Data Fusion for Additive Manufacturing Process Inspection
Open Access
- Author:
- Morgan, Jacob
- Graduate Program:
- Electrical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- March 13, 2019
- Committee Members:
- Richard L. Tutwiler, Thesis Advisor/Co-Advisor
William Evan Higgins, Committee Member
Edward William Reutzel, Committee Member - Keywords:
- data fusion
additive manufacturing
computer vision - Abstract:
- In-situ monitoring of the powder bed fusion additive manufacturing (PBFAM) process is a rapidly expanding area of interest because it offers insight into process physics and is potentially a lower cost alternative to current post-build nondestructive inspection. Ultimately, sensor data may be used as feedback for a real-time fault remediation system. However, it is unclear what defects look like in the sensor data and multiple modalities cannot be used together because they are in arbitrary frames of reference. The goal of this thesis is to present a framework for automatically registering in-situ sensor data to post-build inspection data. This will enable defects found in the post-build inspection to be mapped to the sensor data to serve as a ground truth for developing automatic defect recognition (ADR) algorithms. In this work, high resolution images and multispectral point sensor data collected during the build are registered to a post-build computed tomography (CT). These sensing modalities can be thought of as 2D raster data, 2D point cloud data, and 3D raster data respectively. A unique optimization approach for registering each modality to a common frame of reference is presented. The process is automated so that large datasets may be generated for use in developing future ADR algorithms. Voids that are clearly visible in the CT can be mapped into the in-situ sensing modalities.