Additive Fertigung
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Direct energy deposition additive manufacturing technologies utilizing an electric arc offer a great potential in generating large volume metal components. However, the selection of process parameters that yield the desired near net shape design as well as the requested mechanical component behavior is not a trivial task due to the complex relationship. Exemplarily for additive manufacturing of high-strength precipitation hardening AlMgSi-aluminum alloy this paper shows the application of a newly developed matching solid welding wire doped with TiB as grain refiner. The correlation between process parameters and component quality is examined analyzing the size and distribution of pores as well as the grain morphology. Furthermore, the influences of different post-weld heat treatments are evaluated to meet the reference mechanical properties of the corresponding wrought material. Finally, the digital integration of the entire additive manufacturing chain enables an overall traceability of the relevant process steps which is the basis for a reliable subsequent quality assessment.
In manufacturing, fusion welding processes use a lot of resources, which presents an opportunity to reduce environmental impact. While there is a general understanding of the environmental impact of these processes, it is difficult to quantitatively assess key parameters. This study introduces a welding-specific methodology that uses life cycle assessment (LCA) to evaluate the environmental impact of fusion welding technologies. Our approach analyses the main parameters that affect the environmental performance of different welding techniques, including traditional methods and additive manufacturing through the Direct Energy Deposition-Arc (DED-Arc) process. We integrate real-time resource usage data to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact, facilitating the advancement of sustainable manufacturing practices.
Life cycle assessment of fusion welding processes considering upstream and downstream process steps
(2023)
In manufacturing, fusion welding processes consume significant resources, presenting a significant opportunity for reducing environmental impact. Although there is a qualitative understanding of the environmental implications of these processes, a quantitative assessment of key parameters remains complex. This study introduces a welding-specific methodology that employs life cycle assessment (LCA) to quantitatively evaluate the environmental footprint of fusion welding technologies. Our approach identifies and analyses the principal parameters affecting the environmental performance of various welding techniques, including traditional joint welding and additive manufacturing via the Direct Energy Deposition-Arc (DED-Arc) process. Real-time resource usage data is integrated to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact. This facilitates the advancement of sustainable manufacturing practices.
Numerical and experimental assessment of liquid metal embrittlement in externally loaded spot welds
(2024)
Zinc-based surface coatings are widely applied with high-strength steels in automotive industry. Some of these base materials show an increased brittle cracking risk during loading. It is necessary to examine electrogalvanized and uncoated samples of a high strength steel susceptible to liquid metal embrittlement during spot welding with applied external load. Therefore, a newly developed tensile test method with a simultaneously applied spot weld is conducted. A fully coupled 3D electrical, thermal, metallurgical and mechanical finite element model depicting the resistant spot welding process combined with the tensile test conducted is mandatory to correct geometric influences of the sample geometry and provides insights into the sample’s time dependent local loading. With increasing external loads, the morphology of the brittle cracks formed is affected more than the crack depth. The validated finite element model applies newly developed damage indicators to predict and explain the liquid metal embrittlement cracking onset and development as well as even ductile failure.
Abstract
In the post-processing of large maritime components, a considerable amount of waste in the form of milling and grinding chips is produced. At the same time, additive manufacturing technologies have shown great potential in producing high-volume parts for maritime applications, allowing novel design approaches and short lead times. In this context, this study presents a sustainable approach to recycle and use aluminium bronze waste material, generated during post-processing of large cast ship propellers, as feedstock for laser-powder directed energy deposition. The recycling technology used to produce powder batches is inductive re-melting in combination with ultrasonic atomization. The derived metal powders are characterized using digital image analysis, powder flowability tests, scanning electron microscopy as well as energy dispersive X-ray spectroscopy. Compared to conventional metal powders produced by gas atomization, the recycled material shows excellent sphericity and a powder size distribution with a higher content of finer and coarser particles. Metallographic sections of deposited additively produced specimens show an increased hardness and reduced ductility, but also competitive densities and higher yield and ultimate tensile strength compared to cast material. The process chain shows high potential for the maritime sector to enable circular and sustainable manufacturing.
The present work deals with the recently confirmed widening of the weld pool interface, known as a bulging effect, and its relevance in high power laser beam welding. A combined experimental and numerical approach is utilized to study the influence of the bulge on the hot cracking formation and the transport of alloying elements in the molten pool. A technique using a quartz glass, a direct-diode laser illumination, a high-speed camera, and two thermal imaging cameras is applied to visualize the weld pool geometry in the longitudinal section. The study examines the relevance of the bulging effect on both, partial and complete penetration, as well as for different sheet thicknesses ranging from 8 mm to 25 mm. The numerical analysis shows that the formation of a bulge region is highly dependent on the penetration depth and occurs above 10 mm penetration depth. The location of the bulge correlates strongly with the cracking location. The obtained experimental and numerical results reveal that the bulging effect increases the hot cracking susceptibility and limits the transfer of alloying elements from the top of the weld pool to the weld root.
