查看更多>>摘要：Metal matrix composites (MMCs) with specially designed structures can be fabricated by laser powder bed fusion (LPBF) time-efficiently and cost-effectively, but reinforcements pose enormous challenges to the technique. In this work, the effect of reinforcement on the formation of MMCs in LPBF is studied by taking diamond grinding wheels (GWs) as an example. Based on the balling phenomenon observed in the LPBF process, the formation characteristics of GWs such as surface morphology, porosity, and flexural strength are investigated. The balling size is increased with an increase in the linear energy density of the laser beam due to more intense diamond spattering. The acting forces applied by the plume and ambient gas flow generated around the melt pool on a diamond grain are analyzed to investigate the generation of diamond spatter. For MMCs with coarse reinforcements, spattering is the dominant cause of the balling phenomenon due to the induced discontinuous melt tracks and uneven powder layers. Furthermore, the performance of a cup-type diamond GW fabricated by LPBF in the electrical discharge grinding (EDG) process of reaction-bond silicon carbide (RB-SiC) is evaluated by the surface and subsurface quality. The grinding process removes the resolidified layer of RB-SiC induced during the electrical discharging process and suppresses crack generation in the subsurface. The results presented in this study reveal the influence of diamond grains on the formation mechanism of GWs, which is also suitable for MMCs with coarse reinforcements.
查看更多>>摘要：Over the past decade, the Laser Powder Bed Fusion (LPBF) process has been widely used in the fabrication of industrial parts with advanced functions. It is known that the complex thermal processing of the material during the LPBF process has a significant influence on product quality. While high fidelity simulation models can account for the effects of processing, they are generally too computationally expensive to be directly used in the design of components. Consequently, in this paper we propose a surrogate model for Simulation Models of the residual stress at the part-scale based on a Convolutional Neural Network (CNN) with a 3D U-Net architecture. In order to model the wide range of geometries that can arise during the design process, we developed a feature-based approach in which we trained our CNN on combinations of three basic types of geometric features: circular struts, square struts, and walls. Data augmentation was utilized to account for orientation invariance. Several benchmarks were designed to test the performance of the surrogate model. Results demonstrated that a CNN with a 3D U-Net architecture can accurately predict the residual stress for the features designed. The average training and testing errors are 5.3% and 6.6%, respectively. Prediction performance for the benchmark parts led to validation errors of 14.4%? 28.3% due to their complex geometries. Nevertheless, this strategy led to a significant reduction in runtime, demonstrating that the proposed feature-based surrogate model has the potential to replace high fidelity process simulations for the design of practical engineering parts manufactured using LPBF.
查看更多>>摘要：Recently, industrial robots have been applied to the machining of non-metallic materials because of the advantages of low cutting force requirements, low cost, and high flexibility, etc. Due to the limited static/dynamic stiffness of robot joints and links, the accuracy of robotic machining, especially the surface topography, is more susceptible to the cutting forces and dynamic vibrations, compared with CNC machining. To provide clear insights into the formation process of surface topography in robotic machining of oriented Plexiglas and choose reasonably the machining parameters, a method to predict the surface topography in the robotic machining of oriented plexiglas is presented, considering fully the tool dynamic vibrations. In this method, the dynamic intersection behavior in the cutting zones of the robotic machining process is first modeled, which is then used to determine the dynamic displacements. The gained vibration displacements are integrated into the sweep surfaces of the cutter cutting edges by changing the theoretical equations of cutting edges, in order to more accurately calculate the machining scallop points in the presence of tool vibrations. After that, between the discrete sweep surfaces and the workpiece, a mapping-based intersecting method is proposed to compute the intersections with the lowest scallop heights, and these intersections are used to construct the 3D graph, which can represent the topography of the resulting machined surface. Finally, the computer simulation and machining experiments are conducted, and it is shown that the predicted and physically measured surface topography have a good agreement.
查看更多>>摘要：Laser Powder Bed Fusion (LPBF) is a frequently used Additive Manufacturing (AM) technique for producing metal parts with complex shapes. One of the crucial factors in obtaining an optimal part is understanding the behaviour of the melt pool, a pool of liquid metal molten by a laser. Its size and shape are the result of a wide range of (processing) parameters, making it labour intensive and costly to identify an optimal processing range. To accelerate the identification of suitable processing conditions, this work presents a novel methodology that combines (semi-) analytical expressions of the melt pool width w and the aspect ratio R=d/w to compute the depth d over a large processing range. This range includes both conduction (R<1) and keyhole (R>1) mode melting regimes. The novelty of the presented approach is the prediction of absolute melt pool dimensions for both the conduction and keyhole regime. Furthermore, this methodology is extensively validated for three common LPBF materials: Titanium Ti-6Al-4V, Stainless Steel 316L and Inconel 718. The average errors obtained between model and experimental data of the melt pool width & depth in a relevant processing range (0,8<R<3) are 10,92% & 11,37%, 12,58% & 10,99% and 15,27% & 12,81% for the three materials respectively. With these results, this work shows that the proposed methodology performs well in predicting melt pool dimensions for a large processing range, both in terms of speed and accuracy.
