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The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
The triaxial distribution of the residual stress in laser powder bed fused austenitic steel 316L was determined by X-ray and neutron diffraction. The residual stress analysis results were linked to the thermal history of the specimens, which were manufactured with varying inter-layer-times and scanning velocities. A clear link between the in-process temperature of the specimens and the residual stress was found, based on in-situ monitoring data.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed.
Laser powder bed fusion (L-PBF) is one of the most promising additive manufacturing (AM) technologies for the production of complex metallic real part components. Due to the multitude of factors influencing process conditions and part quality and due to the layer-wise characteristic of the process, monitoring of process signatures seems to be mandatory in case of the production of safety critical components. Here, the iterative process nature enables unique access for in-situ monitoring during part manufacture. In this talk, the successful test of the synchronous use of a high-frequency infrared camera and a camera for long time exposure, working in the visible spectrum (VIS) and equipped with a near infrared filter (NIR), will be introduced as a machine manufacturer independent thermal detection monitoring set-up. Thereby, the synchronous use of an infrared camera and a VIS NIR camera combines the advantages of high framerate and high spatial resolution. The manufacture of a 316L stainless steel specimen, containing purposely seeded defects and volumes with forced changes of energy inputs, was monitored during the build. The measured thermal responses are analysed and compared with a defect mapping obtained by micro X-ray computed tomography (CT).
The first results regarding methods for data analysis, derived correlations between measured signals and detected defects as well as sources of possible data misinterpretation are presented in this talk.
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.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
The manufacturability of metallic alloys using laser-based additive manufacturing methods such as laser powder bed fusion has substantially improved within the last decade. However, local melting and solidification cause hierarchically structured and crystallographically textured microstructures possessing large residual stress. Such microstructures are not only the origin of mechanical anisotropy but also pose metrological challenges for the diffraction-based residual stress determination. Here we demonstrate the influence of the build orientation and the texture on the microstructure and consequently the mechanical anisotropy of as-built Inconel 718. For this purpose, we manufactured specimens with [001]/[011]-, [001]- and [011]/[111]-type textures along their loading direction. In addition to changes in the Young’s moduli, the differences in the crystallographic textures result in variations of the yield and ultimate tensile strengths. With this in mind, we studied the anisotropy on the micromechanical scale by subjecting the specimens to tensile loads along the different texture directions during in situ neutron diffraction experiments. In this context, the response of multiple lattice planes up to a tensile strain of 10% displayed differences in the load partitioning and the residual strain accumulation for the specimen with [011]/[111]-type texture. However, the relative behavior of the specimens possessing an [001]/[011]- and [001]-type texture remained qualitatively similar. The consequences on the metrology of residual stress determination methods are discussed.
As humanity contemplates manned missions to Mars, strategies need to be developed for the design and operation of hospitable environments to safely work in space for years. The supply of spare parts for repair and replacement of lost equipment will be one key need, but in-space manufacturing remains the only option for a timely supply. With high flexibility in design and the ability to manufacture ready-to-use components directly from a computeraided model, additive manufacturing (AM) technologies appear extremely attractive. For the manufacturing of metal parts, laser-beam melting is the most widely used AM process. However, the handling of metal powders in the absence of gravity is one prerequisite for its successful application in space. A gas flow throughout the powder bed is successfully applied to compensate for missing gravitational forces in microgravity experiments. This so-called gas-flow-assisted powder deposition is based on a porous Building platform acting as a filter for the fixation of metal particles in a gas flow driven by a pressure difference maintained by a vacuum pump.