Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling
| Autor: | Guhaprasanna Manogharan, Priyesh Mehta, Catherine G.P. Berdanier, Manoj Malviya, Christopher McComb |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Mechanical Engineering 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Computer Graphics and Computer-Aided Design Computer Science Applications 020901 industrial engineering & automation Mechanics of Materials Eye tracking Artificial intelligence Heuristics Hidden Markov model business computer 0105 earth and related environmental sciences |
| Zdroj: | Journal of Mechanical Design. 142 |
| ISSN: | 1528-9001 1050-0472 |
| Popis: | In this research, we collected eye-tracking data from nine engineering graduate students as they redesigned a traditionally manufactured part for additive manufacturing (AM). Final artifacts were assessed for manufacturability and quality of final design, and design behaviors were captured via the eye-tracking data. Statistical analysis of design behavior duration shows that participants with more than 3 years of industry experience spend significantly less time removing material and revising than those with less experience. Hidden Markov modeling (HMM) analysis of the design behaviors gives insight to the transitions between behaviors through which designers proceed. Findings show that high-performing designers proceeded through four behavioral states, smoothly transitioning between states. In contrast, low-performing designers roughly transitioned between states, with moderate transition probabilities back and forth between multiple states. |
| Databáze: | OpenAIRE |
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