Non-destructive evaluation (NDE) techniques are critical for assessing the integrity, health, and mechanical properties of materials manufactured from various methods. High fidelity NDE techniques are essential for quality control but often lead to massive data generation. Such a vast data load cannot be manually processed, this leads to a severe bottleneck for process engineers. Machine learning (ML) offers a solution to this problem by providing powerful and adaptable algorithms capable of learning patterns, identifying features, and finding hidden relationships in large sets of data. Various ML models are used in this work to improve predictions, improve measurements, detect anomalies, classify anomalies, segment images, determine material health, and directly model behavior. These neural network or ML models are implemented to perform these tasks by utilizing data gathered through various NDE techniques. Additive manufacturing enables the production of complex geometries and customized parts with reduced waste and lead times. The development of new material printing capability and techniques is necessary to expand its capabilities to produce high performance parts with unique properties and functionality. Contributions to advanced additive manufacturing are made via the application of customized machine learning algorithms in this work. The development of a novel grain image generation method was completed to improve grain and grain boundary image segmentation methods on microstructure images. Convolutional Neural Networks (CNNs) were also applied to datasets of Stainless Steel Powder to help identify, qualify, and classify the health of the powder prior to print application. A feasibility study of the implementation of Binder Jetting (BJT) is conducted on Martian and Lunar regolith using a simplistic binder in this work. The need for efficient techniques to process data gathered from NDE methods is crucial to enhance the accuracy, efficiency, and speed of the analysis of this data. This will lead to faster development and implementation of advanced manufacturing techniques.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Mechanical and Aerospace Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Warren, Peter, "Machine Learning Applications in Advanced Additive Manufacturing: Process Modeling, Microstructure Analysis, and Defect Detection" (2023). Electronic Theses and Dissertations, 2020-. 1693.