Papers by Keyword: Sheet Metal Forming

Abstract: Electroplasticity in sheet metal forming is a relatively recent method that involves applying an electric current to metal sheets during or before the forming process. Existing research on Electro-Assisted (EA) forming primarily focused on material characterization; few studies have investigated the effect of electropulsing on loads, power, and energy consumption during sheet metal forming, and no studies have explored the reshaping of previously formed titanium sheets after the Electro-pulsed treatment (EPT). This research aims to bridge some of these gaps of knowledge by applying two different electropulsing treatments, varying in current density, to square Ti6Al4V specimens prior to shaping and reshaping. performed using dies and counter dies having different geometries. Load, power, and energy consumption data were measured to assess the benefits of EPT compared to an untreated specimen serving as a reference. The findings suggest that EPT can significantly reduce the energy consumption and forces required for both shaping and reshaping of titanium components, extending their useful life and reducing the need for remelting. The study highlights the potential of EPT as a sustainable solution for reducing the environmental impact of titanium sheet disposal and recycling, improving material efficiency, and optimizing industrial forming processes.

Abstract: Draw-in distance is a key index for evaluating the quality of sheet metal stamping. Its accurate prediction is therefore required for tool design and process control. Traditional finite element (FE) simulations, while accurate, are computationally intensive and time-consuming for iterative design optimization. In this study, a graph neural network (GNN) method is proposed to predict draw-in during sheet metal forming. A dataset was built from FE simulations with different process settings, including blank holder force and draw bead force. The GNN model uses node coordinates and edge features to describe the spatial relations in the sheet. A multi-level loss function was applied. The coordinate error and edge distance error were included. In this way, the shape of the sheet is better preserved. The trained GNN can be used as a fast model for draw-in prediction. It can also be used for inverse analysis, where the process parameters are found from a given draw-in result. This provides an efficient tool for sheet metal forming design and optimization.

Abstract: Hydrogen-based energy systems are considered a key pillar of the energy transition, yet the cost-efficient, mass production of metallic bipolar plates (BPPs) for proton exchange membrane fuel cells (PEMFCs) remains challenging, as conventional processes are limited by comparatively long cycle times and forming-related instabilities. This paper investigates the rubber drawing process as a cost-efficient manufacturing method for metallic bipolar plates, proposed as an alternative to the commonly applied hydroforming process, analysing the influence of pressing force, rubber hardness and thickness, tool modifications for varying pressure distribution, and the suitability of additively manufactured tool dies made from Maraging Steel 1 (X3NiCoMoTi 18-9-5) or ceramic-filled UV resin. The results show that precise and stable tool guidance, as well as a well-adapted tool setup, are required to achieve reproducible component quality; targeted adjustments of process and rubber parameters improved channel dimensional accuracy, but revealed limited forming capability in certain areas. Furthermore, concavely and convexly modified rubber dies reduced component warping in specific directions, and steel dies exhibited higher precision and less distortion compared to ceramic-filled UV resin dies. These findings highlight the potential of the rubber drawing process for cost-effective production of bipolar plates, while identifying key parameters for further optimization toward industrial-scale manufacturing.

Abstract: Hybrid hot sheet forming routes that integrate heat treatment within the forming tool offer a promising pathway to manufacture complex geometries from precipitation‑hardenable 7xxx aluminum alloys, but the resulting local deformation and thermal histories may generate pronounced spatial property variations. In this work, a gas-based hybrid forming process is demonstrated for EN AW‑7020 sheets, combining in‑tool solution heat treatment, isothermal forming at 500 °C with gas calibration and active pushing, followed by water quenching and artificial aging. A thermo‑mechanically coupled finite‑element model is used to identify regions of distinct equivalent plastic strain in a representative demonstrator geometry and to guide local specimen extraction. Tensile tests from low‑ and high‑strain regions reveal clear location-dependent stress‑strain responses after aging, with a reduction in ultimate tensile strength exceeding 20 % in the more heavily deformed zones compared with reference material. Microstructural observations by optical microscopy indicate differences in grain morphology between component regions, consistent with the non‑uniform thermo‑mechanical history. The results highlight the need to account for local strain and process history when designing hybrid‑formed 7xxx components and motivate targeted strategies for controlling property gradients through process parameter tuning and tailored post-forming heat treatment.

Abstract: This study investigates the structural response of blank-holders (BHs) equipped with spatially distributed magnetorheological (MR) actuators for adaptive deep drawing. While MR actuators provide fast, independent, and high-resolution force modulation, their effectiveness depends critically on the BH’s ability to transmit spatially differentiated loads without excessive diffusion or unrealistic stress localization. The relationships between BH stiffness, actuator spacing, and pressure localization at the sheet interface remain only partially understood, limiting the implementation of distributed blank-holding strategies. To address this gap, a comprehensive finite element (FE) framework is developed, combining a full closed-cup deep-drawing model with a complementary simplified configuration that isolates local deformation mechanisms under single-actuator loading. Parametric analyses examine the influence of BH thickness, local actuator force, and actuator spacing on stress distribution, localization radius, and overlap between adjacent load paths. Results show that BH thickness is the dominant factor governing spatial resolution: thinner BHs enable sharp pressure localization, whereas thicker ones diffuse local loads and suppress stress peaks. The spacing between actuators must therefore be selected as a function of BH stiffness to avoid stress-free regions while preserving distinct pressure footprints. For the reference industrial configuration (60 mm BH thickness), an actuator spacing of approximately 150 mm achieves the optimal compromise between localization capability and continuous sheet support. The proposed framework establishes quantitative design criteria for BH geometries compatible with MR-based adaptive forming and supports the development of next-generation blank-holding systems offering enhanced process stability, reduced scrap, and improved material-flow control.

