GA, USA)
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A Weighted Confidence Metric to Improve Automated Functional Modeling (Edmonds, Mikes, DuPont, and Stone; 2020. International Design Engineering and Technical Conferences/Computers in Engineering Conference)
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Using Decision Trees Supported by Data Mining to Improve Function-Based Design (Ferrero, Alqseer, Tensa, and DuPont; 2020. International Design Engineering and Technical Conferences/Computers in Engineering Conference)
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Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling (Mikes, Edmonds, DuPont, and Stone; 2020. International Design Engineering and Technical Conferences/Computers in Engineering Conference)
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Validating the Sustainability of Eco-Labeled Products Using a Triple-Bottom-Line Analysis (Ferrero, Raman Shankar, Haapala, and DuPont; 2019. Smart and Sustainable Manufacturing Systems)
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Exploring the Effectiveness of Providing Structured DfE Design Strategies During Conceptual Design (Ross, Ferrero, and DuPont; 2019. in International Design Engineering and Technical Conferences/Computers in Engineering Conference)
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An Association Rule Approach for Identifying Physical System-User Interactions and Potential Human Error Using a Design Repository (Soria Zurita, Tensa, Ferrero, Stone, DuPont, Demirel, and ITumer.; 2019. “in International Design Engineering and Technical Conferences/Computers in Engineering Conference)
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Toward Automated Functional Modeling: An Association Rules Approach for Mining the Relationship between Product Components and Function (Tensa, Edmonds, Ferrero, Mikes, Soria Zurita, Stone, and DuPont; 2019. in International Conference on Engineering Design)
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Assessing the Impact of Product Use Variation on Environmental Sustainability (Tensa, Ferrero, Ross, and DuPont; 2018. ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)
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Understanding the Sustainability of Eco-Labeled Products When Compared to Conventional Alternatives (Ferrero, Shankar Raman, DuPont, and Haapala; 2017. in ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)
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Quantifying the Impact of Sustainable Product Design Decisions in the Early Design Phase through Machine Learning (Wisthoff, Ferrero, Huynh, and DuPont. 2016. Pp. 1-10 in ASME International Design Engineering Technical Conference and Computers and Information in Engineering Conference)
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A Method for Understanding Sustainable Design Trade-Offs During the Early Design Phase (Wisthoff & DuPont; 2016. in KES Sustainable Design and Manufacturing Conference)