TY - GEN
T1 - Process monitoring of friction stir blind riveting for lightweight materials
AU - Guo, Weihong Grace
AU - Chen, Jingyu
AU - Guo, Shenghan
AU - Li, Jingjing
N1 - Funding Information:
This work was partially funded by the Rutgers University Research Council Grant and the US National Science Foundation Civil, Mechanical and Manufacturing Innovation Grant No. 1664377.
PY - 2017
Y1 - 2017
N2 - Friction stir blind riveting (FSBR) is a new process for joining automotive lightweight dissimilar materials. During FSBR, a blind rivet rotates at high speed, contacts with the upper sheet or workpiece of a lap joint, and then penetrates the workpieces. Using FSBR to join carbon fiber-reinforced polymer composite and aluminum alloy sheets has been studied experimentally, however, the quantitative relationship between the FSBR process and joint quality/strength remains unclear. To gain a better understanding of FSBR, the proposed method effectively models this relationship by integrating data de-noising, feature extraction, feature selection, and classifier fusion. Engineering-based features are extracted directly from the FSBR penetration force and torque signals; data-driven features are extracted using principal component analysis. Regression models and kernel support vector machines (SVMs) are trained and fused for quality prediction. The proposed method provides online monitoring of FSBR and prediction of joint quality.
AB - Friction stir blind riveting (FSBR) is a new process for joining automotive lightweight dissimilar materials. During FSBR, a blind rivet rotates at high speed, contacts with the upper sheet or workpiece of a lap joint, and then penetrates the workpieces. Using FSBR to join carbon fiber-reinforced polymer composite and aluminum alloy sheets has been studied experimentally, however, the quantitative relationship between the FSBR process and joint quality/strength remains unclear. To gain a better understanding of FSBR, the proposed method effectively models this relationship by integrating data de-noising, feature extraction, feature selection, and classifier fusion. Engineering-based features are extracted directly from the FSBR penetration force and torque signals; data-driven features are extracted using principal component analysis. Regression models and kernel support vector machines (SVMs) are trained and fused for quality prediction. The proposed method provides online monitoring of FSBR and prediction of joint quality.
KW - Feature extraction
KW - Feature selection
KW - Lightweight material manufacturing
KW - Process monitoring
KW - Quality prediction
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M3 - Conference contribution
AN - SCOPUS:85031025135
T3 - 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
SP - 2165
EP - 2170
BT - 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
A2 - Nembhard, Harriet B.
A2 - Coperich, Katie
A2 - Cudney, Elizabeth
PB - Institute of Industrial Engineers
T2 - 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
Y2 - 20 May 2017 through 23 May 2017
ER -