International Journal of Material Forming最新文献

Self-piercing riveting (SPR) technology has been widely used in the automotive industry due to its high degree of automation, excellent joint sealing and fatigue resistance. In this paper, the forming process of SPR and the influence of process parameters on joint quality were investigated in detail through numerical simulation. To accurately capture the deformation and failure behavior of the sheet metal during SPR process, especially under relatively low stress triaxiality, a shear modified GTN damage model proposed by Nahshon and Hutchinson was adopted, and the corresponding numerical implementation method was developed. The damage related parameters were calibrated using a combination of the response surface method and the genetic algorithm. Subsequently, a three-dimensional explicit finite element model for SPR was established, and its validity was verified by comparing the cross-sectional dimensions of the joint with experiment results. The riveting process and damage evolution of the sheet metal were fully analyzed. Furthermore, the riveting process under different sheet stacking sequences, die types and depths was discussed. Specifically, when a thicker sheet is used as the lower sheet, it is conducive to achieving a larger under-cut and residual bottom thickness, with under-cut increasing by 9.5% and residual bottom thickness by 34.3% compared to the reverse configuration. Compared with a convex-bottom die, the use of a flat-bottom die significantly increases the under-cut of the joint by approximately 51.6%, while the residual bottom thickness and damage degree of the upper sheet are reduced by 34.1% and 11.2% respectively. When the die depth is increased from 1.8 mm to 2.2 mm, the high-stress area of the rivet leg decreases, whereas the damage degree of the upper sheet increases by 7.9%, accompanied by a 29.3% increase in residual bottom thickness and an 8.6% decrease in under-cut.

During dynamic roll forming process, downward pressure parameters nonlinearly affect the final radius of curvature, making accurate prediction a significant challenge in precision molding. To address this issue, this paper proposes a hybrid neural network model, ARIMA-TCM-XLSTM-Informer, designed to efficiently model the complex temporal dependencies in the roll forming process. First, the ARIMA module captures the short-term linear trends in the material deformation sequence by focusing on the elastic response and transient stress changes occurring during the early stages of forming. Next, the TCM module identifies the dynamic evolution of local buckling and residual stress by using a multi-scale convolutional structure to extract local nonlinear features and short-term dependencies, thus enhancing the model’s sensitivity to local changes. Furthermore, the XLSTM module improves the ability to characterize long-term dependencies by modeling the roll-over process using an enhanced long-and-short-term memory structure. This enables accurate representation of the stress-strain history and cumulative deformation features during roll-over. Finally, the Informer module optimizes long-sequence modeling by employing a sparse self-attention mechanism to capture remote correlations and trend changes in the forming sequence, which improves the model’s predictive capability for critical forming steps. The experimental results show that the ARIMA-TCM-XLSTM-Informer model can effectively capture the linear and nonlinear dependencies of the molding sequence data. The comparison with other advanced models on the physical experiment dataset demonstrates the proposed model’s accurate prediction ability of the curvature radius.

The in-plane deformation kinematics of a pure-Unidirectional Non-Crimp Fabric (pure-UDNCF) is investigated using novel and existing experimental methods to characterise its shear, tensile, and in-plane bending responses under controlled loading. A pure-UDNCF is defined as a fabric in which stabilising tows are absent in the transverse direction relative to the primary tow orientation. The stitching threads in this fabric are made of polyamide, a highly compliant material that allows significant stretch, introducing a low-energy deformation mode that is relatively absent in biaxial engineering fabrics and quasi-UDNCFs (UDNCFs with inextensible stitching and transverse stabilising fibres). To fully characterise its forming behaviour, several novel testing methods are introduced that generate well-defined combinations of fabric shear, in-plane bending and stitch tensile strain. The total normalised axial force measured in the tests is subsequently decoupled into three contributions from shear, tensile strain in the stitch direction, and in-plane bending of the tows. An important finding is that when tested in the picture frame test, in-plane bending generates more resistance to specimen deformation than shearing of the fabric. The testing protocol and resulting data can be used to create appropriate constitutive models for pure-UDNCFs.

Lightweight aluminum alloys, such as AA7075, are desirable for applications in various industries, but their limited formability and workability often pose significant challenges. Constrained Friction Processing (CFP) has emerged as a promising technique to address these challenges by refining microstructures through the relative motion between two tools and the workpiece. The process involves axial extrusion, dual tool rotation, and constraint of the extrudate to control material flow and enhance microstructural refinement. CFP is particularly attractive for high-strength AA7075 as it imposes constrained material flow and increased shear deformation, overcoming the limited formability observed in existing conventional extrusion processes. This study investigates CFP using both experimental and numerical methods, establishing correlations between process conditions, material flow, microstructural evolution, and hardness for the aluminum alloy AA7075. The process was investigated at rotational speeds of 1000–1400 rpm, resulting in the highest measured peak temperature up to 445°C and refined grain sizes of 2–3 µm. The results show that initial shear deformation and material flow under the rotating tools are redistributed during processing, leading to helical flow behavior in the extruded rod. The refined stir zone (SZ) exhibited increase in hardness, about 25% compared to the base material. The developed finite-element model demonstrates good agreement with experimental results with respect to the complete thermal cycle, spatial temperature distribution, and material flow patterns, providing insight into the microstructural evolution during CFP of aluminium alloys.