Abstract: Machine learning (ML) algorithms have been studied in literature as an inverse method to predict material constitutive parameters. However, these approaches are often dependent on the mesh discretisation settings applied during numerical simulations, and then difficulty model adaptation to experimental digital image correlation (DIC) subsets. Although a recent study explores the use of an interpolation-based approach to achieve experimental adaptation from numerically-based trained ML models, the proposed methodology lacks evaluation using experimental data. As a follow-up, this study proposes a new evaluation approach. Numerical data is DIC-levelled via MatchID software and then submitted to interpolation. An XGBoost algorithm is then trained on interpolated DIC data and evaluated for parameter prediction, comparing the obtained results with those obtained from the model trained on interpolated numerical data. Overall, the proposed DIC-levelling and interpolation pipeline yields an excellent predictive performance, with results comparable to those obtained when training on interpolated numerical data. The largest deviations are observed for the hardening exponent, while the remaining parameters are predicted with consistently high accuracy. These findings validate the practical applicability of the interpolation-based strategy to reduce the subset scheme dependency of ML models trained on real experimental data.

Abstract: Sheet metal components with complex geometries are typically recycled by remelting. Direct remanufacturing necessitates the flattening of parts, which requires the implementation of cuts to facilitate unwinding. The exact positioning of these cuts is a complex planning task, because several influencing factors can be considered, such as material usage, ease of flattening, or minimal forming required. This study presents a geometry-based concept addressing this challenge and demonstrates its use for a test geometry. The finite element method is applied to simulate the flattening process of the resulting sections, and the results are evaluated in terms of planarity and induced plastic strain. The findings of the present work indicate a discernible dependency of results on the selection of the flattening directions. In particular, curved areas impact the induced plastic deformation and springback of flattened sections. This is a crucial consideration when planarity is prioritised over material utilisation.

Abstract: Predicting the final shape of automotive structural components after springback is a challenge to the inclusion of high strength aluminum alloys into the vehicle body-in-white. Complex deformation paths and reverse loading of sheet material during forming operations can induce significant Bauschinger effects and kinematic hardening behaviour. Capturing the through-thickness stress gradient is critical when predicting springback, which is governed by tooling dynamics, frictional forces, and material plasticity. In this study, the anisotropic behaviour of a AA6xxx-T4 aluminum alloy was characterized to calibrate a BBC2005 yield function, kinematic hardening effects were characterized through a novel uniaxial compression-tension technique, and a technology demonstrator U-shaped rail component was formed and scanned to assess the final shape after springback. Multiple model variations were analyzed in AutoForm R12, modifying simulation control parameters, binder loading condition (uniform vs. column), friction model (Coulomb vs. TriboForm), and hardening model (isotropic vs. kinematic). The use of column binder loading paired with TriboForm friction model provided the most significant improvement for thinning and springback prediction accuracy with kinematic hardening being a second order effect compared to accounting for friction and binder force.

Abstract: The present work investigates the global and local formability of two third-generation Advanced High Strength Steels (AHSSs), a quenching & partitioning (Q&P) steel and a Medium Mn (MMn) steel with 1GPa strength. Third-generation Q&P and MMn steels are designed to overcome the limitations of first-generation AHSS grades by enhancing formability while maintaining high mechanical strength, thus enabling more efficient structural design and improved crash performance. Understanding their forming behaviour is essential to ensure their reliable use in complex sheet metal forming operations. In this study, the forming performance of a Q&P and a MMn steel is analysed through experimental procedures involving both in-plane deformation under various loading paths and hole expansion tests with different hole edge qualities, to evaluate their global and local formability. A first-generation Dual Phase (DP) steel is included in the analysis for comparison. The results demonstrate that 3^rd Generation Q&P and MMn steels exhibit very good global formability, superior to conventional 1^st Generation AHSSs. However, local formability, as evaluated by hole expansion capacity, can be severely compromised by edge manufacturing process. These findings contribute to a deeper understanding of the distinction between global and local formability in third-generation AHSS, offering insights to improve process robustness and support the industrial implementation of these steels in high-performance automotive components.

Abstract: Friction plays an important role on formability of deep drawn products. This necessitates an accurate description of friction in finite element formability analyses. It has been shown that constant coefficient of friction does not lead to precise prediction of product formability in these analyses. The multi-scale friction model developed at University of Twente takes the local contact conditions and textures of sheet metal and tools as the input at boundary and mixed lubrication regimes. To correlate the zinc coated sheet metal surface texture parameters with its formability, 60 different textures were analyzed. The multi-scale friction model is used to estimate friction for all the sheet metal surface textures. The effect of different textures on formability of the sheet metal was investigated by simulating cross-die forming using different sheet metal surface textures. The results show that different textures depict distinct formability behavior in the boundary lubrication regime (lubricant amount 0.1 gr/m²). Exploring the correlation between areal field parameters and formability of cross-die for the current dataset shows that besides surface roughness, autocorrelation length and skewness of height distributions are determining parameters.