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Material qualification for laser powder bed fusion (L-PBF) processes are often based on results derived from additively manufactured (AM) bulk material or small density cubes, although it is well known that the part geometry has a tremendous influence on the heat flux and, therefore, on the thermal history of an AM component. This study shows experimentally the effect of simple geometrical obstructions to the heat flux on cooling behavior and solidification conditions of 316L stainless steel processed by L-PBF. Additionally, it respects two distinct inter layer times (ILT) as well as the build height of the parts. The cooling behavior of the parts is in-situ traced by infrared (IR) thermography during the built-up. The IR signals reveal significant differences in cooling conditions, which are correlated to differences in melt pool geometries. The acquired data and results can be used for validation of computational models and improvements of quality assurance.
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.
Microstructure characterisation of advanced materials via 2D and 3D X-ray refraction techniques
(2018)
3D imaging techniques have an enormous potential to understand the microstructure, its evolution, and its link to mechanical, thermal, and transport properties. In this conference paper we report the use of a powerful, yet not so wide-spread, set of X-ray techniques based on refraction effects. X-ray refraction allows determining internal specific surface (surface per unit volume) in a non-destructive fashion, position and orientation sensitive, and with a nanometric detectability. We demonstrate showcases of ceramics and composite materials, where microstructural parameters could be achieved in a way unrivalled even by high-resolution techniques such as electron microscopy or computed tomography. We present in situ analysis of the damage evolution in an Al/Al2O3 metal matrix composite during tensile load and the identification of void formation (different kinds of defects, particularly unsintered powder hidden in pores, and small inhomogeneity’s like cracks) in Ti64 parts produced by selective laser melting using synchrotron X-ray refraction radiography and tomography.
Microstructure characterisation of advanced materials via 2D and 3D X-ray refraction techniques
(2018)
3D imaging techniques have an enormous potential to understand the microstructure, its evolution, and its link to mechanical, thermal, and transport properties. In this conference paper we report the use of a powerful, yet not so wide-spread, set of X-ray techniques based on refraction effects. X-ray refraction allows determining internal specific surface (surface per unit volume) in a non-destructive fashion, position and orientation sensitive, and with a nanometric detectability. We demonstrate showcases of ceramics and composite materials, where microstructural parameters could be achieved in a way unrivalled even by high-resolution techniques such as electron microscopy or computed tomography. We present in situ analysis of the damage evolution in an Al/Al2O3 metal matrix composite during tensile load and the identification of void formation (different kinds of defects, particularly unsintered powder hidden in pores, and small inhomogeneity’s like cracks) in Ti64 parts produced by selective laser melting using synchrotron X-ray refraction radiography and tomography.
Laser based powder bed fusion additive manufacturing offers the flexibility to incorporate standard and userdefined scan strategies in a layer or in between the layers for the customized fabrication of metallic components. In the present study, four different scan strategies and their impact on the development of microstructure, texture, and residual stresses in laser powder bed fusion additive manufacturing of a nickel-based superalloy Inconel 718 was investigated. Light microscopy, scanning electron microscopy combined with electron backscatter diffraction, and neutron diffraction were used as the characterization tools. Strong textures with epitaxially grown columnar grains were observed along the build direction for the two individual scan strategies. Patterns depicting the respective scan strategies were visible in the build plane, which dictated the microstructure development in the other planes. An alternating strategy combining the individual strategies in the successive layers and a 67◦ rotational strategy weakened the texture by forming finer microstructural features. Von Mises equivalent stress plots revealed lower stress values and gradients, which translates as lower distortions for the alternating and rotational strategies. Overall results confirmed the scope for manipulating the microstructure, texture, and residual stresses during laser powder bed fusion additive manufacturing by effectively controlling the scan strategies.
As a part of ProMoAM project, we are optimizing a prototype X-ray backscatter to reach NDT requirements, and thereafter to apply it for process monitoring. Moreover, we studied the capability of a radiography approach to detect artificial defects in AM components made by laser powder bed fusion (L-PBF).
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing
The osseointegration in/around additively manufactured (AM) lattice structures of a new titanium alloy, Ti–19Nb–14Zr, was evaluated. Different lattices with increasingly high sidewalls gradually closing them were manufactured and implanted in sheep. After removal, the bone–interface implant (BII) and bone–implant contact (BIC) were studied from 3D X-ray computed tomography images. Measured BII of less than 10 µm and BIC of 95% are evidence of excellent osseointegration.
