Masters Theses


Ze-Hao Lai

Keywords and Phrases

Augmented reality; Deep learning; Internet of things; Machine learning; Mechanical assembly; Smart manufacturing


"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is trained on a synthetic tool dataset. The dataset is generated using CAD models of tools augmented onto a 2D scene without the need of manually preparing real tool images. By implementing the system to mechanical assembly of a CNC carving machine, the result has shown that the system is not only able to correctly classify and localize the physical tools but also enables workers to successfully complete the given assembly tasks. With the proposed approaches, an efficiently customizable smart AR instructional system capable of sensing, characterizing the requirements, and enhancing worker's performance effectively has been built and demonstrated"--Abstract, page iii.


Leu, M. C. (Ming-Chuan)

Committee Member(s)

Yin, Zhaozheng
Qin, Ruwen


Mechanical and Aerospace Engineering

Degree Name

M.S. in Manufacturing Engineering


National Science Foundation (U.S. )
Missouri University of Science and Technology Intelligent Systems Center


The author acknowledges and thanks all the funding sources granted from National Science Foundation CMMI-1646162 and the Intelligent Systems Center at Missouri University of Science and Technology.

Research Center/Lab(s)

Intelligent Systems Center


Missouri University of Science and Technology

Publication Date

Fall 2018


ix, 52 pages

Note about bibliography

Includes bibliographical references (pages 48-51).


© 2018 Ze-Hao Lai, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11428

Electronic OCLC #