Numerical analysis of the robustness of clinching process considering the pre-forming of the parts

The components of a body in white consist of many individual thin-walled sheet metal parts, which usually are manufactured in deep-drawing processes. In general, the conditions in a deep-drawing process change due to changing tribology conditions, varying degrees of spring back, or scattering material properties in the sheet blanks, which affects the resulting pre-strain. Mechanical joining processes, especially clinching, are influenced by these process-related pre-strains. The final geometric shape of a clinched joint is affected to a significant level by the prior material deformation when joining with constant process parameters. That leads to a change in the stiffness and force transmission in the clinched joint due to the different geometric dimensions, such as interlock, neck thickness and bottom thickness, which directly affect the load bearing capacity. Here, the influence of the pre-straining in the deep drawing process on the force distribution in clinch points in an automotive assembly is investigated by finite-element models numerically. In further studies, the results are implemented in an optimization tool for designing clinched components. The methodology starts with a pre-straining of metal sheets. This step is followed by 2D rotationally symmetric forming simulations of the joining process. The resulting mesh of each forming simulation is rotated and 3D models are obtained. The clinched joint solid model with pre-strains is used further to determine the joint stiffnesses. With the simulation of the same test set-up with an equivalent point-connector model, the equivalent stiffness for each pre-strain combination is determined. Simulations are performed on a clinched component to assess the influence of pre-strain and sheet thinning on the clinched joint loadings by using the equivalent stiffnesses. The investigations clearly show that for the selected component, the loadings at the clinch points are dependent on the sheet thinning and the stiffnesses due to pre-strain. The magnitude of the influence varies depending on the quantity considered. For example, the shear force is more sensitive to the joint stiffness than to the sheet thinning.

Section: Metal Sheet Pre-straining and Clinching Processmentioning

confidence: 64%

Section: Methodsmentioning

confidence: 99%

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Numerical investigation of the clinched joint loadings considering the initial pre-strain in the joining area

Martin

Bielak

Bobbert

et al. 2022

“…The geometry of the joint can be considered for evaluation and calibration [29] until an acceptable deviation from the real experiment is achieved [28]. In [30] the influence of strain hardening and the variation of sheet thickness on the joint characteristics t n , f and t b are studied using a metamodel to investigate the robustness of the clinching process. In [31] the possibilities and limitations of FEM-based sensitivity analysis and optimization for the clinching process are discussed.…”

Section: Finite Element Analysismentioning

confidence: 99%

Analysis of the interactions between joint and component properties during clinching

Steinfelder

Acksteiner

Guilleaume

et al. 2022

Clinching is a joining process that is becoming more and more important in industry due to the increasing use of multi-material designs. Despite the already widespread use of the process, there is still a need for research to understand the mechanisms and design of clinched joints. In contrast to the tool parameters, process and material disturbances have not yet been investigated to a relatively large extent. However, these also have a great influence on the properties and applicability of clinching. The effect of process disturbances on the clinched joint are investigated with numerical and experimental methods. The investigated process variations are the history of the sheets using the pre-hardening of the material, different sheet thicknesses, sheet arrangements and punch strokes. For the consideration of the material history, a specimen geometry for pre-stretching specimens in uniaxial tension is used, from which the pre-stretched secondary specimens are taken. A finite element model is set up for the numerical investigations. Suitable clinching tools are selected. With the simulation, selected process influences can be examined. The effort of the numerical investigations is considerably reduced with the help of a statistical experimental design according to Taguchi. To confirm the simulation results, experimental investigations of the clinch point geometry by using micrographs and the shear strength of the clinched joint are performed. The analysis of the influence of difference disturbance factors on the clinching process demonstrate the importance of the holistic view of the clinching process.

“…Since the clinching process involves highly nonlinear deformations and large element distortions, the software LS-DYNA offers an appropriate environment for the generation of a finite element model. Therefore, the simulation model was validated in [19] showing a sufficient similarity in comparison to experimental microsections by analyzing the qualitatively relevant characteristics interlock, neck and bottom thickness as well as the sheet metal contour. Resulting, the FE model represents a 2D-axisymmetric structure including the following components: die, punch, blank holder, upper and lower sheet.…”

Section: Numerical Clinching Processmentioning

confidence: 99%

Application of reinforcement learning for the optimization of clinch joint characteristics

Zirngibl

Dworschak

Schleich

et al. 2021

Due to increasing challenges in the area of lightweight design, the demand for time- and cost-effective joining technologies is steadily rising. For this, cold-forming processes provide a fast and environmentally friendly alternative to common joining methods, such as welding. However, to ensure a sufficient applicability in combination with a high reliability of the joint connection, not only the selection of a best-fitting process, but also the suitable dimensioning of the individual joint is crucial. Therefore, few studies already investigated the systematic analysis of clinched joints usually focusing on the optimization of particular tool geometries against shear and tensile loading. This mainly involved the application of a meta-model assisted genetic algorithm to define a solution space including Pareto optima with all efficient allocations. However, if the investigation of new process configurations (e. g. changing materials) is necessary, the earlier generated meta-models often reach their limits which can lead to a significantly loss of estimation quality. Thus, it is mainly required to repeat the time-consuming and resource-intensive data sampling process in combination with the following identification of best-fitting meta-modeling algorithms. As a solution to this problem, the combination of Deep and Reinforcement Learning provides high potentials for the determination of optimal solutions without taking labeled input data into consideration. Therefore, the training of an Agent aims not only to predict quality-relevant joint characteristics, but also at learning a policy of how to obtain them. As a result, the parameters of the deep neural networks are adapted to represent the effects of varying tool configurations on the target variables. This provides the definition of a novel approach to analyze and optimize clinch joint characteristics for certain use-case scenarios.