Since AMnaturally leads to a high-roughness surface finish, the wettability of the implant is increased.
The new alloy possesses an increased affinity to the bone. The lattice provides crevices in which the biological tissue can jump in and cling. The combination of these factors is pushing ossification beyond its natural limits. Therefore, the quality and speed of the ossification and osseointegration in/around these Ti–19Nb–14Zr laterally closed lattice implants open the possibility of bone spline key of prostheses. This enables the stabilization of the implant into the bone while keeping the possibility of punctual hooks allowing the implant to be removed more easily if required. Thus, this new titanium alloy and such laterally closed lattice structures are appropriate candidates to be implemented in a new generation of implants.
Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network.
[1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022)
https://dx.doi.org/10.1016/j.imatprotec.2021.117473
[2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013)
https://dx.doi.org/10.1007/978-3-642-40669-0_33
The manufacturing of metal parts for the use in safety-relevant applications by Laser Powder Bed Fusion (L-PBF) demands a quality assurance of both part and process. Thermography is a nondestructive testing method that allows the in-situ determination of the thermal history of the produced part which is connected to the mechanical properties and the formation of defects [1]. A wide range of commercial thermographic camera systems working in different spectral ranges is available on the market. The understanding of the applicability of these cameras for qualitative and quantitative in-situ measurements in L-PBF is of vital importance [2]. In this study, the building process of a cylindrical specimen (Inconel 718) is monitored by three camera systems simultaniously. These camera systems are sensitive in various spectral bandwidths providing information in different temperature ranges. The performance of each camera system is explored in the context of the extraction of image features for the detection of defects. It is shown that the high temporal and thermal process dynamics are limiting factors on this matter. The combination of different spectral camera systems promises the potential of an improved defect detection by data 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 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.
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 appearance of irregularities such as keyhole porosity is a major challenge for the production of metal parts by laser powder bed fusion (PBF-LB/M). The utilization of thermographic in-situ monitoring is a promising approach to extract the thermal history which is closely related to the formation of irregularities. In this study, we investigate the utilization of convolutional neural networks to predict keyhole porosity based on thermographic features. Here, the porosity information calculated from an x-ray micro computed tomography scan is used as reference. Feature engineering is performed to enable the model to learn the complex physical characteristics of the porosity formation. The model is examined with regard to the choice of hyperparameters, the significance of thermal features and characteristics of the data acquisition. Based on the results, future demands on irregularity prediction in PBF-LB/M are derived.
Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived.
The formation of irregularities such as keyhole porosity pose a major challenge to the manufacturing of metal parts by laser powder bed fusion (PBF-LB/M). In-situ thermography as a process monitoring technique shows promising potential in this manner since it is able to extract the thermal history of the part which is closely related to the formation of irregularities. In this study, we investigate the utilization of machine learning algorithms to detect keyhole porosity on the base of thermographic features. Here, as a referential technique, x-ray micro computed tomography is utilized to determine the part's porosity. An enhanced preprocessing workflow inspired by the physics of the keyhole irregularity formation is presented in combination with a customized model architecture. Furthermore, experiments were performed to clarify the role of important parameters of the preprocessing workflow for the task of defect detection . Based on the results, future demands on irregularity prediction in PBF-LB/M are derived.
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 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.
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 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.
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.
While laser powder-bed fusion has overcome some of the design constraints of conventional manufacturing methods, it requires careful selection of process parameters and scan strategies to obtain favorable properties. Here we show that even simple scan strategies, complex ones being inevitable when printing intricate designs, can inadvertently produce local alterations of the microstructure and preferential grain orientation over small areas – which easily remain unnoticed across the macroscale. We describe how a combined usage of neutron imaging and electron backscatter diffraction can reveal these localized variations and explain their origin within cm-sized parts. We explain the observed contrast variations by linking the neutron images to simulated data, pole figures and EBSD, providing an invaluable reference for future studies and showing that presumably minor changes of the scan strategy can have detrimental effects on the mechanical properties. In-situ tensile tests reveal that fracture occurs in a region that was re-melted during the building process.
