Concept for Further Development of Self-pierce Riveting by Using Cyber Physical Systems

Jäckel, Mathias; Falk, Tobias; Landgrebe, Dirk

doi:10.1016/j.procir.2016.02.073

Cited by 18 publications

(10 citation statements)

References 6 publications

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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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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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Existing work on the influence of process inputs on the SPR joint has tended to focus on either a single factor, such as the riveting velocity, [7][8][9][10] or a group of factors relating to the properties of the rivet, material and die. [11][12][13] Papers that have studied the sensitivity of the process via a model-based approach are centred around process parameters on the rivet-material level. [13][14][15] To date, the behaviour of the riveting machine and its influence on the joint is not fully understood.…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13] Papers that have studied the sensitivity of the process via a model-based approach are centred around process parameters on the rivet-material level. [13][14][15] To date, the behaviour of the riveting machine and its influence on the joint is not fully understood.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling of a servo self-pierce riveting (SPR) process

Tang

Evans

Briskham

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Self-pierce riveting (SPR) is a complex joining process where multiple layers of material are joined by creating a mechanical interlock via the simultaneous deformation of the inserted rivet and surrounding material. Due to the large number of variables which influence the resulting joint, finding the optimum process parameters has traditionally posed a challenge in the design of the process. Furthermore, there is a gap in knowledge regarding how changes made to the system may affect the produced joint. In this paper, a new system-level model of an inertia-based SPR system is proposed, consisting of a physics-based model of the riveting machine and an empirically-derived model of the joint. Model predictions are validated against extensive experimental data for multiple sets of input conditions, defined by the setting velocity, motor current limit and support frame type. The dynamics of the system and resulting head height of the joint are predicted to a high level of accuracy. Via a model-based case study, changes to the system are identified, which enable either the cycle time or energy consumption to be substantially reduced without compromising the overall quality of the produced joint. The predictive capabilities of the model may be leveraged to reduce the costs involved in the design and validation of SPR systems and processes.

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“…In addition, with a 2D simulation model in DEFORM, Mathias et al [11] studied the effects of the die geometric parameters on the SPR joint quality and found that the die depth was the most significant die parameter. A novel die geometry with a moveable bottom was proposed in their paper to compensate the changes of the boundary conditions, and thus led to a consistent joint quality.…”

Section: Introductionmentioning

confidence: 99%