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1. Nondestructive Evaluation of Additively Manufactured Metal Components with an Eddy Current Technique

   B. Schneider, B; Shoaib, M; Taheri, H; Koester, L; Bigelow, T; Bond, L (January 2018, ASNT 27th Research Symposium Proceedings)

   The ability of Additive Manufacturing (AM) processes to ensure delivery of high quality metal-based components is somewhat limited by insufficient inspection capabilities. The inspection of AM parts presents particular challenges due to the design flexibility that the fabrication method affords. The nondestructive evaluation (NDE) methods employed need to be selected based on the material properties, type of possible defects, and geometry of the parts. Electromagnetic method, in particular Eddy Current (EC), is proposed for the inspections. This evaluation of EC inspection considers surface and near-surface defects in a stainless steel (SS) 17 4 PH additively manufactured sample and a SS 17 4 PH annealed plates manufactured traditionally (reference sample). The surfaces of the samples were polished using 1 micron polishing Alumina grit to achieve a mirror like surface finish. 1.02 mm (0.04”), 0.508 mm (0.02”) and 0.203 mm (0.008”) deep Electronic Discharge Machining (EDM) notches were created on the polished surface of the samples. Lift off and defect responses for both additive and reference samples were obtained using a VMEC-1 commercial instrument and a 500 kHz absolute probe. The inspection results as well as conductivity assessments for the AM sample in terms of the impedance plane signature were compared to response of similar features in the reference sample. Direct measurement of electromagnetic properties of the AM samples is required for precise inspection of the parts. Results show that quantitative comparison of the AM and traditional materials help for the development of EC technology for inspection of additively manufactured metal parts. 

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2. Future of Non-Destructive Testing in the Era of Additive Manufacturing and Machine Learning

   Gupta, N (March 2024, Journal of Non-Destructive Testing and Evaluation)

   Additive manufacturing (AM) methods have become mainstream in many industry sectors, especially aeronautics and space structures, where production volume for components is low and designs are highly customized. The frequency of launching space missions is increasing around the world. Some of these missions are sending landers and rovers to moon, mars, and other planets. Such space structures require numerous parts that are unique in design or are produced in just one or a very small production run. Such parts produced for high stake and very expensive missions require complete confidence in the quality of each part. Characterization of parts manufactured by AM is a significant challenge for many existing methods due to the geometric complexity, feature size in the structure, and size of the part. This paper discusses various challenges in applying current characterization methods to the AM sector. Machine learning (ML) methods are considered promising in materials and manufacturing fields. However, generating the training dataset by creating a large number of parts is expensive and impractical. New methods are required to train the ML algorithms on small datasets, especially for parts of unique geometry that are produced in limited production run such as space structures. 

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3. Review of internal and external surface finishing technologies for additively manufactured metallic alloys components and new frontiers

   https://doi.org/10.1007/s40964-023-00412-z  

   Demisse, Wondwosen; Xu, Jiajun; Rice, Lucas; Tyagi, Pawan (February 2023, Progress in Additive Manufacturing)

   Surface finishing in additive manufacturing (AM) is a technological bottleneck. The field of surface finishing of AM parts is vast because it not only focuses on roughness reduction in the hard-to-access internal surfaces but also includes the scope of adding coatings and sensors. Even though metal AM component is reaching the density and bulk microstructure at par or even better than conventionally produced components, adverse impact of surface roughness and imperfections is becoming the major obstruction. It is observed that external and internal surface roughness of AM components is a high probability cause of many unavoidable issues such as corrosion, incorrect tolerance estimations during the build stage, and the fatigue failure of parts before the expected life cycle. At present, AM field mainly focuses on improving and enhancing the internal and external surface roughness to pass the stringent qualification requirements for actual applications. To address these challenges, researchers worldwide are conducting many experiments and developing different surface finishing techniques. This paper reviews the state-of-the-art knowledge and processes of different surface finishing technology that can be applied to AM metal components. This article mainly highlights several liquid-based surfaces finishing approaches to develop promising surface microstructures on interior and exterior surfaces as a micromachining tool. The future of making strong and self-monitoring AM component requires broadening of surface finishing field and including advanced topics such as coatings and adding sensor technology. We also discuss new frontiers and the scope of future work in the surface finishing field to bring attention to related concerns and possibilities associated with making smart and strong AM components for twenty-first-century integrated engineering systems. 

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4. In situ / operando synchrotron x-ray studies of metal additive manufacturing

   https://doi.org/10.1557/mrs.2020.275  

   Sun, Tao; Tan, Wenda; Chen, Lianyi; Rollett, Anthony (November 2020, MRS Bulletin)

   null (Ed.)

   Additive manufacturing (AM) comprises a group of transformative technologies that are likely to revolutionize manufacturing. In particular, laser-based metal AM techniques can not only fabricate parts with extreme complexity, but also provide innovative means for designing and processing new metallic systems. However, there are still several technical barriers that constrain metal AM. Overcoming these barriers requires a better understanding of the physics underlying the complex and dynamic laser–metal interaction at the heart of many AM processes. This article briefly describes the state of the art of in situ / operando synchrotron x-ray imaging and diffraction for studying metal AM, mostly the laser powder-bed fusion process. It highlights the immediate impact of operando synchrotron studies on the advancement of AM technologies, and presents future research challenges and opportunities. 

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5. Multiresolution Quality Inspection of Layerwise Builds for Metal 3D Printer and Scanner

   https://doi.org/10.1115/1.4057013  

   Yang, Hui; Reijonen, Joni; Revuelta, Alejandro (October 2023, Journal of Manufacturing Science and Engineering)

   Abstract Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes. 

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