Semester
Summer
Date of Graduation
2025
Document Type
Dissertation (Campus Access)
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Industrial and Managements Systems Engineering
Committee Chair
Zhichao Liu
Committee Co-Chair
Imtiaz Ahmed
Committee Member
Srinjoy Das
Committee Member
Ramy Harik
Committee Member
Thorsten Wuest
Abstract
Laser Additive Manufacturing (LAM) has emerged as a transformative technology in the aerospace and biomedical industries due to its ability to fabricate and repair high-value components with high precision. However, the intricate laser-material interactions involved in LAM processes, such as Directed Energy Deposition (DED) and Powder Bed Fusion (PBF-L), can lead to defects like porosity, which compromise the structural integrity of the final parts. Traditional non-destructive evaluation methods, such as high-resolution X-ray Computed Tomography (XCT), are commonly used to identify internal defects. But the process is very time-consuming and requires labor-intensive manual inspection. In contrast, in-situ thermal monitoring of melt pool dynamics presents a promising alternative, offering real-time insights into defect formation. Despite recent advances, accurately interpreting melt pool thermal gradients remains a challenge for conventional analytical methods. This research proposes a domain-informed artificial intelligence (AI) framework that addresses three interconnected challenges in LAM, namely large amounts of unlabeled data with limited labels and severe class imbalance by performing (i) unsupervised porosity segmentation in unlabeled XCT scan datasets using the Segment Anything Model (SAM) guided by prompts created through unsupervised clustering, (ii) semi-supervised classification of limited-labeled melt pool thermal images by combin- ing a self-supervised Masked Autoencoders (MAE) and Vision Transformers (ViT) classifier, and (iii) handling severe class imbalance by generating synthetic minority samples using a domain-aware class- conditioned diffusion model with high fidelity samples with mode coverage. The proposed methodologies are validated on in-house experimental datasets, highlighting real-world applicability for limited labeled or unlabeled datasets. The research was divided into two main parts: in-house experimentation- tion with data acquisition and labeling, and customized modeling to address key challenges and perform downstream tasks. Overall, this work demonstrates the potential of customizing state-of-the-art AI techniques for complex and label-scarce manufacturing datasets, contributing towards robust, scalable, and efficient defect detection frameworks in the LAM domain.
Recommended Citation
Era, Israt Zarin, "Towards Reliable Defect Detection in Laser Additive Manufacturing: AI-Driven Solutions for Domain-Specific and Interconnected Challenges" (2025). Graduate Theses, Dissertations, and Problem Reports. 12978.
https://researchrepository.wvu.edu/etd/12978