Papers by Keyword: Metal Forming

Abstract: Climate change is progressing rapidly, posing severe risks to the environment and making sustainability and circularity key challenges for future industrial development. Greenhouse gas emissions are largely driven by human activities within industrialized societies, requiring adaptation at individual, societal, and industrial levels. Metal forming technologies can contribute significantly to this transformation by improving material efficiency, process efficiency, product efficiency, and circularity. Material efficiency is particularly important, as material production accounts for the largest share of industrial emissions. Process efficiency offers a high leverage effect, due to the large production volumes in forming process chains, while product efficiency reduces energy consumption during usage and enhances the performance of energy generation systems. Circularity supports sustainability by extending material lifecycles through reuse and recycling, thereby avoiding energy-intensive primary production. This paper presents an overview of exemplary sustainability contributions in metal forming process chains. For open-die forging, it can, for example, be shown, that digital twins, virtual reality–based operator training and real-time assistance systems are measures to improve material and process efficiency. A circularity approach for open-die forging is presented, with a remanufacturing concept for large shafts based on re-forging end-of-life components, in order to heal fatigue-related damage by forming. Increased material, process and product efficiency is demonstrated by a use case study of forging hollow rotor shafts for wind turbines. Whereas, the hollow-forging allows for weight reduction in the rotor component and thus enables higher power density of the generator, thinner tower designs and reduced logistic costs. Additionally, the use of an innovative air-hardening ductile (AHD) steel can eliminate the energy-intensive heat treatment in the process chain.

Abstract: The Guided Material Flow (GMF) process is an advanced variant of the Samanta process designed for the net shape cold extrusion of gears. The GMF process employs a modified die geometry to control material flow and significantly reduce maximum tool loads, effectively overcoming traditional process limitations. Key advantages include enhanced tooth tip strength and a reduction in face end deformations, which are characteristic defects in the conventional Samanta process. Minimising these deformations reduces the requirement for subsequent machining and enhances overall material efficiency. A numerical dataset was generated to train and validate data driven surrogate models, facilitating rapid process analysis without the computational cost of continuous Finite Element Analysis (FEA). The models developed in this paper enable the precise prediction of critical process outputs, including maximum punch force, die filling behaviour, material utilisation and strain hardening at the tooth tip. This paper details the numerical data acquisition, the specific training and validation methodologies of the machine learning models and demonstrates their capability to accurately predict complex process outcomes when varying the geometry of the die active surface in the GMF process.

Abstract: We investigate the predictive performance of specific analytical and numerical methods to determine the effective magnetic properties of two-phase steels at the macroscale. We utilize various mixture rules reported in the literature for the former, some of which correspond to rigorous bounds, e.g., Voigt (arithmetic) and Reuss (harmonic) averages. For the latter, we employ asymptotic homogenization together with the finite element method (FEM) and periodic boundary conditions (PBC). The voxel-based discretization of the representative volume element is conducted with digital image processing on the existing micrographs of DP600-grade steel. We show that unlike the considered isotropic mixture rules, which use only the phase volume fraction as the statistical microstructural descriptor, finite element method-based first-order asymptotic homogenization allows prediction of both phase content and directional dependence in the magnetic permeability by permitting an accurate consideration of the underlying phase geometry.

Abstract: There is a large body of literature on both the techniques for bulge testing and experimental results for various metallic materials (see the book of Banabic [1]). Generally, the experimental data for isotropic materials are interpreted using the von Mises yield criterion [2]. In this paper, we investigate the role played by the third invariant of the stress deviator, on the response under bulging of isotropic materials that have the same mechanical response in tension and compression. To this end, we use the yield criterion developed by Cazacu [3] and our implementation of this model in the F.E. code Abaqus [4]. For isotropic materials, this yield criterion involves a unique parameter, denoted ; in the case when it reduces to the von Mises yield criterion while for it involves dependence on . The results of F.E. simulations of bulge tests for isotropic materials characterized by various values of the parameterput into evidence new aspects concerning the stress states experienced by the respective materials under bulging.

Abstract: The susceptibility of advanced high-strength steels (AHSS) to hydrogen embrittlement (HE) limits the broad utilization of these materials for body-in-white (BIW) components. The considerable decrease of both ductility and toughness due to local hydrogen accumulation inside of formed components may cause unpredictable time-delayed failure. In particular deep-drawn and punched AHSS components are prone to hydrogen absorption. This work investigates the influence of plastic deformation on hydrogen absorption of dual phase (DP) steels. For that purpose, tensile samples were machined out of three commercial 1.2 mm-thick DP sheets with ultimate tensile strengths of 626 MPa, 826 MPa and 1096 MPa. Samples were uniaxially pre-strained to 2 %, 5 %, 10 %, 15 % and 20 %. After pre-straining the samples were electrochemically charged with hydrogen, and the actual hydrogen contents were determined using a thermal desorption analyser (TDA). Before and after charging, the hardness of the samples was measured and the uniaxial quasi-static tensile properties were determined. In order to quantify the influence of plastic deformation on HE, slow strain rate tests (SSRT) were performed. The results of the tests were correlated with the fraction of martensite determined for each of the three steels.

