College

College of Engineering and Polymer Science

Date of Last Revision

2026-05-06 06:30:50

Major

Mechanical Engineering

Honors Course

MECE 497

Number of Credits

3

Degree Name

Bachelor of Science

Date of Expected Graduation

Spring 2026

Abstract

For this project, an external company reached out to the University of Akron requesting assistance with defect detection during their vertical turning operations. As babbitt is removed in a vertical turning process, it occasionally reveals defects, mainly porosity, which can lead to costly downstream failures of the part. Current inspection techniques involve use of dye penetrant, which is time consuming, labor intensive, unergonomic, and a source of human error. The goal of the project is to create an alternative inspection method using an AI-based machine-learning model. After the turning operation, a camera is deployed to perform an in-place inspection, taking images of the inner surface of the bearing sleeve, detecting defects, and returning a live report to manufacturing engineers. Successful implementation of this operation has led to an approximately 90% decrease in inspection times with calculated defect detection accuracy greater than 99%. Future projects include improving image quality using lighting booths and improving network infrastructure to incorporate more cameras in other manufacturing operations.

Research Sponsor

Dr. Yalin Dong

First Reader

Dr. Manigandan Kannan

Second Reader

Dr. Elizabeth Clifford

Honors Faculty Advisor

Dr. Scott Sawyer

Proprietary and/or Confidential Information

No

Community Engaged Scholarship

Yes

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