Additive Fertigung
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Parameter studies are a common step in selecting process parameters for powder bed fusion of metals with laser beam (PBF-LB/M). Density cubes manufactured with varied process parameters exhibit distinguishable surface structures visible to the human eye. Industrial visual in-situ monitoring systems for PBF-LB/M currently have limited resolution and are incapable of reliably capturing differences in the surface structures. For this work, a 65 MPixel high resolution monochrome camera is integrated in an industrial PBF-LB/M machine together with a high intensity led bar. Post-exposure images are taken to analyze differences in light reflection on the specimen’s surface. The grey level co-occurrence matrix is used to quantify the in-situ measured visual surface structure of nickel-based super alloy IN939 density cubes. The properties of the grey level co-occurrence matrix correlate to the energy input and resulting porosity of specimens. Low energy samples with lack of fusion flaws show an increased contrast in the grey level co-occurrence matrix compared to specimens with an optimal energy input. The potential of high-resolution images as reference data in in-situ process monitoring in PBF-LB/M is discussed.
Based on the initial call in March 2024, an update time-frame is introduced on the working plan for a comprehensive review paper series on joining and welding technologies for the manufacturing of components and parts used in sustainable energy generation, conversion, and transport with focus on hydrogen and wind energy.
Defects are still common in metal components built with Additive Manufacturing (AM). Process monitoring methods for laser powder bed fusion (PBF-LB/M) are used in industry, but relationships between monitoring data and defect formation are not fully understood yet. Additionally, defects and deformations may develop with a time delay to the laser energy input. Thus, currently, the component quality is only determinable after the finished process.
Here, active laser thermography, a nondestructive testing method, is adapted to PBF-LB/M, using the defocused process laser as heat source. The testing can be performed layer by layer throughout the manufacturing process. We study our proposed testing method along experiments carried out on a custom research PBF-LB/M machine using infrared (IR) cameras.
Our work enables a shift from post-process testing of components towards in-situ testing during the AM process. The actual component quality is evaluated in the process chamber and defects can be detected between layers.
Hybrid additive manufacturing is becoming increasingly important in the field of additive manufacturing. Hybrid approaches combine at least two different manufacturing processes. The focus of this work is the build-up of geometries onto conventionally manufactured parts using laser-based powder bed fusion of metals (PBF-LB/M). The hybrid build-up requires a precise position detection system inside the PBF-LB/M machines to determine the exact position of the existing component. For this purpose, high-resolution camera systems can be utilized. However, the use of a camera system is associated with several challenges. The captured images are subject to various distortions of the optical path. Due to these distortions, it is not possible to use the images for measurements and, therefore, it is not possible to calculate the positions of objects. In this study a homography matrix is calculated to correct keystone distortion in the images. Different calibration patterns have been tested for the calculation of the homography matrix. The influence of the number of calibration points on the precision of position detection of objects is determined. Furthermore, the influence of an additional camera calibration by using ChArUco boards is evaluated. The result is a camera calibration workflow with associated calibration pattern for a precise position detection of parts inside PBF-LB/M machines allowing a hybrid build-up with minimum physical offset between base component and build-up.
Hybrid additive manufacturing is becoming increasingly important in the field of additive manufacturing. Hybrid approaches combine at least two different manufacturing processes. The focus of this work is the build-up of geometries onto conventionally manufactured parts using Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M). The hybrid build-up requires a precise position detection system inside the PBF-LB/M machines to determine the exact position of the existing component. For this purpose, high-resolution camera systems can be utilized. However, the use of a camera system is associated with several challenges. The captured images are subject to various distortions of the optical path. Due to these distortions, it is not possible to use the images for measurements and, therefore, it is not possible to calculate the positions of objects. In this study a homography matrix is calculated to correct keystone distortion in the images. Different calibration patterns have been tested for the calculation of the homography matrix. The influence of the number of calibration points on the precision of position detection of objects is determined. Furthermore, the influence of an additional camera calibration by using ChArUco boards is evaluated. The result is a camera calibration workflow with associated calibration pattern for a precise position detection of parts inside PBF-LB/M machines allowing a hybrid build-up with minimum physical offset between base component and build-up.
