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research-article

Mining Process Heuristics from Designer Action Data via Hidden Markov Models

[+] Author and Article Information
Christopher McComb

ASME Member, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA
ccm@cmu.edu

Jonathan Cagan

ASME Fellow, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA
cagan@cmu.edu

Kenneth Kotovsky

Department of Psychology, Carnegie Mellon University, Pittsburgh, PA
kotovsky@cmu.edu

1Corresponding author.

ASME doi:10.1115/1.4037308 History: Received February 20, 2017; Revised June 05, 2017

Abstract

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a more effective and nuanced search for solutions.

Copyright (c) 2017 by ASME
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