Abstract: In order to obtain flow curves from compression test results of a cold forging material and predict flow curves of the material at intermediate temperature and strain rate values, a model was developed using Python programming language in this study. The model consists of two parts: Flow curve determination and flow curve prediction. The compression test data including Force-Stroke values was processed to determine the flow curves in the first part, and the flow curve data constructed for certain temperature and strain rate values of the material was used as input for the machine learning algorithms to predict flow curve at desired intermediate temperature and strain rate values in the second part. Moreover, Ludwik material model parameters were estimated by using curve fitting methods in order to define the material model into the simulation software. Machine learning algorithms and various regression models in Python libraries were tested to predict the flow curves. The performances of different machine learning and regression models were compared with respect to the mean squared error and coefficient of determination performance measures. Support vector regression, k-Nearest Neighbour (kNN) and artificial neural network models were used to predict flow curves of cold forging materials and kNN regression model was able to found predictions with the lowest error rate. As a result, a model that can process the compression test data to predict flow curves at intermediate temperature or strain rate values was developed.

Abstract: Viscoplastic self-consistent (VPSC) modeling was used for investigating the deformation behavior of commercial EN AW-7075-T651 aluminum alloy at room temperature under quasi-static tension and compression (i) parallel, (ii) diagonal and (iii) transverse to the rolling direction. Textures of the as-received plate and of the samples after tensile and compression testing were determined using electron backscatter diffraction (EBSD). Euler angles and area fractions of the grains were used as input for calculating direction-dependent flow curves and pole figures of the deformed material. The coefficients of the integrated Voce strain hardening law were adjusted in order to fit the calculated flow curves to flow curves obtained from tensile and compression testing. Pole figures calculated with the VPSC modeling method were validated with pole figures obtained from EBSD analysis of deformed samples. VPSC modeling was successfully applied for predicting the general deformation behavior of EN AW-7075-T651 under both tension and compression. However, texture evolution during tensile testing was negligible, whereas notable texture evolution during compression testing occurred beyond a critical strain value.

Abstract: The damage state in a formed component has a significant influence on the performance of the component in service. Controlling damage evolution during forming through specific modifications of the process parameters will therefore allow an improvement of this performance. The evolution of the stress-strain state during the forming process is the primary influencing factor of the resulting damage state. The stress-strain state is influenced by the friction between tools and workpiece. To investigate the cause-effect relationship between friction and damage evolution in the deep drawing process, Finite Element simulations of the deep drawing of rotationally symmetric cups were performed. Punch velocity and blank holder force were varied. Damage was predicted using a Lemaitre damage model. The damage states predicted using a Coulomb friction law and a model incorporating a dependence on contact normal stress and relative velocity were compared. The parameter-dependent friction model predicted a change in the damage distribution after forming when varying the process parameters, which was not found using the Coulomb friction law.

Abstract: Numerical process design leads to cost and time savings in sheet metal forming processes. Therefore, a modeling of the material behavior is required to map the flow properties of sheet metal. For the identification of current yield criteria, the yield strength and the hardening behavior as well as the Lankford coefficients are taken into account. By considering the anisotropy as a function of rolling direction and stress state, the prediction quality of anisotropic materials is improved by a more accurate modeling of the yield locus curve. According to the current state of the art, the layer compression test is used to determine the corresponding Lankford coefficient for the biaxial tensile stress state. However, the test setup and the test procedure is quite challenging compared to other tests for the material characterization. Due to this, the test is only of limited suitability if only the Lankford coefficient has to be determined. In this contribution, a simplified test is presented. It is a reduction of the layer compression test to one single sheet layer. So the Lankford coefficient for the biaxial tensile stress state can be analyzed with a significantly lower test effort. The results prove the applicability of the proposed test for an easy and time efficient characterization of the biaxial Lankford coefficient.

Abstract: Single point incremental forming is a major process in a number of different industrial applications. Blank are easily found around us from furniture and household equipment to industrial machinery and equipment. To ensure high-quality sheet metal, it is vital to consider the influence of parameters. Accordingly, in this paper, the parameters affecting product quality are analyzed. Through the testing process, we obtain the parameters that help the product achieve high quality in terms of mechanics, which is an important first step for the process of developing the technology.