Process monitoring in additive manufacturing aimed toward part qualification
Additive Manufacturing (AM), or layer-by-layer part fabrication, has played a tremendous role in the maker culture by allowing ideas to be materialized with limited resources or knowledge in manufacturing. Various cutting edge AM technologies exist today that are used to create end-use parts; however, these technologies are still new and the processes have not gone through the rigorous evaluation process that traditional manufacturing (i.e. milling, stamping, casting) methods have been through. As a result, several important questions arise when looking to adapt AM technology, including control of the manufacturing process, effect of manufacturing process on part properties, level of variance between the virtual input and the fabricated object, part defects and identification of defects, and understanding destructive and non-destructive testing etc. Because the process is new and parts fabricated from AM technologies vary widely, each part upon fabrication is unique and hence, certification and qualification of the fabricated part is important. This thesis tries to answer these important questions by developing and implementing a monitoring system and incorporating virtual metrology leading into part qualification. ^ Due to the potential to qualify a part during every stage of fabrication, layerwise monitoring has become an area of interest in the field of AM. Spatial measurement and qualification of every stage of fabrication, including each layer while a part is under fabrication, were demonstrated through this research by the application of a virtual measurement system with a powder bed fusion AM technology. Specifically, the focus of this research was to study the metrology for AM fabrication systems, focusing on powder bed fusion technology, using an in situ optical measurement device that included an infrared (IR) camera. The utilization of an IR camera during fabrication allowed the acquisition of several data sets (e.g. thermal data, surface area, porous areas, etc.), which were acquired, analyzed, and compared with experimental measurements. Development of a MATLAB (The MathWorks, Inc., Natick, MA) code and incorporation of a novel continuous data acquisition system were the ultimate goals of the project. ^ Aiming towards finding the level of geometric variance upon fabrication, the first task was to develop a basic MATLAB code to detect the object within IR thermographs captured during operation of a powder bed fusion electron beam melting (EBM) fabrication system. An algorithm was developed upon selection of a region of interest, and the segmented images were used for the remainder of the process. Surface areas of the fabricated regions were acquired with pixel measurement upon detection of objects. Pixel area (virtual area) was converted into a standard scale of measurement and compared with the experimental measurement system. A 0.98% difference of fabricated area was found, between the geometric data obtained for simple circular features using the acquired IR images and measurements taken from the fabricated parts. Hence, a virtual measurement system was introduced using an IR camera. ^ Detection and quantification of defects (i.e. porosity) was another important factor of part qualification and the next development in this research using IR imaging. As AM systems provide an advantage to fabricate complex functional parts, which include support structures, metrology for complicated structures entails the applicability of the optical measurement system. A bracket, having support structure and variation of geometry with height change, was fabricated and an algorithm was developed for the extraction of an object surface area from the support structure’s surface area. Porous areas were also detected as child boundaries and quantified within the IR images captured during fabrication of a part with intentionally seeded defects. Quantified areas were compared with CT scan data and showed a 52-69% difference existed between intentional defects. Nonetheless, results showed that the IR images could be stacked post-fabrication to represent the part that has been fabricated and help identify any defects within the part. (Abstract shortened by UMI.)^
Ridwan, Shakerur, "Process monitoring in additive manufacturing aimed toward part qualification" (2015). ETD Collection for University of Texas, El Paso. AAI1600342.