Degree Name

MS (Master of Science)

Program

Mathematical Sciences

Date of Award

5-2025

Committee Chair or Co-Chairs

Jeff Knisley

Committee Members

Michele Joyner, Mostafa Zahed

Abstract

This thesis explores the application of deep learning techniques, specifically autoencoder based models, to enhance anomaly detection within Gage Repeatability and Reproducibility (Gage R&R) studies—an essential component of Measurement System Analysis (MSA) in quality engineering. Traditional Gage R&R methodologies, while effective for linear and low-dimensional data, exhibit limitations in detecting subtle, nonlinear variations in complex measurement systems. To address this challenge, an unsupervised autoencoder was developed and trained on a synthetically generated dataset comprising 2,500 voltage measurements (5V and 33V) derived using Generative Adversarial Networks (GANs) based on real-world manufacturing data measurements.

The proposed autoencoder model achieved a 95th percentile-based anomaly detection threshold, successfully identifying anomalies with an overall accuracy of 94.6%, a precision of 92.1%, and a recall of 95.8%—surpassing the diagnostic effectiveness of traditional ANOVA-based Gage R&R methods. The reconstruction error analysis clearly differentiated anomalous measurements, providing a robust visualization of system inconsistencies. Statistical validation using ANOVA indicated minimal 2 operator-induced variability, affirming the system’s robustness in capturing part specific anomalies. The findings underscore the potential of integrating deep learning into Gage R&R analysis, offering a scalable, data-driven alternative for anomaly detection in high-dimensional measurement environments. This approach aligns with the increasing automation and precision demands of Industry 4.0

Document Type

Thesis - unrestricted

Copyright

Copyright by the authors.

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