Simulation of self-piercing rivetting processes in fibre reinforced polymers: Material modelling and parameter identification

Hirsch, Franz; Müller, Sebastian; Machens, Michael; Staschko, Robert; Fuchs, Normen; Kästner, Markus

doi:10.1016/j.jmatprotec.2016.10.010

Cited by 44 publications

(12 citation statements)

References 36 publications

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“…the layers [8]. This modelling approach enables a more precise description of damage, delamination and reorientation phenomena during the joining process.…”

Section: /2mentioning

confidence: 99%

Modelling of thermally supported clinching of fibre-reinforced thermoplastics: Approaches on mesoscale considering large deformations and fibre failure

Gröger¹,

Hornig²,

Hoog³

et al. 2021

Esaform 2021

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Thermally supported clinching (Hotclinch) is a novel promising process to join dissimilar materials. Here, metal and fibre-reinforced thermoplastics (FRTP) are used within this single step joining process and without the usage of auxiliary parts like screws or rivets. For this purpose, heat is applied to improve the formability of the reinforced thermoplastic. This enables joining of the materials using conventional clinching-tools. Focus of this work is the modelling on mesoscopic scale for the numerical simulation of this process. The FTRP-model takes the material behaviour both of matrix and the fabric reinforced organo-sheet under process temperatures into account. For describing the experimentally observed phenomena such as large deformations, fibre failure and the interactions between matrix and fibres as well as between fibres themselves, the usage of conventional, purely Lagrangian based FEM methods is limited. Therefore, the combination of contact-models with advanced modelling approaches like Arbitrary-Lagrangian-Eulerian (ALE), Coupled-Eulerian-Lagrangian (CEL) and Smooth-ParticleHydrodynamics (SPH) for the numerical simulation of the clinching process are employed. The different approaches are compared with regard to simulation feasibility, robustness and results accuracy. It is shown, that the CEL approach represents the most promising approach to describe the clinching process.

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“…the layers [8]. This modelling approach enables a more precise description of damage, delamination and reorientation phenomena during the joining process.…”

Section: /2mentioning

confidence: 99%

Modelling of thermally supported clinching of fibre-reinforced thermoplastics: Approaches on mesoscale considering large deformations and fibre failure

Gröger¹,

Hornig²,

Hoog³

et al. 2021

Esaform 2021

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“…Moreover, Kroll et al [12] experimentally performed the SPR of CFRP and aluminum alloy sheets by focusing on the fiber orientations and simulated the joint formation using FEM. Hirsch et al [13] simulated the SPR of fiber-reinforced polymers and metal sheets using the FEM. The simulation was based on a damage model to consider the deformation and failure behavior of the composites.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process

Kim

Jeong

et al. 2020

IEEE Access

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Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including finite element modeling, but in this study, a data-driven approach consisting of two supervised deep-learning models was proposed. The first model was used for data transformation from an optical microscopic image to a material segmentation map, which characterizes the shape and location of the two sheets and the rivet by applying a convolutional neural network (CNN)based deep-learning structure. To validate the developed models, two types of sheet combinations were tested, namely, carbon-fiber-reinforced plastic (CFRP) and galvanized dual-phase steel (GA590DP) sheets, and steel alloy (SPFC590DP) and aluminum alloy (Al5052-H32) sheets. The transformation was performed with a mean intersection-over-union of 98.50% and a mean pixel accuracy of 99.78%. The next model, which was a novel generative model based on a CNN and conditional generative adversarial network with residual blocks, was then trained to predict the cross-sectional shape from the input punch force. The predicted cross-sectional shapes were compared with the experimental results of SPR. The overall accuracy was 94.20% for CFRP-GA590DP and 96.31% for SPFC590DP-Al5052, with respect to three key geometrical indexes, namely, rivet head height, interlock length, and bottom thickness.

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“…The development process of such structure's places high demands on the engineer due to the complex interactions between design, dimensioning and manufacturing [2]. Numerical optimization of the production process of such lightweight assemblies is therefore of high importance [3]. It can reduce the time to market and can avoid the production of costly prototypes.…”

Section: Introductionmentioning

confidence: 99%

Thermo‐Mechanical Modeling of Pre‐Consolidated Fiber‐Reinforced Plastics for the Simulation of Thermoforming Processes

Ziegs

Weck

Gude

et al. 2019

Proc Appl Math and Mech

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Multi-material design aims at the targeted combination of materials with different characteristics in order to meet technical requirements. Especially the combination of metals and fiber-reinforced plastics (FRP) has led to innovative lightweight structures with high loading capacity and ductility in recent years. The process chain required to produce such structures is characterized by a variety of process parameters which have a significant influence on the quality of the manufactured workpiece. In order to treat the thermoforming process numerically, we present constitutive models for the metal and composite part to simulate the deformation behavior of these components. In addition, experimental setups are described to identify the required material parameters. Simulations of the manufacturing process will indicate correlations between material as well as process parameters and possible defects of the final structure that may occur during manufacturing.

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Simulation of self-piercing rivetting processes in fibre reinforced polymers: Material modelling and parameter identification

Cited by 44 publications

References 36 publications

Modelling of thermally supported clinching of fibre-reinforced thermoplastics: Approaches on mesoscale considering large deformations and fibre failure

Modelling of thermally supported clinching of fibre-reinforced thermoplastics: Approaches on mesoscale considering large deformations and fibre failure

Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process

Thermo‐Mechanical Modeling of Pre‐Consolidated Fiber‐Reinforced Plastics for the Simulation of Thermoforming Processes

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