DOI

10.17077/etd.9k5zfnv3

Document Type

Dissertation

Date of Degree

Fall 2017

Access Restrictions

Access restricted until 01/31/2020

Degree Name

PhD (Doctor of Philosophy)

Degree In

Industrial Engineering

First Advisor

Pennathur, Priyadarshini R.

Second Advisor

Thomas, Geb W.

First Committee Member

McGehee, Daniel V.

Second Committee Member

Hollingworth, Andrew

Third Committee Member

Kreiter, Clarence D.

Abstract

Today’s assembly operations represent about 15-70% of all manufacturing time and about 40% of all manufacturing costs, and manual assembly processes are still a significant portion of today’s assembly operations. Furthermore, today’s manufacturing environment requires a well-trained and flexible workforce that can easily adapt to changing products and processes. Unfortunately, manufacturing training is often performed using the master-apprentice model in the assembly line resulting in unsafe and expensive training conditions as this model is a slow and expensive process. Previous research has considered the use of virtual environments (VEs) for training purposes in different fields such as aviation, driving, construction, medicine, and manufacturing among many others. However, to this date, no assembly studies have been successful in providing a positive transfer of knowledge between virtual environments and real environments.

On the other hand, several eye-tracking studies in radiology, air-traffic control, driving, and reading show that participants with higher levels of experience have different eye-scan patterns than participants with lower levels of experience. However, it is unknown how visual scans are affected by practice. Furthermore, several empirical visuomotor studies of task-oriented processes in real environments show that observers fixated their eyes on the areas that are crucial to the required task. However, we do not know the necessary visual elements to observe when performing and when learning how to perform an assembly task, nor the effects of following visual instructions and having visual distractors during this process. Finally, we have yet to establish what observation differences may exist between real and virtual environments with regards to these unknowns.

This work presents the results of an assembly task which required participants to follow visual instructions and to select assembly objects among similar distractors. This assembly task was performed for ten cycles in real and virtual environments, and we used an eye-tracking device to register participants’ visual scans. We successfully identified the areas that are needed to observe for an assembly task in both environments and the effect of visual instructions and distractors in a visual scan. We found statistically significant differences for visual scans by assembly cycle and environment, with a p-value of <0.05. We also identified a connection between learning curves and participant eye scan, showing a significant decrease in the incidence of eye tracking metrics (visit count, visit duration, fixation count and fixation duration) between the first and the tenth cycles (ΔΜ), particularly for visual distractors ranging from 37.36% to 48.77%, and for visual instructions ranging from 35.17% to 54.82%. We found that participants’ observations became more efficient with practice, not only in terms of identifying distractors and following visual instructions but also in terms of developing an ability to observe key visual elements. For the RE we found a positive Pearson correlation between the proportion of fixation duration and assembly cycle for the key visual areas with p-values<0.002 and a negative Pearson correlation between the proportion of fixation duration for the non-key visual areas with p-values<0.046. Similar results were obtained for the VE.

Keywords

Assembly eye tracking, Eye Tracking, Human Factors, Virtual and Real Assembly, Virtual Reality Eye tracking, Visual Cognition

Pages

xiii, 92 pages

Bibliography

Includes bibliographical references (pages 83-92).

Copyright

Copyright © 2017 Salvador Rojas-Murillo

Available for download on Friday, January 31, 2020

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