This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Preliminary Method of Delivering Engineering Design Heuristics
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
Annotation ability available
This paper argues the importance of engineering heuristics and introduces an educational data-driven tool to help novice engineers develop their engineering heuristics more effectively. The main objective in engineering practice is to identify opportunities for improvement and apply methods to effect change. Engineers do so by applying ‘how to’ knowledge to make decisions and take actions. This ‘how to’ knowledge is encoded in engineering heuristics.
In this paper, we describe a tool that aims to provide heuristic knowledge to users by giving them insight into heuristics applied by experts in similar situations. A repository of automotive data is transformed into a tool with powerful search and data visualization functionalities. The tool can be used to educate novice automotive engineers alongside the current resource intensive practices of teaching engineering heuristics through social methods such as an apprenticeship. The tool can do so by providing novices with powerful search and data visualization capabilities which will allow them to understand tradeoffs between vehicle attributes, to make assumptions from initial information, and to benchmark the vehicle design.
CitationHussain, M. and Paredis, C., "A Preliminary Method of Delivering Engineering Design Heuristics," SAE Technical Paper 2020-01-0741, 2020, https://doi.org/10.4271/2020-01-0741.
- Daly, S., Yilmaz, S., Seifert, C., and Gonzalez, R. , “AC 2010-1032: Cognitive Heuristics Use in Engineering Design Ideation,” Journal of Engineering Education, 2010.
- Yilmaz, S., Seifert, C., and Gonzalez, R. , “Cognitive Heuristics in Design: Instructional Strategies to Increase Creativity in Idea Generation,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(3):335-355, 2010, doi:10.1017/S0890060410000235.
- Fu, K.K., Yang, M.C., and Wood, K.L. , “Design Principles: Literature Review, Analysis, and Future Directions,” Journal of Mechanical Design 138(10):101-103, 2016, doi:10.1115/1.4034105.
- Lee, B.D. and Paredis, C.J.J. , “A Conceptual Framework for Value-Driven Design and Systems Engineering,” Procedia CIRP 21:10-17, 2014, doi:10.1016/j.procir.2014.06.147.
- Daly, S.R., Yilmaz, S., Christian, J.L., Seifert, C.M. et al. , “Design Heuristics in Engineering Concept Generation,” Journal of Engineering Education 101(4):601-629, 2012, doi:10.1002/j.2168-9830.2012.tb01121.x.
- Lee, B., Fillingim, K.B., Binder, W.R., Fu, K. et al. , “Design Heuristics: A Conceptual Framework and Preliminary Method for Extraction,” 2017.
- Cross, N. , “Expertise in Design: An Overview,” Design Studies 25(5):427-441, 2004, doi:10.1016/j.destud.2004.06.002.
- Dixon, R.A. , “Selected Core Thinking Skills and Cognitive Strategy of an Expert and Novice Engineer,” Journal of STEM Teacher Education 48(1):36-67, 2011, doi:10.30707/JSTE48.1Dixon.
- Malak, R.J. Jr., Aughenbaugh, J.M., and Paredis, C.J.J. , “Multi-Attribute Utility Analysis in Set-Based Conceptual Design,” Computer-Aided Design 41(3):214-227, 2009, doi:10.1016/j.cad.2008.06.004.
- Atman, C.J., Adams, R.S., Cardella, M.E., Turns, J. et al. , “Engineering Design Processes: A Comparison of Students and Expert Practitioners,” Journal of Engineering Education 96(4):359-379, 2007, doi:10.1002/j.2168-9830.2007.tb00945.x.
- Ahmed, S., Wallace, K.M., and Blessing, L.T. , “Understanding the Differences between how Novice and Experienced Designers Approach Design Tasks,” Research in Engineering Design 14(1):1-11, 2003, doi:10.1007/s00163-002-0023-z.
- Gerd, G. and Engel, C. , Heuristics and the Law (Cambridge, MA: MIT Press in cooperation with Dahlem University Press, 2006).
- Todd, P.M. and Gigerenzer, G. , Mechanisms of Ecological Rationality: Heuristics and Environments that Make (Oxford Handbook of Evolutionary Psychology, 2007).
