Algorithm-based design of mechanical joining processes

Jäckel, Mathias; Falk, Tobias; Drossel, Welf‐Guntram

doi:10.1007/s11740-022-01121-2

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…2). In order to implement the desired amount of simulations, the joining simulation was integrated into the testing simulation with little effort [4].…”

Section: Numerical Data Generationmentioning

confidence: 99%

“…In [3] an attempt was made to classify on the basis of the sheet thickness ratios whether one can be joined or not. In [4] very recent results are shown for the data-based prognosis of the mechanical joining technology self-pierce riveting. Thereby both the joint contour as well as the joint strength can be predicted via Machine Learning models.…”

Section: Introductionmentioning

confidence: 99%

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Data-Based and Analytical Models for Strength Prediction of Mechanical Joints

Vancraeynest

Jäckel²,

Coppieters³

et al. 2022

KEM

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Currently the design of mechanical joining processes like self-pierce riveting with semi-tubular rivet (SPR-ST) and clinching for production is subject to complex and experimental test series in which process parameters such as the rivet or die geometry are varied iteratively and based on experience until a suitable joint contour and strength is achieved. To simplify the use of mechanical joining technologies these development cycles and thereby the effort for implementation into production must be reduced. In this paper, the numerical data-acquisition and the development of algorithm-based and analytical models for strength prediction for SPR-ST and Clinching is described. Therefore, an extensive experimental and numerical database regarding the SPR-ST process and strength of steel and aluminum joints with tensile strengths of the sheets between 200 - 1000 MPa was generated for the building of the models. This process data could then be used for the training and evaluation of different prediction models. The goal of the research presented here is to enable an immediate prediction of the quasi-static joint strength, based on the input parameters such as properties of the parts to be joined and process parameters.

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“…2). In order to implement the desired amount of simulations, the joining simulation was integrated into the testing simulation with little effort [4].…”

Section: Numerical Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Based and Analytical Models for Strength Prediction of Mechanical Joints

Vancraeynest

Jäckel²,

Coppieters³

et al. 2022

KEM

View full text Add to dashboard Cite

show abstract

“…Other researchers have employed machine learning algorithms to predict cross-section parameters such as the X and Y interlock and the resulting joint strength [ 18 ]. This method was able to predict the joint contour within 18.8% and the joint strength within 18.6%.…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Effect of Interlock Volume on SPR Strength

et al. 2023

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During the design of automotive structures assembled using Self-Piercing Rivets (SPRs), a rivet and die combination is selected for each joint stack. To conduct extensive physical tensile testing on every joint combination to determine the range of strength achieved by each rivet–die combination, a great deal of lab technician time and substrate material are required. It is much simpler and less material-consuming to select the rivet and die solution by examining the cross sections of joints. However, the current methods of measuring cross sections by measuring the amount of mechanical interlock in a linear X–Y direction, achieved with the flared rivet tail, do not give an accurate prediction of joint strength, because they do not measure the full amount of material that must be defeated to pull the rivet tail out of the bottom sheet. The X–Y linear interlock measurement approach also makes it difficult to rapidly rank joint solutions, as it creates two values for each cross section rather than a single value. This study investigates an innovative new measurement method developed by the authors called Volumelock. The approach measures the volume of material that must be defeated to pull out the rivet. Creating a single measurement value for each rivet–die combination makes it much easier to compare different rivet and die solutions; to identify solutions that work well across a number of different stacks; to aid the grouping of stacks on one setter for low-volume line; and to select the strongest solutions for a high-volume line where only one or two different stacks are made by each setter. The joint stack results in this paper indicate that there is a good predictive relationship between the new Volumelock method and peel strength, measured by physical cross-tension testing. In this study, the Volumelock approach predicted the peel strength within a 5% error margin.

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