Liquid metal embrittlement (LME) cracking is a phenomenon observed during resistance spot welding (RSW) of zinccoated advanced highstrength steels (AHSS) in automotive manufacturing. In this study, severe cracks are observed at the edge of the sheet under reduced flange widths. These cracks, traversing the AHSS sheet, culminate at the edge with a width of approximately 1.2 mm.
Through combined numerical and experimental investigations, and material testing, these cracks are identified and validated as a new type of LME crack. The mechanism behind this crack formation is attributed to unique geometric conditions that, when compared to center welding, amplify radial material flow by ninefold to 0.87 mm. The resultant tangential tensile stresses approximate 760 MPa, which exceed the yield strength of the examined advanced highstrength steel (AHSS) under heightened temperature conditions, and when combined with liquid zinc, promote the formation of this new type of LME crack.
New developments in nickel-based superalloys and production methods, such as the use of additive manufacturing (AM), can result in innovative designs for turbines. It is crucial to understand how the material behaves during the AM process to advance industrial use of these techniques. An analytical model based on reaction-diffusion formalism is developed to better explain the solidification behavior of the material during laser metal deposition (LMD). The well-known Scheil-Gulliver theory has some drawbacks, such as the assumption of equilibrium at the solid-liquid interface, which is addressed by this method. The solidified fractions under the Scheil model and the pure equilibrium model are calculated using CALPHAD simulations. Differential scanning calorimeter is used to measure the heat flow during the solid-liquid phase transformation, the result of which is further converted to solidified fractions. The analytical model is compared with all the other models for validation.
In recent years, in addition to the commonly known wire-based processes of Directed Energy Deposition using lasers, a process variant using the electron beam has also developed to industrial market maturity. The process variant offers particular potential for processing highly conductive, reflective or oxidation-prone materials. However, for industrial usage, there is a lack of comprehensive data on performance, limitations and possible applications. The present study bridges the gap using the example of the high-strength aluminum bronze CuAl8Ni6. Multi-stage test welds are used to determine the limitations of the process and to draw conclusions about the suitability of the parameters for additive manufacturing. For this purpose, optimal ranges for energy input, possible welding speeds and the scalability of the process were investigated. Finally, additive test specimens in the form of cylinders and walls are produced, and the hardness profile, microstructure and mechanical properties are investigated. It is found that the material CuAl8Ni6 can be well processed using wire electron beam additive manufacturing. The microstructure is similar to a cast structure, the hardness profile over the height of the specimens is constant, and the tensile strength
and elongation at fracture values achieved the specification of the raw material.
The application of anti-corrosion coated, high-strength steels in the automotive industry has increased in recent years. In combination with various zinc-based surface coatings, liquid metal embrittlement cracking can be observed in some of these materials. A high-quality, crack-free spot-welded joint is essential to realize the lightweight potential of the materials. In this work, the LME susceptibility of different coatings, which will be determined by the crack length and the occurrence rate, will be investigated using a welding under external load setup. The uncoated specimens did not show any LME. EG, GI and GA showed significantly less LME than ZM coatings. The latter coatings showed much larger crack lengths than the EG, GI and GA coatings. Furthermore, two mechanisms regarding the LME occurrence rate were observed: the occurrence of LME in zinc–magnesium coatings was theorized to be driven by the material properties of the coatings, whereas the occurrence of LME at EG, GI and GA samples was forced mainly by the application of the external tensile load.
In the experimental setup of this work, the materials were exposed to unusually high mechanical loads (up to 80% of their yield strength) to evoke LME cracks.
The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision.
To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency.
Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings,walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.
Laser metal deposition (LMD) as an additive manufacturing technique became increasingly important in recent years and thus the demand for component safety. This is the reason, for the need for reliable in-situ defect detection techniques. For laser beam weld seams an optical measurement technique based on an optical flow algorithm was successfully used to define the critical straining conditions that lead to hot cracking. This algorithm was adapted for bead-on-plate weld seams on LMD deposited layers of IN718 alloy while performing external strain on the specimen in an externally loaded hot cacking test facility. The resulting transversal hot cracks along the weld seam were localized via X-Ray inspection and the type of cracking confirmed by Scanning Electron Microscopy (SEM). The strain distribution was measured in the vicinity of the solidification front and correlated to the detected hot cracks. Based on the results this technique could be adopted for LMD experiments.
This article presents a cost-effective and reliable method for welding 30 mm thick sheets of shipbuilding steel EH36. The method proposes to perform butt welding in a two-run technique using hybrid laser arc welding (HLAW) and submerged arc welding (SAW). The HLAW is performed as a partial penetration weld with a penetration depth of approximately 25 mm. The SAWis carried out as a second run on the opposite side. With a SAWpenetration depth of 8 mm, the weld cross-section is closed with the reliable intersection of both passes. The advantages of the proposed welding method are: no need for forming of the HLAW root; the SAW pass can effectively eliminate pores in the HLAWroot; the high stability of the welding process regarding the preparation quality of the weld edges. Plasma cut edges can be welded without lack of fusion defects. The weld quality achieved is confirmed by destructive tests.
