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Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibility and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in the form of a beadon-plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is developed, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control, and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the temperature.
The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.
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.
High-strength steels offer potential for weight optimization due to reduced wall thicknesses in modern constructions. Additive manufacturing processes such as Wire Arc Additive Manufacturing (WAAM) enable the resource-efficient production of structures. In the case of defects occurring in weld seams orWAAM components due to unstable process conditions, the economical solution is local gouging or machining and repair welding. It is important to understand the effects of machining steps on the multiaxial stress state in conjunction with the design-related shrinkage restraints. Research into how welding and slot milling of welds andWAAM structures affects residual stresses is still lacking. For this reason, component-related investigations with high-strength steels with yield strengths ≥790 MPa are carried out in our research. In-situ digital image correlation (DIC) and ex-situ X-ray diffraction (XRD) were used to analyze the stresses and strains induced on specimens during and after milling. The systematic analyses revealed a significant interaction of the stiffness and microstructure of the specimens with the initial residual stresses induced by welding. Subsequent repair welds can result in significantly higher residual stresses.
Additive manufacturing (AM) processes enable the efficient production of advanced constructions. New developments in topology optimization are leading to weight-optimized designs of increasing complexity. Direct energy deposition processes (DED) such as wire and arc-based additive manufacturing are an important method of additive manufacturing. The wire filler metals enable a wide range of materials, while the arc process provides a high deposition rate compared to laser and powder-based processes. Combined with the use of high-strength steels, the thickness of walls or components can be significantly reduced in the context of lightweight construction, which results in significant savings in energy, costs, time and resources. Suitable high-strength steel filler metals are commercially available for DED-arc AM processes. However, guidelines and quantitative knowledge about welding stresses and cold cracking issues during component production and service are lacking. This limits the industrial application considerably. In a joint project of BAM and Chemnitz University of Technology, the main influences and complex interactions of material, production process, design and processing steps on the residual stress level are investigated. The aim is to develop processing recommendations and a cold cracking test for economical processing and stress-related design of high-strength steels with DED-arc. This study focuses on residual stress analysis by neutron diffraction (ND) and X-ray diffraction (XRD) on defined test specimens. The ND analysis were performed at the Paul Scherrer Institute- Villigen, Switzerland (PSI) and the XRD analysis at BAM. The study shows a quantitative and qualitative comparison of the residual stress magnitudes and distribution between the component bulk (ND) and surface (XRD) analyses. The ND analysis reveals that in DED-arc AM walls the residual stresses dominate in the direction of welding and are negligibly small in each case transverse to the direction of welding. The topology of the analyzed residual stresses shows almost identical residual stress maps compared to XRD. In addition, the residual stresses are significantly influenced by the solid phase transformation of the material due to low cooling times and less post heat treatment cycles of following AM layers in the area of the top layer.
Residual stresses Analysis in Additively Manufactured alloys using neutron diffraction (L-PBF)
(2023)
An overview of recent progress at BAM of residual stress analysis in additively manufactured, in particular Laser Powder Bed Fusion of metallics materials, using neutron diffraction will be presented. This will cover important topics of the stress-free reference, the diffraction elastic moduli and principal stress determination.
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.
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.
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.
Additive manufacturing (AM) has revolutionized the manufacturing industry, offering a new paradigm to produce complex geometries and parts with customized properties. Among the different AM techniques, the wire arc additive manufacturing (WAAM) process has gained significant attention due to its high deposition rate and low equipment cost. However, the process is characterized by a complex thermal history, dynamic metallurgy, and mechanical behaviour that make it challenging to simulate it in real-time for online process control and optimization.
In this context, a reduced order model (ROM) using the proper generalized decomposition (PGD) method is proposed as a powerful tool to overcome the limitations of conventional numerical methods and enable the real-time simulation of the temperature field of WAAM processes. Though, the simulation of a moving heat source leads to a hardly separable parametric problem, which is handled by applying a novel mapping approach. Using this procedure, it is possible to create a simple separated representation of the model, also allowing to simulate multiple layers.
In this contribution, a PGD model is derived for the WAAM procedure simulating the temperature field. A good agreement with a standard finite element method is shown. The reduced model is further used in a stochastic model parameter estimation using Bayesian inference, speeding up calibrations and ultimately leading to a calibrated real-time simulation.