Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Bian, Linkan

Committee Member

Tian, Wenmeng

Committee Member

Wang, Haifeng

Committee Member

Bond, Glenn

Committee Member

Falls, T.C

Date of Degree

8-8-2023

Document Type

Dissertation - Campus Access Only

Major

Industrial and Systems engineering

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

This dissertation aims to provide efficient process defect identification methods for advanced manufacturing environments using AI tools/algorithms with limited labeled data availability. Asset and equipment quality become highly sensitive in sustaining virtuous performance and safety in various manufacturing domains. Internally generated process imperfections degrade finished products' optimum performance and mechanical attributes. The evolution of big data and intelligent sensing systems leverage data-driven defect identification in advanced manufacturing environments. Widely adopted data-driven process anomaly detection methods assume that the training (source) and testing (target) data follow the same distribution and that labeled data are available in both source and target domains. However, the source and target sometimes follow different distributions in real-world manufacturing environments as the diversity of industrialization processes leads to heterogeneous data collection under different production conditions. Such a case significantly limits the performance of AI algorithms when distribution discrepancy exists.

Moreover, labeling data is typically costly and time-consuming, signifying that identifying process defects is limited by rare labeled data. Also, more realistic industrial applications incorporate fewer defect data than ordinal data and unforeseen target defects, leading to complications in understanding the process behaviors in various aspects. Therefore, we introduced methodological principles, including unsupervised grouping, transfer learning, data augmentation, and ensemble learning to address these limitations in advanced operations. First, rapid porosity prediction methodology for additive manufacturing (AM) processes under varying process conditions is developed by leveraging knowledge transfer from existing process conditions. Second, designing an effective classification method concerning time series signals to advance predictive maintenance (PdM) for machine state prediction is discussed. Finally, a data augmentation-based stacking classifier approach is developed to enhance the precision of predicting porosity, even when limited porosity data is available.

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