Gathering of Process Data through Numerical Simulation for the Application of Machine Learning Prognosis Algorithms

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

doi:10.1016/j.promfg.2020.04.186

Cited by 10 publications

(22 citation statements)

References 4 publications

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“…Thereby the process parameters for each material combination was chosen by experience and the joint contour was obtained by means of micrograph preparations and measurement of the characteristic values (Fig. 1) [13]. In addition to the joint contour the quasi-static strength of the 71 material combinations was determined with respect to their load-bearing capacity in shear and top tension (Fig.…”

Section: Experimental and Numerical Process Data Miningmentioning

confidence: 99%

“…3) were built and validated for each of the 71 experimentally joined material combinations for the SPR-ST process as well as the following quasi-static shear and top tensile tests. The boundary conditions for the simulation models for joining process can be found in [13]. The flow curves for the process simulation Fig.…”

Section: Experimental and Numerical Process Data Miningmentioning

confidence: 99%

“…Due to the acceptable agreement between experiment and simulation, these 50 simulation models were used for the numerical variation calculations. Therefore material properties conditions like material flow curve, sheet thickness, die and rivet geometry were varied [13]. In total, based on a statistical experimental design, 19 variations per simulation model were built and calculated for the strength calculations, resulting in a total of 1.000 simulation models for the SPR-ST process with the following strength calculation.…”

Section: Experimental and Numerical Process Data Miningmentioning

confidence: 99%

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Algorithm-based design of mechanical joining processes

Jäckel

Falk

Drossel

2022

Prod. Eng. Res. Devel.

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In this paper, the development of algorithm-based process models for the mechanical joining process self-pierce riveting with semi-tubular rivet (SPR-ST) 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 and 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 algorithms. Furthermore, the simulation data is applied to predict the entire contour (mesh) of non-simulated joints. This includes the visualization of output values such as strains, stresses and damage for each element and node of the mesh. That approach enables to obtain more information about the joint than just discrete values such as interlock or strength.

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Section: Experimental and Numerical Process Data Miningmentioning

confidence: 99%

Section: Experimental and Numerical Process Data Miningmentioning

confidence: 99%

Section: Experimental and Numerical Process Data Miningmentioning

confidence: 99%

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Algorithm-based design of mechanical joining processes

Jäckel

Falk

Drossel

2022

Prod. Eng. Res. Devel.

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“…An average computing time of 13 minutes per SPR process simulation is achieved on a workstation with 14 cores. [11] The chosen parameters of the combined friction model are a result of a numerical sensitivity analysis by fitting the calculated with the experimental joint contour and force-displacement curve of the SPR process. Flow curves for the sheet and the rivet material are determined by stack compression tests (SCT) due to the good comparability in terms of stress state between SPR and SCT [12].…”

Section: Numerical Sensiti Numerical Sensitivity Stud Vity Study Ymentioning

confidence: 99%

“…In total, process data for 2376 SPR joints could be generated by this methodology, which from a technological point of view also meet the requirements of a proper joint formation. [11] Fig. 5.…”

Section: Numerical Sensiti Numerical Sensitivity Stud Vity Study Ymentioning

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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