Application of Deep Learning on Gage R&R for Anomaly Detection
Abstract
This research examines the use of deep learning techniques, particularly autoencoders, to improve Gage Repeatability and Reproducibility (Gage R&R) assessments within quality engineering. Gage R&R serves as a crucial aspect of Measurement System Analysis (MSA), designed to evaluate the variation within measurement systems. The focus of the study lies on the use of unsupervised deep learning models to detect anomalies in measurement systems, thus improving reliability and efficiency in quality control processes. This study integrates theoretical foundations, literature reviews, and empirical analyses to present a comprehensive understanding of the subject.
Start Time
16-4-2025 1:30 PM
End Time
16-4-2025 4:00 PM
Presentation Type
Poster
Presentation Category
Science, Technology and Engineering
Student Type
Graduate Student - Masters
Faculty Mentor
Jeff Knisley
Faculty Department
Mathematics and Statistics
Application of Deep Learning on Gage R&R for Anomaly Detection
This research examines the use of deep learning techniques, particularly autoencoders, to improve Gage Repeatability and Reproducibility (Gage R&R) assessments within quality engineering. Gage R&R serves as a crucial aspect of Measurement System Analysis (MSA), designed to evaluate the variation within measurement systems. The focus of the study lies on the use of unsupervised deep learning models to detect anomalies in measurement systems, thus improving reliability and efficiency in quality control processes. This study integrates theoretical foundations, literature reviews, and empirical analyses to present a comprehensive understanding of the subject.