Additive Fertigung
Filtern
Dokumenttyp
- Zeitschriftenartikel (14)
- Vortrag (10)
- Beitrag zu einem Tagungsband (8)
- Posterpräsentation (3)
- Buchkapitel (1)
Sprache
- Englisch (36) (entfernen)
Schlagworte
- Thermography (19)
- Additive manufacturing (15)
- Process monitoring (14)
- Additive Manufacturing (12)
- Laser metal deposition (7)
- Infrared thermography (5)
- Laser powder bed fusion (5)
- ProMoAM (5)
- L-PBF (4)
- LMD (4)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (36)
- 8.0 Abteilungsleitung und andere (36)
- 9 Komponentensicherheit (25)
- 9.3 Schweißtechnische Fertigungsverfahren (17)
- 8.5 Röntgenbildgebung (10)
- 9.6 Additive Fertigung metallischer Komponenten (8)
- 5 Werkstofftechnik (5)
- 1 Analytische Chemie; Referenzmaterialien (3)
- 5.1 Mikrostruktur Design und Degradation (3)
- 8.4 Akustische und elektromagnetische Verfahren (3)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (10)
Laser powder bed fusion of metallic components (PBF-LB/M) is gaining acceptance in industry. However, the high costs and lengthy qualification processes required for printed components create the need for more effective in-situ monitoring and testing methods. This article proposes multispectral Optical Tomography (OT) as a new approach for monitoring the PBF-LB/M process. Compared to other methods, OT is a low-cost process monitoring method that uses long-time exposure imaging to observe the build process. However, it lacks time resolution compared to expensive thermographic sensor systems. Monochromatic OT (1C-OT) is already commercially available and observes the building process layer-wise using a single wavelength window in the NIR range. Multispectral OT (nC-OT) utilizes a similar setup but can measure multiple wavelength ranges per location simultaneously. By comparing the classical 1C-OT and nC-OT approaches, this article examines the advantages of nC-OT (two channel OT and RGB-OT) in reducing the false positive rate for process deviations and approximating maximum temperatures for a better comparison between different build processes and materials. This could ultimately reduce costs and time for part qualification. The main goal of this contribution is to assess the advantages of nC-OT compared to 1C-OT for in-situ process monitoring of PBF-LB/M.
The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given.
Great complexity characterizes Additive Manufacturing (AM) of metallic components via laser powder bed fusion (PBF-LB/M). Due to this, defects in the printed components (like cracks and pores) are still common. Monitoring methods are commercially used, but the relationship between process data and defect formation is not well understood yet. Furthermore, defects and deformations might develop with a temporal delay to the laser energy input. The component’s actual quality is consequently only determinable after the finished process.
To overcome this drawback, thermographic in-situ testing is introduced. The defocused process laser is utilized for nondestructive testing performed layer by layer throughout the build process. The results of the defect detection via infrared cameras are shown for a research PBF-LB/M machine.
This creates the basis for a shift from in-situ monitoring towards in-situ testing during the AM process. Defects are detected immediately inside the process chamber, and the actual component quality is determined.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
Safety-critical applications of products manufactured by laser powder bed fusion (PBF-LB/M) are still limited to date. This is mainly due to a lack of knowledge regarding the complex relationship between process, structure, and resulting properties. The assurance of homogeneity of the microstructure and homogeneity of the occurrence and distribution of defects within complexly shaped geometries is still challenging. Unexpected and unpredicted local inhomogeneities may cause catastrophic failures. The identification of material specific and machine specific process parameter windows for production of fully dense simple laboratory specimens is state of the art. However, the incorporation of changing thermal conditions that a complexly shaped component can be faced with during the manufacturing process is often neglected at the stage of a process window determination. This study demonstrates the tremendous effect of changing part temperatures on the defect occurrence for the broadly used stainless steel alloy AISI 316L. Process intrinsic variations of the surface temperature are caused by heat accumulation which was measured by use of a temperature adjusted mid-wavelength infrared (MWIR) camera. Heat accumulation was triggered by simple yet effective temporal and geometrical restrictions of heat dissipation. This was realized by a variation of inter layer times and reduced cross section areas of the specimens. Differences in surface temperature of up to 800 K were measured. A severe development of keyhole porosity resulted from these distinct intrinsic preheating temperatures, revealing a shift of the process window towards unstable melting conditions. The presented results may serve as a warning to not solely rely on process parameter optimization without considering the actual process conditions a real component is faced with during the manufacturing process. Additionally, it motivates the development of representative test specimens.