The influence of the process gas, laser scan speed, and sample thickness on the build-up of residual stresses and porosity in Ti-6Al-4V produced by laser powder bed fusion was studied. Pure argon and helium, as well as a mixture of those (30% helium), were employed to establish process atmospheres with a low residual Oxygen content of 100 ppm O2. The results highlight that the subsurface residual stresses measured by X-ray diffraction were significantly lower in the thin samples (220 MPa) than in the cuboid samples (645 MPa). This difference was attributed to the shorter laser vector length, resulting in heat accumulation and thus in-situ stress relief. The addition of helium to the process gas did not introduce additional subsurface residual stresses in the simple geometries, even for the increased scanning speed. Finally, larger deflection was found in the cantilever built under helium (after removal from the baseplate), than in those produced under argon and an argon-helium mixture. This result demonstrates that complex designs involving large scanned areas could be subjected to higher residual stress when manufactured under helium due to the gas’s high thermal conductivity, heat capacity, and thermal diffusivity.
Contour scanning and process gas type are process parameters typically considered achieving second order effects compared to first order factors such as laser power and scanning speed. The present work highlights that contour scanning is crucial to ensure geometrical accuracy and thereby the high performance under uniaxial compression of complex Alloy 718 lattice structures. Studies of X-ray computed tomography visualizations of as-built and compression-strained structures reveal the continuous and smooth bending and compression of the walls, and the earlier onset of internal contact appearance in the denser lattices printed with contour. In contrast, the effect of addition of He to the Ar process gas appears to have limited influence on the mechanical response of the lattices and their microstructure as characterized by electron backscattered diffraction. However, the addition of He proved to significantly enhance the cooling rate and to reduce the amount of the generated spatters as evidenced by in situ monitoring of the process emissions, which is very promising for the process stability and powder reusability during laser powder bed fusion.
Direct energy deposition additive manufacturing technologies that utilize an electric arc have great potential for generating large volume metal components. However, selecting process parameters that yield the desired near net shape design and requested mechanical component behavior is not a trivial task due to the complex relationship between all process parameters and material characteristics. This presentation exemplifies the application of a newly developed solid welding wire doped with TiB to enhance grain refinement in the deposited metal for additive manufacturing based on DED-Arc of high-strength precipitation hardening AlMgSi-aluminum alloys. It is worth noting that the solid wire is the result of our preliminary metallurgical studies on grain refinement in aluminum weld metal.
Consequently, research focuses on the correlation between process parameters and component quality to understand the underlying mechanisms. This is crucial for evaluating a robust process parameter space that yields component quality in line with corresponding standards which are mainly taken from welding technology.
Specifically, we examine component quality by analyzing pore size and distribution, as well as grain morphology. To enhance the mechanical properties of the deposited metal, a post-weld heat treatment was conducted, comprising of solution treatment, quenching, and artificial aging. The study also evaluates the effects of various heat treatment strategies on the final mechanical properties of the material.
To demonstrate the applicability of 3D metal printing of high-strength aluminium alloys, a more complex demonstrator was created. It has been shown that DED-Arc can produce high-volume aluminium parts with the same quality as the corresponding subtractive processing strategy.
Additionally, the entire additive manufacturing chain has been digitally integrated, enabling traceability of all relevant process steps, which is essential for reliable subsequent quality assessment.
Frass (fine powdery refuse or fragile perforated wood produced by the activity of boring insects) of larvae of the European house borer (EHB) and of drywood termites was tested as a natural and novel feedstock for 3D-printing of wood-based materials. Small particles produced by the drywood termite Incisitermes marginipennis and the EHB Hylotrupes bajulus during feeding in construction timber, were used. Frass is a powdery material of particularly consistent quality that is essentially biologically processed wood mixed with debris of wood and faeces. The filigree-like particles flow easily permitting the build-up of woodbased structures in a layer wise fashion using the Binder Jetting printing process. The Quality of powders produced by different insect species was compared along with the processing steps and properties of the printed parts. Drywood termite frass with a Hausner Ratio HR = 1.1 with ρBulk = 0.67 g/cm3 and ρTap = 0.74 g/cm3 was perfectly suited to deposition of uniformly packed layers in 3D printing. We suggest that a variety of naturally available feedstocks could be used in environmentally responsible approaches to scientific material sciences/additive manufacturing.
In this presentation we discuss the online monitoring of metallic AM parts produced by the Laser Powder Bed Fusion (LPBF) process by using optical, thermographic and electromagnetic methods. In a first approach we present the detection of defects generated during the process and discuss how to improve these methods for the optimization of design and production of metallic AM parts.