查看更多>>摘要：Viscous pressure forming has shown its advantages in precision forming of complex surface components. However, there are very few reports focusing on revealing the mechanism of springback control by viscous medium. This paper aims to study the effect of viscous medium on springback and the mechanism of springback control by viscous medium. The viscous pressure forming of a bulging axisymmetric geometry is studied. The effect of viscous medium on springback and stress path of sheet is analyzed in numerical simulation by unloading the sheet and viscous medium together. Then, the experiments are conducted using different kinds of viscous medium and loading speeds. The digital correlation image (DIC) technology is employed to measure the springback. The stress and membrane force of the bulging sheet are calculated and analyzed. Results show that non-uniformly distributed force of viscous medium changes the shape and stress path of the sheet. The viscous medium simultaneously possessing good strain rate sensitivity and viscosity can well control the springback. This is because the coupling effect of non-uniform normal force and tangential adhesion promotes the uniformity of stress distribution on the whole sheet and reduces the bending moment of sheet near the die corner.
查看更多>>摘要：A multi-energy source (MES) method featuring a high-power scanning laser (SL) was used to achieve independent control of layer width and height in a wire-based directed energy deposition (w-DED) process. In the MES system, a plasma transferred arc (PTA) was employed to create an initial melt pool and melt the wire, and a SL was used to reshape the melt pool and precisely control the bead width. The distance between the SL and the PTA and different laser scanning strategies were investigated. Images of the melt pool with varying scanning widths were captured. A bead shape control strategy was demonstrated by using the wire feed speed to control layer height and the laser scanning width to control the layer width independent of each other. The advancing speed was adjusted in proportion to the scanning width to keep the same specific process energy of the SL. The experimental results demonstrated that the MES approach provides independent control of layer width and height. Some single-pass walls were built using the MES to show that MES can be used for w-DED additive manufacturing.
查看更多>>摘要：In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. These results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.
查看更多>>摘要：Coupling of multiple abrasive grains is crucial for the efficiency in the grinding process and grinder design. Here the coupling effect in a double-grain model in vibration-assisted scratch of single-crystal silicon carbide (SiC) have been investigated using the molecular dynamics simulations for both simultaneous and sequential scratch processes. The coupling between the double abrasive grains affect the scratch force, stress, amorphous layer and surface morphology. The reduction ratios of tangential and normal force and the influenced material volume show that the critical distance for the inhibition of the coupling of vibration-assisted scratch is significantly greater than that in conventional scratch. The change of overlap ratio can reflect the change trend of the scratch force reduction ratio. In the vibration-assisted grinding, the increase of overlap ratio also intensifies the coupling of the abrasive grains, resulting in faster material removal, smaller scratch force and better surface finish. Insights obtained through the molecular dynamics analysis in this work into the coupling effects of abrasive grains in the vibration-assisted grinding process is believed to be beneficial in the development of grinding wheels and the optimization of machining processes.
查看更多>>摘要：High-strength metal channels with local features are widely used in the automobile industry. In this study, the chain-die forming of long channels with local features is investigated. Two key problems in forming local features under the unique roll-stamping mode, position lag and interference between the workpiece and die blocks, are analyzed by kinematic modeling of die blocks at the forming and release stages. Considering the progressive rotational movement of discrete die blocks, design criteria for the die blocks and local features are proposed. Finally, a flat blank and U-channel with local features are designed as two benchmark cases to validate the proposed design criteria by simulation and experiment. The results show that the forming problems are solved, and the forming precision of the local features can be significantly improved. This study provides effective process design guidance for the chain-die forming of long channels with local features.
查看更多>>摘要：The controllable wire-fed laser cladding (WLC) on inclined surfaces is one of the most challenging issues to repair free-form surfaces of high-valued parts because of the gravity effect, and therefore has attracted substantial attentions. Numerical methods need large computational efforts and can only achieve indirect inter-relationship between processing parameters and cladding geometries. In this concern, analytical models can be a promising solution but the models of overlapped profiles on non-horizontal surfaces are still absent from the literature. To fill this gap, this paper investigates the overlapped wire-fed laser cladding on inclined surfaces by proposing an analytical model with the considerations of substantial physical phenomena (gravity, interfacial tension) and thermal behaviors (Marangoni flow, wetting). The model is then validated by experimental trials, followed by a real cladding application on non-horizontal surfaces to show the model ability. The findings in this paper are expected to be meaningful and helpful to not only academic research but also engineering applications.
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Elsevier