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
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.
In-situ monitoring of the Laser Powder Bed Fusion build process via bi- chromatic optical tomography
(2022)
As metal additive manufacturing (AM) is entering industrial serial production of safety relevant components, the need for reliable process qualification is growing continuously. Especially in strictly regulated industries, such as aviation, the use of AM is strongly dependent on ensuring consistent quality of components. Because of its numerous influencing factors, up to now, the metal AM process is not fully controllable. Today, expensive part qualification processes for each single component are common in industry.
This contribution focusses on bi-chromatic optical tomography as a new approach for AM in-situ quality control. In contrast to classical optical tomography, the emitted process radiation is monitored simultaneously with two temperature calibrated cameras at two separate wavelength bands. This approach allows one to estimate the local maximum temperatures during the manufacturing process, thus increases the comparability of monitoring data of different processes. A new process information level at low investment cost is reachable, compared to, e.g., infrared thermography.
The industrial use of additive manufacturing for the production of metallic parts with high geometrical complexity and lot sizes close to one is rapidly increasing as a result of mass individualisation and applied safety relevant constructions. However, due to the high complexity of the production process, it is not yet fully understood and controlled, especially for changing (lot size one) part geometries.
Due to the thermal nature of the Laser-powder bed fusion (L-PBF) process – where parts are built up layer-wise by melting metal powder via laser - the properties of the produced part are strongly governed by its thermal history. Thus, a promising route for process monitoring is the use of thermography. However, the reconstruction of temperature information from thermographic data relies on the knowledge of the surface emissivity at each position on the part. Since the emissivity is strongly changing during the process due to phase changes, great temperature gradients, possible oxidation, and other potential influencing factors, the extraction of real temperature data from thermographic images is challenging. While the temperature development in and around the melt pool, where melting and solidification occur is most important for the development of the part properties. Also, the emissivity changes are most severe in this area, rendering the temperature deduction most challenging.
A possible route to overcome the entanglement of temperature and emissivity in the thermal radiation is the use of hyperspectral imaging in combination with temperature emissivity separation (TES) algorithms. As a first step towards the combined temperature and emissivity determination in the L-PBF process, here, we use a hyperspectral line camera system operating in the short-wave infrared region (0.9 µm to 1.7 µm) to measure the spectral radiance emitted. In this setup, the melt pool of the L-PBF process migrates through the camera’s 1D field of view, so that the radiation intensities are recorded simultaneously for multiple different wavelength ranges in a spatially resolved manner. At sufficiently high acquisition frame rate, an effective melt pool image can be reconstructed. Using the grey body approximation (emissivity is independent of the wavelength), a first, simple TES is performed, and the resulting emissivity and temperature values are compared to literature values. Subsequent work will include reference measurements of the spectral emissivity in different states allowing its analytical parametrisation as well as the adaption and optimisation of the TES algorithms. An illustration of the proposed method is shown in Fig.1.
The investigated method will allow to gain a deeper understanding of the L-PBF process, e.g., by quantitative validation of simulation results. Additionally, the results will provide a data basis for the development of less complex and cheaper sensor technologies for L-PBF in-process monitoring (or for related process), e.g., by using machine learning.
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.
Results of the project ProMoAM (Process monitoring in additive manufacturing) presented. Results from in-situ eddy current testing, optical emission spectroscopy, thermography, optical tomography as well as particle and gas emission spectroscopy are summarized and correlated to results from computed tomography for future in-situ defect detection.
Additive manufacturing of metals gains increasing relevance in the industrial field for part production. However, especially for safety relevant applications, a suitable quality assurance is needed. A time and cost efficient route to achieve this goal is in-situ monitoring of the build process. Here, the BAM project ProMoAM (Process monitoring in additive manufacturing) is briefly introduced and recent advances of BAM in the field of in-situ monitoring of the L-PBF and the LMD process using thermography are presented.
