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Faculty of Graduate Studies

Omar Alrubie

  • BSc (Al-Balqa Applied University, 2022)

Notice of the Final Oral Examination for the Degree of Master of Applied Science

Topic

Automated Robotic Assembly of MEMS 3D Structures

Department of Mechanical Engineering

Date & location

  • Tuesday, August 25, 2026

  • 10:00 A.M.

  • Engineering Office Wing

  • Room 430

Reviewers

Supervisory Committee

  • Dr. Nick Dechev, Department of Mechanical Engineering, University of Victoria (Co-Supervisor)

  • Dr. Mohamed Bahsa, Department of Mechanical Engineering, UVic (Co-Supervisor) 

External Examiner

  • Dr. Daler Rakhmatov, Department of Electrical and Computer Engineering, University of Victoria 

Chair of Oral Examination

  • Dr. Alejandro Sinner, Department of Greek and Roman Studies, UVic 

Abstract

Micro-Electro-Mechanical Systems (MEMS) are microscale devices used in sensing, actuation, and biomedical applications. They are commonly fabricated using surface micromachining, which produces flat, planar structures. Creating three-dimensional MEMS structures therefore requires a post-fabrication assembly process that is typically performed manually under a microscope, making it time-consuming and dependent on operator skill. This work focuses on automating the grasping and joining operations required for MEMS microassembly using a five-degree-of-freedom (5-DOF) robotic platform. The robotic platform was enhanced with an added independent camera Z-stage and a secondary camera system positioned at a 30⁰ angle to the chip surface, providing visual feedback from two perspectives during microassembly. The feedback system utilized machine vision methodologies which included normalized cross-correlation (NCC) template-matching for object detection, a Laplacian-based autofocus algorithm for identifying the camera position along the Z-axis, and a brightness measure analysis for detecting tilting of micro-components during operation. Control algorithms were developed using the machine vision methods and conditional statements for grasping, manipulating, and joining MEMS microparts. The automated grasping and joining operations were both experimentally validated through five trials each, achieving an observed success rate of 100% for both operations under the tested conditions. The grasping trials had an average run-time of 3 minutes and 4 seconds, while the joining trials had an average run-time of 5 minutes and 5 seconds. Overall, these results demonstrate the efficiency, reliability, and accuracy of the automated robotic microassembly system with reduced manual operation.

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