The ratio of ferrite to austenite in duplex stainless steel significantly affects its mechanical properties. In order to regulate the proportion of ferrite and austenite in duplex stainless steel produced by arc additive manufacturing and improve the mechanical properties of duplex stainless steel, the deposited samples of TIG arc additive manufacturing were treated by solution treatment. The effects of solid solution temperatures of 1200 ℃, 1250 ℃, 1300 ℃, and 1350 ℃ on the microstructure and properties of deposited samples were studied and compared with untreated samples. The results showed that as the solution temperature increased from 1200 °C to 1350 °C, the proportion of α-ferrite in the sample and the hardness of the sample increased, and the elongation of the sample increased first and then decreased. At a solutionizing temperature of 1250 ℃, the two-phase ratio in the deposited sample was close to 1 : 1, and the plasticity, wear resistance and corrosion resistance of the deposited sample were the best. the elongation in the horizontal direction reached 36.9%. The corrosion current density, corrosion hole area and number of the sample were 1.44 µA/cm², 8 µm² and 32, respectively. The friction coefficient of the sample was 0.53, and the wear mass and wear volume were 0.7 mg and 2.12 mm³, respectively. By comparing the XRD results of the surface before and after wear, the oxides produced in the wear scar were mainly composed of Fe₂O₃, Mn₃O₄ and Cr₂O₃. The dry friction and wear mechanism of duplex stainless steel fabricated by arc additive manufacturing at room temperature involved adhesive wear, abrasive wear and oxidation.

In the injection molding process, cooling efficiency and uniformity significantly influence product quality and cycle time. This paper presents a multi-objective optimization study aiming to minimize both molding time and maximum warpage through process parameter tuning, while comparing the performance of traditional and conformal cooling systems. Using Latin Hypercube Sampling, 15 process parameter sets were simulated in Moldflow. A radial basis function neural network was trained to model the relationship between process parameters and quality objectives, followed by particle swarm optimization to obtain Pareto fronts for both cooling systems. Experimental validation confirmed simulation accuracy with errors below 5%. The results show that through optimized process parameters, conformal cooling achieved a trade-off between warpage and molding time in the ranges of approximately 0.25 ~ 0.51 mm and 12.9 ~ 15.8 s, respectively. Compared to traditional cooling, the conformal system reduced maximum warpage by about 25% and molding time by 30% on average, thereby shortening the molding cycle and improving efficiency while enhancing product quality.

Complex sheet metal parts are manufactured through multi-stage plastic deformation processes. The geometric configuration design, feature recognition, and segmentation of these forming stages constitute a core challenge in intelligent die design. This demands systematic planning integrating material behavior, process constraints, and tool structure, thus necessitating precise geometric understanding of the part. To address this, we propose a spectral-graph-theory-based approach for analyzing forming-feature attributes. A topological model of geometric features is established to support optimal forming-stage geometry design. Beginning with the 3D surface model, elementary geometric features are extracted, and their mean curvatures are adopted as primary graph signals. A weighted Laplacian matrix is constructed using Gaussian kernel weights, with its eigen decomposition yielding the spectral graph representation of the part. Spectral clustering then partitions this graph into distinct subgraphs, each corresponding to an individual geometric forming feature. This facilitates the establishment of a topological structure graph for forming features, enabling robust feature recognition. Validation via recognition and design case studies demonstrate that the method efficiently translates geometric features into frequency-domain signals through mathematical modeling. This achieves effective feature extraction and classification, confirming its significant potential for advancing process design and manufacturing in sheet-metal forming.

This study reveals the fundamental microstructural and textural transformations that occur in high-purity copper after a single pass of Equal Channel Angular Pressing (ECAP). The main issue addressed is the lack of understanding regarding the onset of grain refinement and texture development during the early stage of severe plastic deformation (SPD) in face-centered cubic (FCC) metals. Contrary to the common focus on multi-pass processing, this work demonstrates that even a single ECAP pass induces significant microstructural reorganization. Transmission Electron Microscopy (TEM) and Electron Backscatter Diffraction (EBSD) analyses show the formation of dense dislocation networks, subgrains, and a notable increase in high-angle grain boundaries (HAGBs), indicating the transition from a coarse-grained to an ultrafine-grained (UFG) structure. X-ray diffraction pole figure analysis reveals a pronounced shear texture characterized by {111}<110 > and {112}<110 > components. These findings highlight that substantial structural and crystallographic changes can be initiated through minimal deformation. The innovation of this work lies in its demonstration that meaningful grain refinement and texture evolution begin at the very first stage of ECAP, offering valuable insights for designing SPD strategies to tailor material properties. This contribution forms a critical basis for optimizing ECAP-based processing routes in copper and similar FCC metals, even at the initial deformation step.

Low-alloy steel drill bits are commonly used in the mining industry for boring holes into rocks. They are characterized by high hardenability and a good combination of mechanical properties, with minimal additions of alloying elements. As product quality improves, the market becomes more competitive. Significant growth in the use of engineering development tools has also occurred, leading to improved performance in rock drilling, oil and mineral exploration, and the search for gemstones. Due to the constant cyclical mechanical stresses applied, the expected life of drill bits is often reduced by fatigue or mechanical wear, indicating that the market consistently requires the availability of new drill bits. This work aimed to demonstrate the suitability of hot forging as a substitute for machining to manufacture the specific profile of the drill bit preform used in rock drilling. A mining-industry partner selected AISI 8640 steel, as it is widely used for drilling applications. Tests were performed with AISI 8640 as a modeling material, and the results from the forging process were used to evaluate the punch movement and consequent metal flow, as well as the filling of the part’s cavity within the forging dies. The results obtained using Elementary Plasticity Theory (EPT) allowed verification of the physical experiment with AISI 8640 steel forged parts. Following this evaluation, numerical modeling was performed and compared with the hot forging process. Formation of the hollow cavity(sigma_b) within the part and the head of the preform in the same step was assessed. As a result of this optimized process, the reduction in the initial height of the billet and the preform formation were analyzed, with the finishing of the drill bit contingent upon the machining process. This hot forging demonstrated improved material utilization compared to the machining process, highlighting the benefits of deformation through simulations.