<p dir="ltr">Process monitoring of additive manufacturing (AM) allows users to ensure part quality while reducing costs associated with non-conformances. A wide range of issues can occur during an AM process, but several which are most critical are thermal and geometric errors. While various strategies exist to detect and correct these errors, most rely on a single type of sensor. In contrast, multimodal sensing can provide a more comprehensive monitoring method for AM by detecting a broader range of errors. To fuse three dimensional (3D) and thermal data, an infrared (IR) camera and 3D scanning system must be calibrated. Structured light (SL) 3D imaging offers a fast and accurate solution for collecting 3D data, and these systems are typically calibrated using a printed checkerboard or circular pattern. This approach works well for visible light cameras, but without external heating the calibration target has a uniform temperature and its features cannot be detected by an IR camera. Existing calibration solutions create thermal features by using external heating sources, arrays of small heated points, or temperature masks to cover a heated background, but each has its own limitations. This thesis presents a novel method which uses a fused filament fabrication (FFF) 3D printer to create an array of short cylinders, imitating the circles on a traditional calibration board and heating the print bed to create a thermal gradient. This target is used to estimate the intrinsic parameters of the IR camera along and perform an extrinsic calibration with a SL 3D scanner. Thermal and 3D data are then fused together using a Gaussian weighting algorithm, and an occlusion method filters any 3D points not visible to the IR camera. Additionally, a method for assigning thermal data to ideal G-code points using only an IR camera is presented. Experimental results show that these methods can accurately register temperature data to corresponding 3D data points, providing process monitoring for AM. Additionally, the mapping process occludes points where no temperature data is available, and the system is capable of detecting a subsurface void using thermal data. This process achieves multimodal data fusion for AM process monitoring while offering an integrated solution for calibrated an IR camera. </p>