The European Metrology Network (EMN) for Advanced Manufacturing has been established in June 2021. Currently nine EMNs focussing on different important topics of strategic importance for Europe exist and form an integral part of EURAMET, the European Association of National Metrology Institutes (NMI). EMNs are tasked to ▪ develop a high-level coordination of the metrology community in Europe in a close dialogue with the respective stakeholders (SH)
▪ develop a strategic research agenda (SRA) within their thematic areas
▪ provide contributions to the European Partnership on Metrology research programme Based on the analysis of existing metrology infrastructures and capabilities of NMIs, the metrology research needs for advanced manufacturing are identified in close cooperation with academic, governmental and industrial stakeholders.
Here, we report on the progress of the EMN for Advanced Manufacturing.
Advanced manufacturing has been identified as one of the key enabling technologies with applications in multiple industries. The growing importance of advanced manufacturing is reflected by an increased number of publications on this topic in recent years. Advanced manufacturing requires new and enhanced metrology methods to assure the quality of manufacturing processes and the resulting products. However, a high-level coordination of the metrology community is currently absent in this field and consequently this limits the impact of metrology developments on advanced manufacturing. In this article we introduce the new European Metrology Network (EMN) for Advanced Manufacturing within EURAMET, the European Association of National Metrology Institutes (NMIs). The EMN is intended to be operated sustainably by NMIs and Designated Institutes in close cooperation with Stakeholders interested in advanced manufacturing. The objectives of the EMN are to set up a permanent stakeholder dialogue, to develop a Strategic Research Agenda for the metrology input required for advanced manufacturing technologies, to create and maintain a knowledge sharing programme and to implement a web-based service desk for stakeholders. The EMN development is supported by a Joint Network Project within the European Metrology Programme for Innovation and Research.
Laser Powder Bed Fusion (L-PBF) allow the fabrication of lightweight near net shape AlSi10Mg components attractive to the aerospace, automotive, biomedical and military industries. During the build-up process, high cooling rates occur. Thus, L-PBF AlSi10Mg alloys exhibit a Si-nanostructure in the as-built condition, which leads to superior mechanical properties compared to conventional cast materials. At the same time, such high thermal gradients generally involve a deleterious residual stress (RS) state that needs to be assessed during the design process, before placing a component in service. To this purpose post-process heat treatments are commonly performed to relieve detrimental RS. In this contribution two low-temperature stress-relief heat treatments (SRHT) are studied and compared with the as-built state: a SRHT at 265°C for 1 hour and a SRHT at 300°C for 2 hours. At these temperatures microstructural changes occur. In the as-built state, Si atoms are supersaturated in the α-aluminium matrix, which is enveloped by a eutectic Si-network. At 265°C the Si precipitation from the matrix to the pre-existing network is triggered. Thereafter, above 295°C the fragmentation and spheroidization of the Si branches takes place, presumably by Al–Si interdiffusion. After 2 hours the original eutectic network is completely replaced by uniformly distributed blocky particles. The effect of the heat and the microstructure modification on the RS state and the fatigue properties is investigated. Energy dispersive x-ray and neutron diffraction are combined to investigate the near-surface and bulk RS state of a L-PBF AlSi10Mg material. Differences in the endurance limit are evaluated experimentally by high cycle fatigue (HCF) tests and cyclic R-curve determination.
Al-Si alloys produced by Laser Powder Bed Fusion (L-PBF) techniques allow the fabrication of lightweight free-shape components. Due to the high cooling rates occurring during the building process, L-PBF AlSi10Mg alloys exhibit an ultra-fine microstructure that leads to superior mechanical properties in the as-built condition compared to conventional cast Al-Si materials. Nevertheless, L-PBF processing induces high thermal gradients, leading to deleterious residual stress. In order to relax detrimental residual stress and to increase the ductility, post-processing stress relief treatments are performed. The objective of the contribution is to investigate, under different heat treatment condition, the evolution of microstructure and residual stresses in view of optimizing the fatigue performance of the alloy. To this purpose various heat treatments in a range of temperatures between 265°C and 300°C for a duration between 15 minutes and 2 hours are performed.
Al-Si alloys produced by Laser Powder Bed Fusion (PBF-LB/M) techniques allow the fabrication of lightweight free-shape components. Due to the extremely heterogeneous cooling and heating, PBF-LB/M induces high magnitude residual stress (RS) and a fine Si microstructure. As the RS can be deleterious to the fatigue resistance of engineering components, great efforts are focused on understanding their evolution before and after post-process heat treatments (HT).