During resistance spot welding of zinc-coated advanced high-strength steels (AHSSs) for automotive production, liquid metal embrittlement (LME) cracking may occur in the event of a combination of various unfavorable influences. In this study, the interactions of different welding current levels and weld times on the tendency for LME cracking in third-generation AHSSs were investigated. LME manifested itself as high penetration cracks around the circumference of the spot welds for welding currents closely below the expulsion limit. At the same time, the observed tendency for LME cracking showed no direct correlation with the overall heat input of the investigated welding processes. To identify a reliable indicator of the tendency for LME cracking, the local strain rate at the origin of the observed cracks was analyzed over the course of the welding process via finite element simulation. While the local strain rate showed a good correlation with the process-specific LME cracking tendency, it was difficult to interpret due to its discontinuous course. Therefore, based on the experimental measurement of electrode displacement during welding, electrode indentation velocity was proposed as a descriptive indicator for quantifying cracking tendency.
Numerical simulations are essential in predicting the behavior of systems in many engineering fields and industrial sectors. The development of accurate virtual representations of actual physical products or processes (also known as digital twins) allows huge savings in cost and resources. In fact, digital twins would allow reducing the number of real, physical prototypes, tests, and experiments, thus also increasing the sustainability of production processes and products’ lifetime. Standard numerical methods fail in providing real time simulations, especially for complex processes such as additive manufacturing applications.
This work aims to use a reduced order model for efficient wire arc additive manufacturing simulations, calibrations and real-time process control. Model reduction, e.g. the proper generalized decomposition [1,2] method, is a popular concept to decrease the computational effort. A new mapping approach [3] was applied to simulate a moving heat source with the proper generalized decomposition. Using this procedure even complex models can be simulated in real-time. The physical model is later on calibrated with the use of a stochastic model updating process and the reduced order model, leading to an optimized real-time simulation.
In this contribution, a proper generalized decomposition model for a bead-on-plate wire arc additive manufacturing is presented. It is also coupled with a stochastic model updating process identifying the heat source characteristics as well as the boundary conditions of the transient thermal problem, whereas the heat source shape is simulated using a Goldak heat source
The application of hybrid laser-arc welding (HLAW) for joining closed circumferential welds is a challenge due to the high risk of forming a defective overlap area with a shrinkage void or solidification cracks in the material thickness. A series of HLAW experiments were performed to understand the development of a faulty overlap area when closing the circumferential weld. Welding trials on flat specimens and pipe segments were supported by numerical analyses in which the thermomechanical behavior of the welds in the overlap area was investigated. Different process control strategies were tested, including variations in defocusing levels and the overlap length. The newly developed HLAW head, including laser optics with a motor-driven collimation system, made it possible to defocus the laser beam during welding without disturbing the stability of the welding process. High-level defocusing of the laser beam of more than 40 mm relative to the specimen surface with a resulting beam diameter of > 2.9 mm, and in combination with a short overlap length of 15 mm, was promising with respect to the formation of a desired cup-shaped weld profile that is resistant to solidification cracks.
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
Duplex stainless steels combine the positive properties of its two phases, austenite and ferrite. Due to its good corrosion resistance, high tensile strength and good ductility it has multiple applications. But laser beam welding of duplex steels changes the balanced phase distribution in favor of ferrite. This results in a higher vulnerability to corrosion and a lower ductility. In this study different powder combinations consisting of duplex and nickel for coating layers by laser metal deposition are investigated. Afterwards laser tracks are welded, and the temperature cycles measured. The ferrite content of the tracks are analyzed by feritscope, metallographic analysis and Electron Backscatter Diffraction. The goal is the development of a powder mixture allowing for a duplex microstructure in a two-step process, where firstly the edges of the weld partners are coated with the powder mixture by LMD and secondly those edges are laser beam welded. The powder mixture identified by the pretests is tested in the two-step process and analyzed by metallographic analysis, energy dispersive X-ray spectroscopy and Vickers hardness tests. The resulting weld seams show a balanced duplex microstructure with a homogenous nickel distribution and a hardness of the weld seam similar to the base material.
The talk motivates and introduces the WelDX project and the proposed solutions for current challenges in the field of research data management and Open Science practices in welding research.
Using an exemplary welding dataset based on the joint and welding process design of offshore structures, advanced data fusion and analysis capabilities are demonstrated.
The dataset shown consists of a complex welding sequence covering multiple weld layers with varying process parameters and adaptive weaving motions to cover manufacturing tolerances. In the presentation, an interactive exploration of the dataset contents in the spatial domain is presented. Furthermore, transformation between spatial and time domain of the data is demonstrated.
In addition to data gathered during the welding process, the integration of downstream testing data and results is also explained. For demonstration, integration of weld seam cross section images and Vickers hardness mapping test results into the dataset are explained an demonstrated. The testing data is set into context with the welding process information.
Finally, implications for advancements in research data management for WAAM and AI applications are discussed.