This dissertation aims to provide critical methodological advancements for sensor fusion and physics-informed machine learning in metal additive manufacturing (MAM) to assist practitioners in detecting quality control structural anomalies. In MAM, there is an urgent need to improve knowledge of the internal layer fusion process and geometric variation occurring during the directed energy deposition processes. A core challenge lies in the cyclic heating process, which results in various structural abnormalities and deficiencies, reducing the reproducibility of manufactured components. Structural abnormalities include microstructural heterogeneities, porosity, deformation and distortion, and residual stresses. Data-driven monitoring in MAM is needed to capture process variability, but challenges arise due to the inability to capture the thermal history distribution process and structural changes below the surface due to limitations in in-situ data collection capabilities, physical domain knowledge integration, and multi-data and multi-physical data fusion. The research gaps in developing system-based generalizable artificial intelligence (AI) and machine learning (ML) to detect abnormalities are threefold. (1) Limited fusion of various types of sensor data without handcrafted selection of features. (2) There is a lack of physical domain knowledge integration for various systems, geometries, and materials. (3) It is essential to develop sensor and system integration platforms to enable a holistic view to make quality control predictions in the additive manufacturing process. In this dissertation, three studies utilize various data types and ML methodologies for predicting in-process anomalies. First, a complementary sensor fusion methodology joins thermal and ultrasonic image data capturing layer fusion and structural knowledge for layer-wise porosity segmentation. Secondly, a physics-informed data-driven methodology for joining thermal infrared image data with Goldak heat flux improves thermal history simulation and deformation detection. Lastly, a physics-informed machine learning methodology constrained by thermal physical functions utilizes in-process multi-modal monitoring data from a digital twin environment to predict distortion in the weld bead. This dissertation provides current practitioners with data-driven and physics-based interpolation methods, multi-modal sensor fusion, and anomaly detection insights trained and validated with three case studies.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-7220 |
| Date | 10 May 2024 |
| Creators | Zamiela, Christian E. |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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