Scott Ferguson

Associate Professor

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Dr. Ferguson’s research answers questions about optimal product variety and optimal system architecture. His team conducts cutting-edge research into how product/system value can be maximized by modeling market-driven environments and considering uncertain and/or conflicting future requirements. This is done by applying design thinking principles and recognizing that design is inherently a decision-making process. The products of this research advance design automation and help engineers navigate the interdisciplinary challenges associated with designing consumer products and complex engineered systems.

 

Engineering design requires the incorporation of interdisciplinary perspectives, creating solutions by synthesizing various components, dealing with uncertainty, and considering the unintended consequences of design decisions. Dr. Ferguson’s research is motivated by the fact that even the most basic engineering design decision requires a value analysis. Fundamental questions driving this work include:

  1. What information do product engineers need for making value-driven design decisions in a market-systems context?
  2. How we we maximize the user experience with a product/system?
  3. What is the right mix of products that should be offered?
  4. What is the relationship between system configuration/architecture and needs/preferences that change over time?
  5. How do we effectively manage change after a product/system has been fielded?

 

Dr. Ferguson and his team draw upon research advances in design automation, multiobjective / multidisciplinary optimization, customer preference modeling techniques (such as conjoint analysis and discrete choice theory), data analytics (including AI/ML), product customization, requirements definition, system architecture modeling, and change propagation techniques. Currently, the lab pursues four primary research directions:

  1. Preference/demand modeling, product platforming, and product customization/personalization
  2. Designing complex engineered systems capable of reconfigurability, evolvability and resilience
  3. Inverse engineering design using machine learning and AI
  4. Advancing engineering design education

 

To learn more about this work, please visit his lab website: System Design Optimization Laboratory.

 

Dr. Ferguson arrived at NC State in the Fall of 2008 and has taught undergraduate courses in Dynamics (MAE 208), Introduction to Aerospace Engineering (MAE 250), Aerospace Vehicle Performance (MAE 251), ME Capstone Design (MAE 416), Fundamentals of Product Design (MAE 426), and Introduction to Space Flight (MAE 467). He also teaches graduate sections of Fundamentals of Product Design (MAE 526) and created Engineering Design Optimization (MAE 531).

 

Education

Ph.D. 2008

Mechanical Engineering

University at Buffalo, State University of New York

M.S. 2004

Mechanical Engineering

University at Buffalo, State University of New York

B.S. 2002

Aerospace Engineering

University at Buffalo, State University of New York

B.S. 2002

Mechanical Engineering

University at Buffalo, State University of New York

Research Description

Dr. Ferguson’s research in the area of engineering design focuses on market-based product design and the design of complex engineered systems. His contributions toward technical feasibility modeling and product line optimization have been adopted by his industrial collaborators and have received interest from market research software companies in this area. Dr. Ferguson’s research has also helped define the area of reconfigurable system design, advanced understanding of how such configuration changes can mitigate unexpected system behavior, and studied how the information presented to designers can facilitate tradeoff decisions in the presence of multiple objectives.

Honors and Awards

  • ASEE New Mechanical Engineering Educator Award, 2015
  • ASME Design Automation Young Investigator Award, 2014
  • NC State Outstanding Teacher Award, 2012
  • National Science Foundation CAREER Award, 2011

Publications

A case study of evolvability and excess on the B-52 stratofortress and FA-18 hornet
Long, D., & Ferguson, S. (2017), Proceedings of the asme international design engineering technical conferences and computers and information in engineering conference, 2017, vol 4. https://doi.org/10.1115/detc2017-68287
Design for excess capability to handle uncertain product requirements in a developing world setting
Allen, J. D., Mattson, C. A., Thacker, K. S., & Ferguson, S. M. (2017), Research in Engineering Design, 28(4), 511–527. https://doi.org/10.1007/s00163-017-0253-8
Exploring architecture selection and system evolvability
White, S., & Ferguson, S. (2017), Proceedings of the asme international design engineering technical conferences and computers and information in engineering conference, 2017, vol 2b. https://doi.org/10.1115/detc2017-68290
Exploring how optimal composite design is influenced by model fidelity and multiple objectives
Joglekar, S., Von Hagel, K., Pankow, M., & Ferguson, S. (2017), Composite Structures, 160, 964–975. https://doi.org/10.1016/j.compstruct.2016.10.089
Bubble tracking simulations of turbulent two-phase flows
Fang, J., & Bolotnov, I. A. (2016), Proceedings of the asme fluids engineering division summer meeting, 2016, vol 1b. https://doi.org/10.1115/fedsm2016-1005
Design optimization and analysis of a prescribed vibration system
Malinga, B., Ferguson, S. M., & Buckner, G. D. (2016), Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2016, Vol 2b, 353–360. https://doi.org/10.1115/detc2016-60096
Evaluation of system evolvability based on usable excess
Allen, J. D., Mattson, C. A., & Ferguson, S. M. (2016), Journal of Mechanical Design (New York, N.Y. : 1990), 138(9). https://doi.org/10.1115/1.4033989
Excess identification and mapping in engineered systems
Cansler, E. Z., White, S. B., Ferguson, S. M., & Mattson, C. A. (2016), Journal of Mechanical Design (New York, N.Y. : 1990), 138(8). https://doi.org/10.1115/1.4033884
Exploring the relationship between excess and system evolutions using a stress-test
Cansler, E. Z., Ferguson, S. M., & Mattson, C. A. (2016), International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2015, vol 7. https://doi.org/10.1115/detc2015-47603
Modeling noncompensatory choices with a compensatory model for a product design search
Shin, J., & Ferguson, S. (2016), International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2015, vol 2A. https://doi.org/10.1115/detc2015-47632

View all publications via NC State Libraries

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Grants

Impact Protection Design Solutions Using AI/Neural Networks
Windpact(10/01/19 - 6/30/20)
Algorithm optimization and tuning for Under Armour
Under Armour(11/15/18 - 6/28/19)
UAS Roadmap
NC Department of Transportation(8/01/17 - 7/31/19)
Powering Energy Efficiency & Impacts Framework: Mapping a comprehensive energy strategy for the Upper Coastal Plain Council of Governments Region
US Dept. of Energy (DOE) - Energy Efficiency & Renewable Energy (EERE)(10/01/16 - 12/31/18)
Workshop: NSF CAREER Writing Workshop for Early Career Professionals; Charlotte, NC; August 23, 2016
National Science Foundation (NSF)(8/01/16 - 7/31/17)
CAD Apps for Core Courses in AE and ME Curricula: Bridge Funding 
The MathWorks, Inc.(1/01/16 - 5/31/17)
Building a Business Case for UAS Use in Public Power Operations
American Public Power Association(12/16/14 - 11/30/16)
Transfer Spike Analysis and Redesign
Neogen(12/02/13 - 3/31/14)
Collaborative Research: Mitigating Emergent System Behavior through System Evolvability
National Science Foundation (NSF)(6/01/13 - 5/31/17)
Transforming Teaching Through Implementing Inquiry (T2I2)
National Science Foundation (NSF)(11/30/-1 - 7/31/16)