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Additive manufacturing technology review From prototyping to production
Published in Adedeji B. Badiru, Vhance V. Valencia, David Liu, Additive Manufacturing Handbook, 2017
Larry Dosser, Kevin Hartke, Ron Jacobsen, Sarah Payne
Similar to CT scanning described in the previous section, industrial computed tomography takes a series of X-ray scans of part as it is rotated (see Figure 24.50) rather than cross sections, and then uses digital geometry processing to generate a 3D map. Again, the highlight is the ability to map internal structures along with the outer shape.
Investigation of cooling hole blockage in the plasma spraying of thermal barrier coatings on super-alloy
Published in Transactions of the IMF, 2023
Zhuang Liu, Changshui Gao, Zhongyu Wang, Xiaoyu Yu
To avoid the hole blockage owing to the TBC process, many efforts have been put into the development of ‘drilling post-coating’ technology, such as laser drilling of coated superalloys.16–18 According to this, the cooling holes will be drilled after the TBC process. This will eliminate the deposition of zirconia material inside the hole structure because the TBC and superalloy will be removed consecutively in the drilling process. However, after a period of service of these aeroengine parts, the TBC layer may fail and need to be repaired. During the repair, it is necessary to remove the coating material and spray the TBC once again. Because the cooling holes already exist, the hole blockage occurs again owing to the coating process.19 Therefore, it is highly necessary to acquire knowledge of the blockage morphology inside small holes and to provide a reference for future unblocking measures. Unfortunately, there is a lack of relevant research, to date, in this field because previously there was no better way to measure the blockage except mechanically cutting the holes, which is highly difficult. Currently, industrial computed tomography (CT) technology provides a measure to inspect the inner structure of the small holes without destroying the samples. Thus, this paper aims to investigate the hole blockage morphology using the APS coating method and CT inspection to deeply understand the geometric characteristics of the coating deposition in the pre-drilled holes.
Automated defect detection for fast evaluation of real inline CT scans
Published in Nondestructive Testing and Evaluation, 2020
Maxim Schlotterbeck, Lukas Schulte, Weaam Alkhaldi, Martin Krenkel, Eno Toeppe, Stephan Tschechne, Christian Wojek
The recent progress in information technology has paved the way for industrial computed tomography (CT) to be used in real inline applications. The development of high-speed CT scanners such as the ZEISS VoluMax enables full integration in the production line of, e.g. the automotive industry where large aluminum castings are produced. CT allows finding defects, determining their precise location and performing quantitative analyses in three dimensions. This information can be used to predict if further processing of a part is worth the money or if the part needs to be reworked. Hence, insights are generated that can help save machining time of subsequent process steps and thus directly generate a valuable benefit for the customer. The demand to inspect every single part and the short cycle times in the production lines impose a limit on the resulting CT image quality. Also, the evaluation of every part needs to be done automatically, as manual inspection of CT volumes on that scale would be infeasible. However, typical evaluation algorithms that deliver easy to interpret information require good image quality and thus may not perform properly in the inline case. Furthermore, the need to scan a part in just a few seconds may result in imaging artifacts that could be interpreted as defects. For example, the use of a low number of projections could generate streak artifacts, which may look like residual unwanted aluminum structures in the measured part. One big benefit of inline inspection is that all inspected parts are very similar to each other. This generates the possibility of using new evaluation methods. As shown in Figure 1, the evaluation workflow consists of several steps.