AbstractIn this study, we compare the residual stress state in a laser powder bed fusion (PBF‐LB/M) AlSi10Mg alloy in the as‐built (AB) condition with that after two different heat treatments (265 °C for 1 h, HT1; and 300 °C for 2 h, HT2). The bulk residual stress (RS) is determined using synchrotron X‐ray diffraction (SXRD), and near‐surface profiles are determined using laboratory energy‐dispersive X‐ray diffraction (EDXRD). The EDXRD results do not reveal any notable difference between the conditions at a depth of 350 μm, suggesting that the machining process yields a comparable residual stress state in the near‐surface regions. On the other hand, the SXRD results show that HT1 is more effective in relieving the bulk RS. It is observed that HT1 reduces the RS state in both the aluminium matrix and the silicon network. In addtion, HT2 does not have a significant impact on relaxing the RS as‐built state of the matrix, although it does induce a reduction in the RS magnitudes of the Si phase. It is concluded that the heat treatment stress relieving is effective as long as the Si‐network is not disaggregated.
The microstructure has a great influence on short fatigue crack growth in metallic materials. Laser-based Powder Bed Fusion AlSi10Mg alloys exhibit in the as-built condition a fine fibrous Si structure and a supersaturated solid solution of Si in the α-Al matrix, which is significantly modified by heat treatments starting already at temperatures under 260 °C. This study focuses on the influence of post-process heat treatments on the microstructural evolution and the resulting fatigue crack growth resistance. As compared to the as-built condition, two heat treatments at 265 °C/1 h and at 300 °C/2 h are found to be beneficial to the fatigue crack growth resistance of the investigated material.
Al-Si alloys produced by Laser Powder Bed Fusion (L-PBF) techniques allow the fabrication of lightweight free-shape components that find space in aerospace, automotive, biomedical and military applications. Due to the high cooling rates occurring during the building process, L-PBF AlSi10Mg alloys exhibit an ultra-fine microstructure that leads to superior mechanical properties in the as-built condition compared to conventional cast Al-Si materials. Nevertheless, L-PBF processing induces high thermal gradients, leading to deleterious residual stress levels that must be considered to avoid part distortion and unpredicted failures. In order to relax detrimental residual stress and to increase the ductility, post-processing stress relief treatments are generally performed. In as-built condition the hypoeutectic AlSi10Mg microstructure consist of fine α-Al cells containing uniformly dispersed silicon nanoparticles, which are, in addition, surrounded by a eutectic Si network. Above 260°C the silicon interconnectivity starts to breakdown into spheroidized particles and to coarsen. At the same time, the heating residual stresses are relieved.
The objective of the contribution is to investigate, under different heat treatment conditions, the evolution of microstructure and residual stresses in view of optimizing the fatigue performance of the alloy. To this purpose various heat treatments in a range of temperatures between 265°C and 300°C for a duration between 15 minutes and 2 hours are performed. The microstructure modifications are analysed using a scanning electron microscope and the residual stress state is measured by laboratory X-ray diffraction.
Influence of a 265 °C heat treatment on the residual stress state of a PBF-LB/M AlSi10Mg alloy
(2022)
Laser Powder Bed Fusion (PBF-LB/M) additive manufacturing (AM) induces
high magnitude residual stress (RS) in structures due to the extremely heterogeneous cooling and heating rates. As the RS can be deleterious to the fatigue resistance of engineering components, great efforts are focused on understanding their generation and evolution after post-process heat treatments. In this study, one of the few of its kind, the RS relaxation induced in an as-built PBF-LB/M AlSi10Mg material by a low-temperature heat treatment (265 °C for 1 h) is studied by means of X-ray and neutron diffraction. Since the specimens are manufactured using a baseplate heated up to 200 °C, low RS are found in the as-built condition. After heat treatment a redistribution of the RS is observed, while their magnitude remains constant. It is proposed that the redistribution is induced by a repartition of stresses between the a-aluminium matrix and the silicon phase, as the morphology of the silicon phase is affected by the heat treatment. A considerable scatter is observed in the neutron diffraction RS profiles, which is principally correlated to the presence (or absence) of pockets of porosity developed at the borders of the chessboard pattern.
