Increasing flexibility of self-pierce riveting using numerical and statistical methods

Falk, Tobias; Jäckel, Mathias

doi:10.1016/j.promfg.2019.02.137

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…From literature, it has been observed that black-box models such as neural networks and fuzzy logic-based models are widely used to build models of the manufacturing process from measured input/output data [2,11,13]. These approaches perform better rather than statistical models [14] and mathematical equations. However, artificial neural networks and fuzzy logic have limitations.…”

Section: Problem Statementmentioning

confidence: 99%

Fuzzy pattern modeling of self-pierce riveting for data from experiments and computer simulations

Nemati

Jäckel

Bocklisch

et al. 2022

Int J Adv Manuf Technol

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Modeling forms the basis for optimal control of complex technical processes in the context of industry 4.0 development and, hence, for high product quality as well as efficient production. For the mechanical joining process of self-pierce riveting with 11 input and 5 output variables, two modeling approaches based on (1) experimental data and (2) FEM computer simulation are outlined and performed. A physical modeling approach is ruled out due to the high problem dimensionality and complex nonlinear dynamic relationships between input and output variables. Alternatively, data-based approaches lead to Artificial Intelligence (AI) model designs. The experimental approach is cost- and resource-consuming; therefore, only a relatively small data set can be collected. Here, we present results from experimental trials that serve as representatives and are generalized by a description with high-dimensional parametric membership functions (fuzzification). The fuzzification procedure is also applied to the FEM computer simulation results. In principle, it can provide an arbitrarily large database. However, consequently, time- and computational effort increase considerably. Both data sets form the basis for parallel model building using the AI method of local fuzzy pattern models, which can be used to describe highly nonlinear input-output relationships by error-minimizing partitioning. Finally, the comparison of the results of the two modeling approaches is outlined. Finally, a coupled modeling strategy and future model adaptation are proposed.

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Section: Problem Statementmentioning

confidence: 99%

Fuzzy pattern modeling of self-pierce riveting for data from experiments and computer simulations

Nemati

Jäckel

Bocklisch

et al. 2022

Int J Adv Manuf Technol

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“…In [3][4][5][6][7], this method was applied to self-piercing riveting and it was demonstrated that a larger scope of FEM calculations for metamodeling can also be realized for this process.…”

Section: Moti Motiv Vation Ationmentioning

confidence: 99%

Development of General data-based Process Models for Self-Pierce Riveting

Jäckel¹,

Falk²,

Kropp³

et al. 2021

Esaform 2021

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The determination of ideal process parameters for mechanical joining processes such as self-pierce riveting currently requires a comprehensive understanding of the process, the availability of the materials to be joined and the corresponding system technology. General process models can simplify the use of these joining technologies, accelerate development cycles and thereby reduce the effort for implementation into production. In this paper, the development of general data-based process models for the mechanical joining method self-pierce riveting with semi-tubular rivet is described. Extensive experimental and numerical investigations with more than 2300 joint combinations for steel and aluminum sheets with tensile strengths between 240 - 1020 MPa were generated for the building of the models. Based on these results, different meta-models are fused into general data-based process models for the self-pierce riveting process in order to show the general relationships between material properties, process parameters and joining results. The paper discusses the acquisition of the experimental and numerical data, the statistical methods for evaluation and the application of the data-based process models.

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“…6 cannot be attained in material combination 1, even for a die with a lower depth. In [16], the joining range of one rivet geometry was extended through the development of an optimised die shape and, in [17], a new die concept was developed to achieve better joining results. However, an analysis of the material flow shows that the dies used for the sampling are already the best choice, given the requirements of the materials in the present investigation.…”

Section: Analysis Of the Process And Improvement Of The Rivet Geometrymentioning

confidence: 99%

Improvement of a rivet geometry for the self-piercing riveting of high-strength steel and multi-material joints

Uhe

Kuball

Merklein

et al. 2020

Prod. Eng. Res. Devel.

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As a result of lightweight design, increased use is being made of high-strength steel and aluminium in car bodies. Self-piercing riveting is an established technique for joining these materials. The dissimilar properties of the two materials have led to a number of different rivet geometries in the past. Each rivet geometry fulfils the requirements of the materials within a limited range. In the present investigation, an improved rivet geometry is developed, which permits the reliable joining of two material combinations that could only be joined by two different rivet geometries up until now. Material combination 1 consists of high-strength steel on both sides, while material combination 2 comprises aluminium on the punch side and high-strength steel on the die side. The material flow and the stress and strain conditions prevailing during the joining process are analysed by means of numerical simulation. The rivet geometry is then improved step-by-step on the basis of this analysis. Finally, the improved rivet geometry is manufactured and the findings of the investigation are verified in experimental joining tests.

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Increasing flexibility of self-pierce riveting using numerical and statistical methods

Cited by 10 publications

References 4 publications

Fuzzy pattern modeling of self-pierce riveting for data from experiments and computer simulations

Fuzzy pattern modeling of self-pierce riveting for data from experiments and computer simulations

Development of General data-based Process Models for Self-Pierce Riveting

Improvement of a rivet geometry for the self-piercing riveting of high-strength steel and multi-material joints

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