Quality prediction and rivet/die selection for SPR joints with artificial neural network and genetic algorithm

Zhao, Haibin; Han, Lei; Liu, Yunpeng; Liu, Xianping

doi:10.1016/j.jmapro.2021.04.033

Cited by 26 publications

(4 citation statements)

References 36 publications

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“…It is further used in engine nacelles, high load parts and other auxiliary components. 1 The Al-Mg-Si 6061 aluminum alloy has the advantages of high specific strength, excellent machinability, corrosion resistance, oxidation resistance, etc. Therefore, it is widely used in structural parts of aerospace electronic products.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the fatigue fractures failure mechanism of the joint was studied. 1 He et al conducted a study on titanium alloy SPR joints and found that the static strength of the titanium sheet as the top sheet of the joint was higher than that of the bottom sheet. 9 Zhang et al investigated the mechanical properties of the SPR joint with the local softening zone.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance analysis of similar and dissimilar self-piercing riveted joints in aluminum alloys

Zhao,

Xiaole,

Abbas

et al. 2023

Composites and Advanced Materials

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In this work, similar (2A12) and dissimilar (6061) aluminum alloy sheets are validly joined using self-piercing rivet process. A quasi-static experiment is proposed to investigate the mechanical behaviors, failures mode, and mechanism of the different joints. Moreover, a method based on deep learning algorithm is anticipated to detect the appearance defects of the SPR welded joints. The results indicated that 2A12 joints of similar sheets contained the advantageous static strength and 6061 similar sheet joints had superior anti-vibration performance conducts. The joints with 6061-2A12 sheets introduced the most decent and comprehensive mechanical properties. The main failure mode of 2A12 similar sheet joints was substrate fracture. The performance of the substrate affects the failure mode of the joint and the plasticity of the substrate is better. When the time comes, the failure mode is mostly pull-off failure. Poor plasticity of the substrate can easily lead to substrate breakage. The reason for joint pull-off and button fall-off failure is that there is large plastic deformation in the lower plate of the joint and the mechanical internal locking structure is damaged. 2A12 substrate breakage belongs to a composite fracture that combines intergranular fracture and microvoid aggregation type fracture. The area of the 6061 substrate near the edge of the sample is shear fracture and the area near the center of the sample thickness is dominated by microvoid aggregation type normal fracture. The effectiveness of the method was verified by conducting a series of experiments and the detection accuracy of the method can reach about 90%. The detection speed was as high as 50 frames per second (FPS), which can effectively solve the problem that the rivet quality was difficult to monitor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis of similar and dissimilar self-piercing riveted joints in aluminum alloys

Zhao,

Xiaole,

Abbas

et al. 2023

Composites and Advanced Materials

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show abstract

“…During the last few decades, lightweight materials have been increasingly used in the automotive industry to reduce the vehicle weight without compromising its performances [1]. Self-piercing riveting (SPR) technique experiences a rapid development under such circumstances, and has been widely adopted by many automobile companies (e.g., BMW, JLR and Audi) as the major joining approach to assemble the aluminum car body-inwhite (BIW) structures [2]. The SPR process is consist of four major steps as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Analysis of joint formation mechanisms for self-piercing riveting (SPR) process with varying joining parameters

Zhao

Han

Liu

et al. 2022

Journal of Manufacturing Processes

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“…Through SPR technology combined with back propagation neural network, the interlock and residual bottom thickness etc. are optimized through ANN 24 . The SPR molding process can easily lead to various forming defects due to differences in rivet quality, sheet quality differences and equipment stability.…”

mentioning

confidence: 99%

Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning

Lin

Zhao

Wang

et al. 2023

Sci Rep

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Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm’s accuracy in this paper is increased by 4.5$$\%$$ % , and the recall is increased by 1.4$$\%$$ % . In addition, the amount of redundant parameters is reduced by 86.5$$\%$$ % , and the amount of computation is reduced by 47.33$$\%$$ % . This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality.

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Quality prediction and rivet/die selection for SPR joints with artificial neural network and genetic algorithm

Cited by 26 publications

References 36 publications

Performance analysis of similar and dissimilar self-piercing riveted joints in aluminum alloys

Performance analysis of similar and dissimilar self-piercing riveted joints in aluminum alloys

Analysis of joint formation mechanisms for self-piercing riveting (SPR) process with varying joining parameters

Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning

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