By allowing economic on demand manufacturing of highly customized and complex workpieces, metal based additive manufacturing (AM) has the prospect to revolutionize many industrial areas. Since AM is prone to the formation of defects during the building process, a fundamental requirement for AM to become applicable in most fields is the ability to guarantee the adherence to strict quality and safety standards. A possible solution for this problem lies in the deployment of various in-situ monitoring techniques. For most of these techniques, the application to AM is still very poorly understood. Therefore, the BAM in its mission to provide safety in technology has initiated the project “Process Monitoring of AM” (ProMoAM). In this project, a wide range of in-situ process monitoring techniques, including active and passive thermography, optical tomography, optical emission and absorption spectroscopy, eddy current testing, laminography, X-ray backscattering and photoacoustic methods, are applied to laser metal deposition (LMD), laser powder bed fusion and wire arc AM. Since it is still unclear which measured quantities are relevant for the detection of defects, these measurements are performed very thoroughly. In successive steps, the data acquired by all these methods is fused and compared to the results of reference methods such as computer tomography and ultrasonic immersion testing. The goal is to find reliable methods to detect the formation of defects during the building process. The detailed acquired data sets may also be used for comparison with simulations.
Here, we show first results of high speed (> 300 Hz) thermographic measurements of the LMD process in the SWIR range using 316L as building material. For these experiments, the camera was mounted fixed to the welding arm of the LMD machine to keep the molten pool in focus, regardless of the shape of the specimen. As the thermograms do not contain any information about the current spatial position during the building process, we use an acceleration sensor to track the movement and synchronize the measured data with the predefined welding path. This allows us to reconstruct the geometry of the workpieces and assign the thermographic data to spatial positions. Furthermore, we investigate the influence of the acquisition wavelength on the thermographic data by comparing measurements acquired with different narrow bandpass filters (50 nm FWHM) in a spectral range from 1150 nm to 1550 nm.
This research was funded by BAM within the Focus Area Materials.
The formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The LPBF process is characterized by a large number of influencing factors which can be hard to quantify. Machine Learning (ML) is a prominent tool to predict the outcome of complex processes on the basis of different sensor data. In this study, a ML model for defect prediction is created using thermographic image features as input data. As a reference, the porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan of the produced specimen is used. Physical knowledge about the keyhole pore formation is incorporated into the model to increase the prediction accuracy. From the prediction result, the quality of the input data is evaluated and future demands on in-situ monitoring of LPBF processes are formulated.
Infrared thermography is a technique that allows to measure the temperatures of objects by analyzing the intensity of the thermal emission without the need of direct contact with very high spatial and temporal resolution. As the temperature is a fundamental factor for the additive manufacturing processes of metals, infrared thermography can provide experimental data that can be used for the validation of simulations and improving the understanding of the processes as well as for in-situ process monitoring for nondestructive evaluation (NDE) for quality control.
In this talk we will provide an overview over the possibilities of state of the art thermographic in-situ monitoring systems for the DED-LB/M and PBF-LB/M processes and the challenges such as phase transitions and unknown emissivity values in respect to the determination of real temperatures. We define the requirements for different camera systems in various configurations and give examples on the selection of appropriate measurement parameters and data acquisition techniques as well as on techniques for data analysis and interpretation. Finally, we compare in-situ monitoring methods against post NDE methods by analyzing the advantages and disadvantages of both.
This research was funded by BAM within the Focus Area Materials.
For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M ) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring) but not researched to an extent that allows to guarantee the adherence to strict quality and safety standards.
In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques.
In this paper shortwave infrared (SWIR) thermographic measurements of the manufacturing of thin single-line walls via laser metal deposition (LMD) are presented. As the thermographic camera is mounted fixed to the welding arm, an acceleration sensor was used to assist in reconstructing the spatial position from the predefined welding path. Hereby we could obtain data sets containing the size of the molten pool and the oxide covered areas as functions of the position in the workpiece. Furthermore, the influence of the acquisition wavelength onto the thermograms was investigated in a spectral range from 1250 nm to 1550 nm. All wavelengths turned out to be usable for the in-situ process monitoring of the LMD process. The longer wavelengths are shown to be beneficial for the lower temperature range, while shorter wavelengths show more details within the molten pool.
For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring), which have not been researched to an extent that allows to guarantee the adherence to strict quality and safety standards.
In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques.
