Over the past few decades, additive manufacturing (AM) or 3D printing (3DP) technologies witnessed revolutionary growth in the manufacturing sector. Parts produced with metal AM techniques, especially Laser Powder Bed Fusion (LPBF), are often prone to porosity issues. The presence of pores leads to harmful effects such as crack formation and, eventually, premature failure of the component. Consequently, research in defect detection and pore prediction attracted substantial attention. Utilizing image-based porosity detection in preexisting systems is a simple, effective, and cost-efficient approach for final part inspection. This thesis investigates the possibility of predicting porosity using U-Net and its novel network architectures named RU-Net and RAU-Net, on an X-ray computed tomography (XCT) image dataset. Later, the performance of these models is analyzed and compared using precision, recall, F1 score, mAP, IoU metrics, and their hybrid losses combining BCG and Dice loss. RAU-Net outperforms RU-Net and U-Net in all these metrics by detecting more than 90% of actual pores while retaining 95% precision. While RU-Net and U-Net required additional training, RAU-Net achieved high performance in only 50 epochs, demonstrating its data efficiency and convergence. Due to its shorter training period, also leading to lower computational overhead, RAU-Net is suited for practical high throughput and low latency applications. Particularly in time-sensitive applications, RAU-Net can enable more widespread adoption of dense prediction networks. A custom script is developed for estimating the porosity percentage level in 3D printed metal components precisely, further enhancing final product inspection procedures. As a result, the entire quality control process is simplified, which allows for the quicker inspection of final components to deliver, by ensuring they meet required quality and reliability standards.

Online quality assurance is crucial for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining stable process operations and establishing efficacious online quality assurance schemes. To address these challenges, four different advanced manufacturing processes, namely, fused filament fabrication (FFF), binder jetting, chemical mechanical planarization (CMP), and the slicing process in wafer production, are investigated in this dissertation for applications of online quality assurance, with utilization of various sensors, such as thermocouples, infrared temperature sensors, accelerometers, etc. The overarching goal of this dissertation is to develop innovative integrated methodologies tailored for these individual manufacturing processes but addressing their common challenges to achieve satisfying performance in online quality assurance based on heterogeneous sensor data. Specifically, three new methodologies are created and validated using actual sensor data, namely, (1) Real-time process monitoring methods using Dirichlet process (DP) mixture model for timely detection of process changes and identification of different process states for FFF and CMP. The proposed methodology is capable of tackling non-Gaussian data from heterogeneous sensors in these advanced manufacturing processes for successful online quality assurance. (2) Spatial Dirichlet process (SDP) for modeling complex multimodal wafer thickness profiles and exploring their clustering effects. The SDP-based statistical control scheme can effectively detect out-of-control wafers and achieve wafer thickness quality assurance for the slicing process with high accuracy. (3) Augmented spatiotemporal log Gaussian Cox process (AST-LGCP) quantifying the spatiotemporal evolution of porosity in binder jetting parts, capable of predicting high-risk areas on consecutive layers. This work fills the long-standing research gap of lacking rigorous layer-wise porosity quantification for parts made by additive manufacturing (AM), and provides the basis for facilitating corrective actions for product quality improvements in a prognostic way. These developed methodologies surmount some common challenges of advanced manufacturing which paralyze traditional methods in online quality assurance, and embody key components for implementing effective online quality assurance with various sensor data. There is a promising potential to extend them to other manufacturing processes in the future. / Ph. D. / This dissertation work develops novel online quality assurance methodologies for advanced manufacturing using various sensor data. Four advanced manufacturing processes, including fused filament fabrication, binder jetting, chemical mechanical planarization, and wafer slicing process, are investigated in this research. The developed methodologies address some common challenges in the aforementioned processes, such as nonlinear process dynamics and high variety in sensor data dimensions, which have severely hindered the effectiveness of traditional online quality assurance methods. Consequently, the proposed research accomplishes satisfying performance in defect detection and quality prediction for the advanced manufacturing processes. In this dissertation, the research methodologies are constructed in both space and time domains based on different types of sensor data. Sensor data representation and integration for a variety of data formats (e.g., online data stream, profile data, image data) with the dimensionality covering a wide range (from ~100 to ~105 ) are researched to extract effective features that are sensitive to manufacturing process defects; the devised methods, based on the extracted features, utilize spatiotemporal analysis to realize timely detection and accurate prediction of process defects. These integrated methodologies have a promising potential to be extended to other advanced manufacturing processes for efficacious process monitoring and quality assurance. The accomplished work in this dissertation is an effective effort towards sustainable operations of advanced manufacturing. The achieved performance not only enables improvement in defect detection and quality prediction, but also lays the foundation for future implementation of corrective actions that can automatically mitigate the process defects.