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Overview of Innovation, Design Reasoning, Engineering Education, and Methods Lab

Overview of Innovation, Design Reasoning, Engineering Education, and Methods Lab. Julie Linsey Innovation, Design Reasoning, Engineering Education and Methods Lab Woodruff School of Mechanical Engineering. Design Cognition in Engineering. Design cognition Idea generation

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Overview of Innovation, Design Reasoning, Engineering Education, and Methods Lab

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  1. Overview of Innovation, Design Reasoning, Engineering Education, and Methods Lab Julie Linsey Innovation, Design Reasoning, Engineering Education and Methods Lab Woodruff School of Mechanical Engineering

  2. Design Cognition in Engineering • Design cognition • Idea generation • Design by analogy • Bioinspired design • Human behavior in the design process • Mental Models • New tools and design methods

  3. Cognitive Impacts of Physical Representations • Motivation- Designers often use physical models in idea generation and throughout the design process – more needs to be know about the cognitive impact Three different physical models used in the development of the orange juicer (IDSA, 2003) NSF CMMI-1322335 • Findings: • Physical models helps designers to overcome negative design fixation to ineffective concepts • Physical representations early in the design process must minimizes costs (time, efforts, money, etc.) otherwise design fixation tends to occur due to the Sunk Cost effect • Physical representation assist designers in overcoming flaws in their mental models of how a system works and improves the quality of the design

  4. Current Work Design by Analogy: Meta-Analogy by Performance Specification (MAPS) Illustration of what MAPS output might look like for a set of analogues. Proposed overview of MAPS Process. MAPS is in development Collaborative Project with Prof. Cameron Turner (Colorado School of Mines) NSF CMMI- 1304383 Goal: Retrieve appropriate analogues based on both performance specifications and function. Current computer tools only use function

  5. Bioinspired Design Methods’ Impact NSF EEC- 1025155 Co-PIs McAdams & McTigue • Measure how engineering creativity changes from freshman to seniors • Determining the impact of different Bioinspired Methods • Bio TRIZ • Function-based bioinspired design • Case studies

  6. Impact of Maker Spaces! • We believe university maker spaces: • increase student retention • improve students’ engineering innovation skills • increase design confidence • improve deep technical knowledge of engineering Objectives: Determine the impact of the maker spaces on students Identify best practices and novel approaches for maker spaces.

  7. Studying Complex Systems Engineering Research Question: How do physical representations impact design cognition during early concept generation? Hypothesis Controlled Experiment Mental Model Hypothesis Triangulation of Results Qualitative Study- Grad team Fixation Hypothesis Qualitative Existing Project Sunk Cost Hypothesis • Viswanathan, V., and Linsey, J., 2014, “Spanning the Complexity Chasm: A Research Method to Move from Simple to Complex Engineering Systems,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM)- Special Issue on Complex Systems, 28(4), accepted. As Green As It Gets, 2011

  8. Other Possible Applications of My Research Experts predictions of design consequences Eliciting analogies used for making choices Evaluation of inferences based on analogical reasoning

  9. Thank-you.Questions? Julie Linsey Assistant Professor Georgia Institute of Technology julie.linsey@me.gatech.edu https://sites.google.com/site/idreemlaboratory/ http://www.me.gatech.edu/faculty/linsey • Acknowledgements • National Science Foundation: CMMI-1000954, DUE-0942400,

  10. Study Approaches Controlled Experiments or Quasi-Experimental Designs Mixed Methods Survey and Interviews Empirical Product Studies of Existing Documents (qualitative)

  11. Types of Research Activities the IDREEM Lab Focuses on Developing new methods to support innovation/invention. Which approach or method is best? Quantifying early phase design outcomes. Identification of Design Principles (e.g., DFM, DFX)

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