AbstractThis research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF‐LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro‐computed tomography (CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters.
Additive manufacturing is one of the most promising techniques for industrial production and maintenance, but the specifics of the layered structure must be considered. The Direct Energy Deposition-Arc process enables relatively high deposition rates, which is favourable for larger components. For this study, specimens with different orientations were prepared from one AISI316 steel block – parallel and orthogonal to the deposition plane. Quasistatic tensile loading tests were carried out, monitored by an infrared camera. The obtained surface temperature maps revealed structural differences between both orientations. The consideration of surface temperature transients yields more details about the behaviour of the material under tensile loading than the conventional stress-strain-curve. These preliminary investigations were supplemented by thermographic fatigue trials. Although the anisotropy was also observed during fatigue loading the fatigue behaviour in general was the same, at least for both inspected specimens. The presented results demonstrate the abilities and the potential of thermographic techniques for tensile tests.
Guaranteeing safety and security of citizens requires a significant effort and innovative tools from national and international agencies and governments, especially when it comes to the field of explosives detection. The need to detect Improvised Explosive Devices (IEDs) and Home-made Explosives (HMEs) at a point of suspicion, has grown rapidly due to the ease with which the precursors can be obtained and the reagents synthesised. The limited availability of immunoanalytical tools for HME detection presents an opportunity for the development of new devices, which enable a rapid detection and recognise the target analyte with high specificity and sensitivity. In this work, we introduce an optical biosensor for highly specific and sensitive HME detection. The immunoassay system is placed in a hydrogel environment permeable to the analyte and transparent to light interrogating the fluorescently labelled antibodies. The readout of the immunoanalytical system is realized with Supercritical Angle Fluorescence (SAF), an advanced microscopy technique. To accomplish this, we made use of recent, commercial high resolution (< 22 µm) Liquid Crystal Display 3D printers to fabricate a parabolic optical element with high refractive index (RI>1.5) and transmission values (>90%) from photo-resin. Aiming at a new generation of sensors, which not only can meet the requirements of trace detection, but can also be used for substance identification, the combination of immunoanalytical recognition with SAF detection offers a modularity and versatility that is principally well suitable for the measurements of target analytes at trace levels.
Ceramic additive manufacturing (AM) requires a complex process chain with various post-processing steps that require expensive machines and special expertise. The key to further market penetration is AM that makes it possible to integrate into an already established ceramic process chain. Most successful AM technologies for ceramics are, however, based on processes that initially have been developed for polymeric materials. For ceramics AM, polymers or precursors are loaded with ceramic particles. This strategy facilitates the entry into AM, however the introduction of organic additives into the ceramic process chain represents a considerable technological challenge to ultimately obtain a ceramic component after additive shaping. In the present communication, two technologies based on ceramic suspensions will be introduced, the “layerwise slurry deposition” (LSD) and “laser induced slip casting” (LIS) technology. Both technologies take advantage of the high packing densities reached by conventional slip casting and moreover enable the processing of fines, even nanoparticles.
AbstractTwo‐photon polymerization (2PP) additive manufacturing (AM) utilizes feedstocks of ceramic nanoparticles of a few nanometers in diameter, enabling the fabrication of highly accurate technical ceramic design with structural details as small as 500 nm. The performance of these materials is expected to differ from conventional AM ceramics, as nanoparticles and three‐dimensional printing at high resolution introduce new microstructural aspects. This study applies 2PP‐AM of yttria‐stabilized zirconia to investigate the mechanical response behavior under compressive load, probing the influence of smallest structural units induced by the line packing during the printing process, design of sintered microblocks, and sintering temperature and thereby microstructure. We find a dissipative mechanical response enhanced by sintering at lower temperatures than conventional. The pursued 2PP‐AM approach yields a microstructured material with an increased number of grain boundaries that proposedly play a major role in facilitating energy dissipation within the here printed ceramic material. This microplastic response is further triggered by the filigree structures induced by hollow line packing at the order of the critical defect size of ceramics. Together, these unique aspects made accessible by the 2PP‐AM approach contribute to a heterogeneous nano‐ and microstructure, and hint toward opportunities for tailoring the mechanical response in future ceramic applications.