Undetected and undesired microstructural variations in components produced by laser powder bed fusion are a major challenge, especially for safety-critical components. In this study, an in-depth analysis of the microstructural features of 316L specimens produced by laser powder bed fusion at different levels of volumetric energy density and different levels of inter layer time is reported. The study has been conducted on specimens with an application relevant build height
(>100 mm). Furthermore, the evolution of the intrinsic preheating temperature during the build-up of specimens was monitored using a thermographic in-situ monitoring set-up. By applying recently determined emissivity values of 316L powder layers, real temperatures could be quantified. Heat accumulation led to preheating temperatures of up to about 600 °C. Significant differences in the preheating temperatures were discussed with respect to the individual process parameter combinations, including the build height. A strong effect of the inter layer time on the heat accumulation was observed. A shorter inter layer time resulted in an increase of the preheating temperature by more than a factor of 2 in the upper part of the specimens compared to longer inter layer times. This, in turn, resulted in heterogeneity of the microstructure and differences in material properties within individual specimens. The resulting differences in the microstructure were analyzed using electron back scatter diffraction and scanning electron microscopy. Results from chemical analysis as well as electron back scatter diffraction measurements indicated stable conditions in terms of chemical alloy composition and austenite phase content for the used set of parameter combinations. However, an increase of the average grain size by more than a factor of 2.5 could be revealed within individual specimens. Additionally, differences in feature size of the solidification cellular substructure were examined and a trend of increasing cell sizes was observed. This trend was attributed to differences in solidification rate and thermal gradients induced by differences in scanning velocity and preheating temperature. A change of the thermal history due to intrinsic preheating could be identified as the main cause of this heterogeneity. It was induced by critical combinations of the energy input and differences in heat transfer conditions by variations of the inter layer time. The microstructural variations were directly correlated to differences in hardness.
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.
In this chapter, the current state-of-the-art of in situ monitoring and in situ NDE methods in additive manufacturing is summarized. The focus is set on methods, which are suitable for making statements about the quality and usability of a component currently being manufactured. This includes methods which can be used to determine state properties like temperature or density, other physical properties like electrical or thermal conductivity, the microstructure, the chemical composition, the actual geometry, or which enable the direct detection of defects like cracks, voids, delaminations, or inclusions. Thus, optical, thermographic, acoustic, and electromagnetic methods, as well as methods being suitable for investigating particle and fume emission are presented. The requirements of in situ monitoring methods with a focus on thermographic methods are discussed by considering different additive manufacturing processes like laser powder bed fusion (PBF-LB/M) and direct energy deposition (DED-LB/M). Examples of the successful implementation and applications of such monitoring methods at BAM are given. The in situ monitoring and NDE methods are compared against post-process NDE methods. The advantages and challenges of in situ methods concerning real-time data analysis and the application of AI algorithms are addressed and discussed.
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.
Due to the rapid thermal cycles involved in additive manufacturing of metals, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine their formation, in-process measurements of the temperature are needed. Since the part emissivity is strongly inhomogeneous and rapidly changing in the process, the applicability of thermography for the determination of thermodynamic temperatures is limited. Measuring the thermal radiation in different wavelengths simultaneously, temperature and emissivity can be separated. Here, we present results of a preliminary study using multispectral thermography to obtain real temperatures and emissivities in directed energy deposition (DED) processes.
Due to the rapid thermal cycles involved in additive manufacturing of metals, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine their formation, in-process measurements of the temperature are needed. Since the part emissivity is strongly inhomogeneous and rapidly changing in the process, the applicability of thermography for the determination of thermodynamic temperatures is limited. Measuring the thermal radiation in different wavelengths simultaneously, temperature and emissivity can be separated. Here, we present results of a preliminary study using multispectral thermography to obtain real temperatures and emissivities in directed energy deposition (DED) processes.
Additive manufacturing of metals offers the opportunity to build parts with a high degree of complexity without additional costs, opening a new space for design optimization. However, the processes are highly complex and due to the rapid thermal cycles involved, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine the formation of internal stresses and the microstructure, in-process spatially resolved measurements of the part temperature are needed. If the emissivity of the inspected part is known, its thermodynamic temperature can be reconstructed by a suited radiometric model. However, in additive manufacturing of metals, the emissivity of the part surface is strongly inhomogeneous and rapidly changing due to variations of, e.g., the degree of oxidation, the material state and temperature. Thus, here, the applicability of thermography in the determination of thermodynamic temperatures is limited. However, measuring the process thermal radiation at different wavelengths simultaneously enables one to separate temperature and emissivity spatially resolved to obtain further insight into the process. Here, we present results of an initial study using multispectral thermography to obtain real temperatures and emissivities in the powderfree LMD process.
Introduction to ProMoAM
(2021)