Technologies for the mechanical joining of aluminum die castings

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

doi:10.1063/1.5112572

Cited by 6 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to use machine learning algorithms to predict results in mechanical joining technology has already been used widely. For example, artificial neural networks have been used to predict joint strengths [3], to classify defects in radial clinching [8] and rivet head end position in SPR-ST [9], to predict forces in clinching with divided dies [10], to predict punch force [11] and to generally predict joining ability [12] in SPR-ST. Other algorithms were used, for example, in the prediction of loadbearing behavior of clinch joints (k-nearest neighbors) [13], for the determination of failure values in SPR-ST (XG Boost) [14] or joining point prediction of clinching joints [1], lockbolts [15], self-pierce riveting with solid formable rivet [16] or self-flaring rivet [17] (Kriging, moving least squares, polynomial approaches). These works only allow the prediction of discrete values and not the prediction of a complete joining point contour.…”

Section: Machine Learning and Mechanical Joiningmentioning

confidence: 99%

Realtime Prediction of Self-Pierce Riveting Joints - Prognosis and Visualization Based on Simulation and Machine Learning

Falk

Schwarz

Drossel

2022

KEM

Self Cite

View full text Add to dashboard Cite

Machine learning is used in many fields nowadays to predict events, be it a pure classification or the prediction of certain values. Thus, these methods are also increasingly used in mechanical joining technology, for example for the prediction of joint strengths, in the classification of defects and rivet head positions or in the prediction of discrete result values such as interlock. This paper further shows how the complete joint contour including the output of stresses, strains and damage can be predicted and visualized in real time for self-piercing riveting with semi-tubular rivet. First, classical sampling is carried out in experiments with steel and aluminum sheets of different types and thicknesses. These are used as a basis for the qualification of the numerical simulations. For this validation experiments and simulations are compared via joint contour and force curves. For the simulations validated in such way several tool variants are carried out in variation calculations for each material-thickness combination. The simulation meshes of the thus generated database are standardized with respect to comparability (same number of nodes) and a data reduction is performed. After testing different approximation approaches, the best possible results are predicted and can be visualized in the developed software demonstrator.

show abstract

Section: Machine Learning and Mechanical Joiningmentioning

confidence: 99%

Realtime Prediction of Self-Pierce Riveting Joints - Prognosis and Visualization Based on Simulation and Machine Learning

Falk

Schwarz

Drossel

2022

KEM

Self Cite

View full text Add to dashboard Cite

show abstract

Self-Piercing Riveting (SPR) of Magnesium High Pressure Die Castings

Tabatabaei,

Wang,

Weiler

2024

Proceedings in Engineering Mechanics

View full text Add to dashboard Cite

A Comparative Study of Self-Piercing Riveting and Friction Self-Piercing Riveting of Cast Aluminum Alloy Al–Si7Mg

Yang

et al. 2022

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

Cast aluminum alloys are promising materials that can simplify the manufacturing process of automobile body structures. However, the low ductility of cast aluminum poses significant challenges to existing riveting technologies. In the present work, dissimilar AA6061-T6 aluminum alloy and Al-Si7Mg cast aluminum were joined by self-piercing riveting (SPR) and friction self-piercing riveting (F-SPR) processes to reveal the effect of friction heat on rivetability of low-ductility cast aluminum alloys. The joint macro-morphology, microstructure, peak tooling force, microhardness distribution, tensile-shear, and cross-tension performance of the two processes were comparatively studied. Results indicated that the in-situ softening effect of friction heat in the F-SPR process could effectively improve the ductility of cast aluminum, avoid cracking, and reduce the tooling force by 53%, compared to the SPR process. The severe plastic deformation and friction heat induced by rivet rotation results in refined equiaxed grains of aluminum near the rivets and solid-state bonding between aluminum sheets in the rivet cavity. The F-SPR joints are superior to SPR joints in both tensile-shear and cross-tension performance due to the avoidance of cracking, increase of mechanical interlocking, and solid-state bonding of interfaces. Significantly, when Al-Si7Mg is placed on the lower layer, the peak tensile-shear and cross-tension loads of the F-SPR joints are 7.2% and 45.5% higher than those of the corresponding SPR joints, respectively.

show abstract

Technologies for the mechanical joining of aluminum die castings

Cited by 6 publications

References 3 publications

Realtime Prediction of Self-Pierce Riveting Joints - Prognosis and Visualization Based on Simulation and Machine Learning

Realtime Prediction of Self-Pierce Riveting Joints - Prognosis and Visualization Based on Simulation and Machine Learning

Self-Piercing Riveting (SPR) of Magnesium High Pressure Die Castings

A Comparative Study of Self-Piercing Riveting and Friction Self-Piercing Riveting of Cast Aluminum Alloy Al–Si7Mg

Contact Info

Product

Resources

About