Metal Additive Manufacturing (AM) provides the means to fabricate complex metallic parts with reduced time to market and material waste and improved design freedom. Industries with strict materials qualifications such as aerospace, biomedical, and automotive are increasingly looking to AM to meet their production needs. However, significant materials-related challenges impede the widespread adoption of these technologies for critical components. In particular, fatigue resistance in as-built parts has proven to be inferior and unpredictable due to the large and variable presence of porosity. This presents a challenge for the qualification of any load bearing part without extensive post-processing, such as Hot Isostatic Pressing, and thorough inspection. Improved understanding of the underlying mechanisms behind defect formation will assist in designing process improvements to minimize or eliminate defects without relying entirely on postprocessing. In this work, the effects of powder, processing parameters, and post-processing on porosity formation in powder-bed metal AM processes are investigated using X-ray microtomography and a newly developed in-situ high speed radiography technique, Dynamic Xray Radiography. High resolution X-ray computed tomography is used to characterize defect morphology, size, and spatial distribution as a function of process and material inputs. Dynamic X-ray Radiography, which enables the in-situ observation of the laser-metal interactions at frame rates on the order of 100 kHz (and faster), is utilized to understand the dynamic behavior and transitions that occur in the vapor depression across process space. Experimental validation of previously held assumptions regarding defect formation as well as new insights into the influence of the vapor cavity on defect formation are presented.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-2199 |
| Date | 01 May 2018 |
| Creators | Cunningham, Ross W. |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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