Process monitoring of additive manufacturing by using optical tomography

Zenzinger, G.; Bamberg, Joachim; Ladewig, Alexander; Hess, Thomas; Henkel, Benjamin; Satzger, Wilhelm

doi:10.1063/1.4914606

Cited by 69 publications

(27 citation statements)

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“…The general steps of defect detection and process control in vision systems include image acquisition, image processing, detection algorithms and a control system. For camera-based monitoring systems, images of deposited layers are usually obtained by a single lens reflective camera (Zenzinger et al, 2015). However, Iravani-Tabrizipour and Toyserkani (2007) used a trinocular optical detector composed of three CCD cameras and interference filters for real-time measurement of deposition height and used a neural network model to determine optimal threshold value for the images.…”

Section: Optical Tomographymentioning

confidence: 99%

Powder-based additive manufacturing - a review of types of defects, generation mechanisms, detection, property evaluation and metrology

Taheri

Shoaib

Koester³

et al. 2017

IJASMM

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Powder-based additive manufacturing (AM) technologies have been evaluated for use in different fields of application (aerospace, medical, etc.). Ideally, AM parts should be at least equivalent, or preferably better quality than conventionally produced parts. Manufacturing defects and their effects on the quality and performance of AM parts are a currently a major concern. It is essential to understand the defect types, their generation mechanisms, and the detection methodologies for mechanical properties evaluation and quality control. We consider the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capability of existing characterisation methodologies for powder based AM parts in this work. Methods of in-situ non-destructive evaluation and the influence of defects on mechanical properties and design considerations are also reviewed. Together, these provide a framework to understand the relevant machine and material parameters, optimise the process and production, and select appropriate characterisation methods.Abstract: Powder-based additive manufacturing (AM) technologies have been evaluated for use in different fields of application (aerospace, medical, etc.). Ideally, AM parts should be at least equivalent, or preferably better quality than conventionally produced parts. Manufacturing defects and their effects on the quality and performance of AM parts are a currently a major concern. It is essential to understand the defect types, their generation mechanisms, and the detection methodologies for mechanical properties evaluation and quality control. We consider the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capability of existing characterisation methodologies for powder based AM parts in this work. Methods of in-situ non-destructive evaluation and the influence of defects on mechanical properties and design considerations are also reviewed. Together, these provide a framework to understand the relevant machine and material parameters, optimise the process and production, and select appropriate characterisation methods.Reference to this paper should be made as follows: Taheri, H., Shoaib, M.R.M., Koester, L., Bigelow, T.A., Collins, P.C. and Bond, L.J. (2017) 'Powder-based additive manufacturing -a review of types of defects, generation mechanisms, detection, property evaluation and metrology', Int. Institute of Aviation Technology conducting research on UAVs and designing and printing 3D parts for UAVs. His research interest areas include engineering mechanics, additive manufacturing and non-destructive testing.Lucas W. Koester received his PhD from the University of Nebraska at Lincoln studying wave propagation and scattering in polycrystalline media. His current and past research interests include wave propagation in polycrystalline media, defect scattering, and modelling with emphasis on additive manufacturing processes and materials.Powder-based additive...

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Section: Optical Tomographymentioning

confidence: 99%

Powder-based additive manufacturing - a review of types of defects, generation mechanisms, detection, property evaluation and metrology

Taheri

Shoaib

Koester³

et al. 2017

IJASMM

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“…Therefore, several different in-situ monitoring approaches have been developed to observe distinct process parameters and objects such as laser power [17,18], powder recoating and powder bed surface [19], powder bed compaction [20], plume and spatter behavior [21,22], particle gas emissions [23] or part distortion [24]. In addition, and mostly applied [2], monitoring the spatial and temporal temperature conditions within or close to the laser-material interaction zone has been studied by many authors; most of these studies focusing on temperature measurements in L-PBF are based on contactless measurement techniques such as diodes or cameras [1,[25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Mohr

Altenburg

Ulbricht

et al. 2020

Metals

107

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Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.

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“…Plotting of whatever metric in space can give a region whose cooling is differentiated from the bulk is likely able to localize areas of concern [23,24]. These regions of concern can then be linked through mechanical testing to defects, such as porosity or regions with a lack of fusion and these can be relatively easily visualized using custom software or commercially available computed tomography (CT) software [25,26]. This process is depicted in Fig.…”

Section: Optical Techniquesmentioning

confidence: 99%