Purdue University Graduate School
Kozikowski_Thesis_Document_SP21_2.pdf (4.25 MB)


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posted on 2021-04-29, 12:36 authored by Eric J KozikowskiEric J Kozikowski
The manufacturing industry has begun its transition into a digital age, where data-driven decisions aim to improve product quality, output, and efficiency. Decisions made based on manufacturing data can help identify key problem areas in an assembly line and mitigate any defects from progressing through to the next step in the assembly process. But what if the products’ as manufactured data was inaccurate or didn’t exist at all? Decisions based on incorrect data can lead to defective parts being passed as good parts, costing manufacturers millions of dollars in rework or recalls. When specifically referring to mechanically fastened assemblies, products that experience rotation, like an aircraft propeller, or compress to create a seal, like an oil pipe flange, all require specific torque pattern sequences to be followed during assembly. When incorrectly torqued, the parts can have catastrophic failures resulting in consumer injury or ecological contamination. This paper outlines the development and feasibility of a system and its components for tracking and error-proofing the assembly of bolted joints in an industrial environment.
Using a machine vision system, the system traces the tool location relative to the mechanical fastener and records which order the fasteners were torqued in, if an error is detected, the system does not allow the user to progress through the assembly process, notifying if an error is detected. The system leverages open source machine learning algorithms from TensorFlow2 and OpenCv, that allow efficient object detection model training. The proposed system was tested using a series of tests and evaluated using the STEP method. The data collected aims to understand the system's feasibility and effectiveness in an industrial setting.
The tests aim to understand the effectiveness of the system under standard and variable industrial work conditions. Using the STEP method and other statistical analysis, an evaluation matrix was completed, ranking the system's ability to successfully meet all predetermined benchmarks and successfully record the torque pattern used to assemble apart


Degree Type

  • Master of Science


  • Computer Graphics Technology

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Nathan Hartman

Additional Committee Member 2

Dr. Jorge Dorribo-Camba

Additional Committee Member 3

Dr. Chad Laux