Since additive manufacturing processes typically introduce heterogeneous microstructures and residual stresses, the applicability of parts produced in an as-built state is limited. Therefore, often different post-processing treatments are necessary to obtain the desired stress state and properties.
For additively manufactured Inconel 718, the recently developed standard ASTM F3301 provides guidance for the heat treatment of powder bed fusion specimens. Although this standard is based on standards developed for wrought Inconel 718, it does not include the direct aging variant. In this study, we characterized the microstructure and tensile behavior of Inconel 718 specimens produced by a laser powder bed fusion process. The specimens were heat-treated according to two different routines after stress relieving: a full heat treatment cycle versus a one-step aging process. Differences in the resulting texture and grain morphology were observed. Although these differences prevail, the ex-situ tensile behavior was broadly similar. Minor differences were observed in yield strength and work hardening rate for the direct aged specimen. In order to understand this behavior, investigations with in-situ tensile testing during synchrotron energy-dispersive X-ray diffraction measurements revealed differences in the load partitioning among different crystal directions. This was attributed to microstructural differences between the heat treatment variants. Further analysis emphasized that the various strengthening mechanisms are present to a different extent depending on the heat-treatment cycle applied. In addition, the elastic anisotropy expressed by the differences in the diffraction elastic constants displayed a dependence on the microstructure. Importantly, a precise knowledge of such constants is indispensable to reliably determine residual stresses in parts.
In this presentation, the results of the determination of the diffraction and single-crystal elastic constants of laser powder bed fused Inconel 718 are presented. The analysis is based on high-energy synchrotron diffraction experiments performed at the Deutsches Elektronen-Synchrotron. It is shown that the characteristic microstructure of laser powder bed fused Inconel 718 impacts the elastic anisotropy and therefore the diffraction and single-crystal elastic constants. Finally, the consequences on the diffraction-based residual stress determination of laser powder bed fused Inconel 718 are discussed.
The interest to additively manufacture Nickel-based superalloys has substantially grown within the past decade both academically and industrially. More specifically, additive manufacturing processes such as laser powder bed fusion (LPBF) offer the ability to produce dense parts within a single manufacturing step. In fact, the exceptional freedom in design associated with the layer-based nature of the processes is of particular interest for the complex shapes typically required in turbine applications. In certain cases, the overall part performance can be achieved by tailoring the microstructure and the crystallographic texture to the specific application. However, these advantages must be paid at a price: the large local temperature gradients associated with the rapid melting and solidification produce parts that inherently contain large residual stress in the as-manufactured state. In addition, the presence of pores in the final part may further affect the in-service part failure. As among Nickel-based alloys Inconel 718 exhibits excellent weldability, this alloy has been widely studied in open research in the domain of LPBF. However, significant microsegregation of the heavier alloying elements such as Niobium and Molybdenum accompanied by dislocation entanglements may preclude the application of conventional heat treatment schedules. Therefore, different post processing heat treatments are required for laser powder bed fused Inconel 718 as compared to conventional variants of the same alloy.
In this study, we investigated two different heat treatment routes for LPBF Inconel 718. In a first routine, the samples were stress relieved and subsequently subjected to hot isostatic pressing (HIP) followed by a solution heat treatment and a two-step age (referred to as FHT). In a second routine, the samples were subjected to a single-step direct age post stress relieving heat treatment (referred to DA). We investigated the consequences of such heat treatment schedules on the microstructure, texture, and mechanical behavior. We show that by applying a DA heat treatment the typical columnar microstructure possessing a crystallographic texture is retained, while an equiaxed untextured microstructure prevails in case of an FHT heat treatment. We further evaluate how these heat treatments affect the mechanical behaviour on the macroscopic and microscopic scale.
Laser powder bed fusion (PBF-LB/M) of metallic alloys is a layer-wise additive manufacturing process that provides significant scope for more efficient designs of components, benefiting performance and weight, leading to efficiency improvements for various sectors of industry. However, to benefit from these design freedoms, knowledge of the high produced induced residual stress and mechanical property anisotropy associated with the unique microstructures is critical. X-ray and neutron diffraction are considered the benchmark for non-destructive characterization of surface and bulk internal residual stress. The latter, characterized by the high penetration power in most engineering alloys, allows for the use of a diffraction angle close to 90° enabling a near cubic sampling volume to be specified. However, the complex microstructures of columnar growth with inherent crystallographic texture typically produced during PBF-LB/M of metallics present significant challenges to the assumptions typically required for time efficient determination of residual stress. These challenges include the selection of an appropriate set of diffraction elastic constants and a representative lattice plane suitable for residual stress analysis. In this contribution, the selection of a suitable lattice plane family for residual stress analysis is explored. Furthermore, the determination of an appropriate set of diffraction and single-crystal elastic constants depending on the underlying microstructure is addressed.