- Curran, R., Verhagen, W.J.C., van Tooren, Michel, J.L. et al. , “A Multidisciplinary Implementation Methodology for Knowledge-Based Engineering: KNOMAD,” Expert Systems with Applications 37(11):7336-7350, 2010, doi:10.1016/j.eswa.2010.04.027.
- Yilmaz, S. and Seifert, C.M. , “Creativity through Design Heuristics: A Case Study of Expert Product Design,” Design Studies 32(4):384-415, 2011, doi:10.1016/j.destud.2011.01.003.
- Tversky, A. and Kahneman, D. , “Judgment under Uncertainty: Heuristics and Biases,” Science 185(4157):1124-1131, 1974.
- “Design Heuristics: Analysis and Synthesis from jet Propulsion Laboratory’s Architecture Team,” in ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2018.
- Chi, M.T.H. , “Two Approaches to the Study of Experts’ Characteristics,” In: Ericsson, K.A., Charness, N., Feltovich, P.J., and Hoffman, R.R., editors. The Cambridge Handbook of Expertise and Expert Performance, Cambridge University Press, 2006, 21-30.
- Björklund, T.A. , “Initial Mental Representations of Design Problems: Differences between Experts and Novices,” Design Studies 34(2):135-160, 2013, doi:10.1016/j.destud.2012.08.005.
- LaFrance, M. , “The Quality of Expertise: Implications of Expert-Novice Differences for Knowledge Acquisition,” ACM SIGART Bulletin 6-14, 1989, doi:10.1145/63266.63267.
- Heyworth, R.M. , “Procedural and Conceptual Knowledge of Expert and Novice Students for the Solving of a Basic Problem in Chemistry,” International Journal of Science Education 21(2):195-211, 1999, doi:10.1080/095006999290787.
- Dym, C.L. , “Design, Systems, and Engineering Education,” International Journal of Engineering Education 20(3):305-312, 2004.
- Dym, C.L., Agogino, A.M., Eris, O., Frey, D.D. et al. , “Engineering Design Thinking, Teaching, and Learning,” Journal of Engineering Education 94(1):103-120, 2005, doi:10.1002/j.2168-9830.2005.tb00832.x.
- Felder, R.M., Woods, D.R., Stice, J.E., and Rugarcia, A. , “The Future of Engineering Education II. Teaching Methods that Work,” Chemical Engineering Education 34(1):26-39, 2000.
- US Government - Office of Energy Efficiency & Renewable Energy , “Fuel Economy Data - Datasets for all Model Years,” https://www.fueleconomy.gov/feg/download.shtml, September 2019.
- US Government - Office of Energy Efficiency & Renewable Energy , FuelEconomy.gov Web Services, https://www.fueleconomy.gov/feg/ws/index.shtml#vehicle, September 2019.
- TrueCar Auto Buying Service , “TrueCar - New Cars,” https://www.truecar.com/.
- Publicly Available Sources , “AutoData.net - Wiki Automotive Catalog,” https://www.auto-data.net/en/.
- “Freebase, Google, and Open-Source-Community”, OpenRefine (3.2), https://openrefine.org/.
- Groves, A. , “Beyond Excel: How to Start Cleaning Data with OpenRefine,” Multimedia Information and Technology 42(2):18-22, 2016.
- Delbridge, R., Lowe, J., and Oliver, N. , “The Process of Benchmarking: A Study from the Automotive Industry,” International Journal of Operations & Production Management 15(4):50-62, 1995, doi:10.1108/01443579510083604.
- Hardman, S., Steinberger-Wilckens, R., and Horst, D.V.D. , “Disruptive Innovations: The Case for Hydrogen Fuel Cells and Battery Electric Vehicles,” International Journal of Hydrogen Energy 38(35):15438-15451, 2013, doi:10.1016/j.ijhydene.2013.09.088.
- Gao, P., Kaas, H., Mohr, D., and Wee, D. , “Disruptive Trends that Will Transform the Auto Industry,” McKinsey & Company 1:1-11, 2016.
- Linoff, G.S. , Data Analysis Using SQL and Excel (Indianapolis, IN: John Wiley & Sons, 2015).
- “Stanford Center for Biomedical Informatics Research and National Institute of General Medical Sciences,” Protégé (5.5.0), https://protege.stanford.edu/.