In-situ loading experiments have been performed at the Swiss Spallation Neutron Source with the main scope to study the deformation behaviour of laser powder bed fused Inconel 718. Cylindrical tensile bars have been subjected to an increasing mechanical load. At pre-defined steps, neutron diffraction data has been collected. After reaching the yield limit, unloads have been performed to study the accumulation of intergranular strain among various lattice plane families.
The general term additive manufacturing (AM) encompasses processes that enable the production of parts in a single manufacturing step. Among these, laser powder bed fusion (PBF-LB) is one of the most commonly used to produce metal components. In essence, a laser locally melts powder particles in a powder bed layer-by-layer to incrementally build a part. As a result, this process offers immense manufacturing flexibility and superior geometric design capabilities compared to conventional processes. However, these advantages come at a cost: the localized processing inevitably induces large thermal gradients, resulting in the formation of large thermal stress during manufacturing. In the best case, residual stress remains in the final parts produced as a footprint of this thermal stress. Since residual stress is well known to exacerbate the structural integrity of components, their assessment is important in two respects. First, to optimize process parameter to minimize residual stress magnitudes. Second, to study their effect on the structural integrity of components (e.g., validation of numerical models). Therefore, a reliable experimental assessment of residual stress is an important factor for the successful application of PBF-LB. In this context, diffraction-based techniques allow the non-destructive characterization of the residual stress. In essence, lattice strain is calculated from interplanar distances by application of Braggs law. From the known lattice strain, macroscopic stress can be determined using Hooke’s law. To allow the accurate assessment of the residual stress distribution by such methods, a couple of challenges in regard of the characteristic PBF-LB microstructures need to be overcome.
This presentation highlights some of the challenges regarding the accurate assessment of residual stress in PBF-LB on the example of the Nickel-based alloy Inconel 718. The most significant influencing factors are the use of the correct diffraction elastic constants, the choice of the stress-free reference, and the consideration of the crystallographic texture. Further, it is shown that laboratory X-ray diffraction methods characterizing residual stress at the surface are biased by the inherent surface roughness. Overall, the impact of the characteristic microstructure is most significant for the selection of the correct diffraction elastic constants. In view of the localized melting and solidification, no significant gradients of the stress-free reference are observed, even though the cell-like solidification sub-structure is known to be heterogeneous on the micro-scale.
Additive manufacturing methods such as laser powder bed fusion (LPBF) allow geometrically complex parts to be manufactured within a single step. However, as an aftereffect of the localized heat input, the rapid cooling rates are the origin of the large residual stress (RS) retained in as-manufactured parts. With a view on the microstructure, the rapid directional cooling leads to a cellular solidification mode which is accompanied by columnar grown grains possessing crystallographic texture. The solidification conditions can be controlled by the processing parameters and the scanning strategy. Thus, the process allows one to tailor the microstructure and the texture to the specific needs. Yet, such microstructures are not only the origin of the mechanical anisotropy but also pose metrological challenges for the diffraction-based RS determination. In that context the micromechanical elastic anisotropy plays an important role: it translates the measured microscopic strain to macroscopic stress. Therefore, it is of uttermost importance to understand the influence of the hierarchical microstructures and the texture on the elastic anisotropy of LPBF manufactured materials.
This study reveals the influence of the build orientation and the texture on the micro-mechanical anisotropy of as-built Inconel 718. Through variations of the build orientation and the scanning strategy, we manufactured specimens possessing [001]/[011]-, [001]-, and [011]/[111]-type textures. The resulting microstructures lead to differences in the macroscopic mechanical properties. Even further, tensile in-situ loading experiments during neutron diffraction measurements along the different texture components revealed differences in the microstrain response of multiple crystal lattice planes. In particular, the load partitioning and the residual strain accumulation among the [011]/[111] textured specimen displayed distinct differences measured up to a macroscopic strain of 10 %. However, the behavior of the specimens possessing [001]/[011]-and [001]-type texture was only minorly affected. The consequences on the metrology of RS analysis by diffraction-based methods are discussed.