Abstract
Friction stir blind riveting (FSBR) is a recently developed manufacturing process for joining dissimilar lightweight materials. The objective of this study is to gain a better understanding of FSBR in joining carbon fiber-reinforced polymer composite and aluminum alloy sheets by developing a sensor fusion and process monitoring method. The proposed method establishes the relationship between the FSBR process and the quality of the joints by integrating feature extraction, feature selection, and classifier fusion. This study investigates the effectiveness of lower rank tensor decomposition methods in extracting features from multi-sensor, high-dimensional, heterogeneous profile data. The extracted features are combined with process parameters, material stack-up sequence, and engineering-driven features such as the peak force to provide rich information about the FSBR process. Sparse group lasso regression is adopted to select the optimal monitoring features. The selected features are fed into weighted classification fusion to estimate the quality of the joints. The fusion method integrates five individual classifiers with optimal weights. The correct classification rates resulted from various feature extraction and selection methods are assessed and compared. The proposed method can also be applied to other manufacturing processes with online sensing capabilities for the purpose of process monitoring and quality prediction.
| Original language | English (US) |
|---|---|
| Title of host publication | Manufacturing Equipment and Systems |
| Publisher | American Society of Mechanical Engineers (ASME) |
| ISBN (Print) | 9780791851371 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 - College Station, United States Duration: Jun 18 2018 → Jun 22 2018 |
Publication series
| Name | ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 |
|---|---|
| Volume | 3 |
Other
| Other | ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 |
|---|---|
| Country | United States |
| City | College Station |
| Period | 6/18/18 → 6/22/18 |
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All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
Cite this
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Sensor fusion and on-line monitoring of friction stir blind riveting for lightweight materials manufacturing. / Gao, Zhe; Guo, Weihong; Li, Jingjing.
Manufacturing Equipment and Systems. American Society of Mechanical Engineers (ASME), 2018. (ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018; Vol. 3).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Sensor fusion and on-line monitoring of friction stir blind riveting for lightweight materials manufacturing
AU - Gao, Zhe
AU - Guo, Weihong
AU - Li, Jingjing
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Friction stir blind riveting (FSBR) is a recently developed manufacturing process for joining dissimilar lightweight materials. The objective of this study is to gain a better understanding of FSBR in joining carbon fiber-reinforced polymer composite and aluminum alloy sheets by developing a sensor fusion and process monitoring method. The proposed method establishes the relationship between the FSBR process and the quality of the joints by integrating feature extraction, feature selection, and classifier fusion. This study investigates the effectiveness of lower rank tensor decomposition methods in extracting features from multi-sensor, high-dimensional, heterogeneous profile data. The extracted features are combined with process parameters, material stack-up sequence, and engineering-driven features such as the peak force to provide rich information about the FSBR process. Sparse group lasso regression is adopted to select the optimal monitoring features. The selected features are fed into weighted classification fusion to estimate the quality of the joints. The fusion method integrates five individual classifiers with optimal weights. The correct classification rates resulted from various feature extraction and selection methods are assessed and compared. The proposed method can also be applied to other manufacturing processes with online sensing capabilities for the purpose of process monitoring and quality prediction.
AB - Friction stir blind riveting (FSBR) is a recently developed manufacturing process for joining dissimilar lightweight materials. The objective of this study is to gain a better understanding of FSBR in joining carbon fiber-reinforced polymer composite and aluminum alloy sheets by developing a sensor fusion and process monitoring method. The proposed method establishes the relationship between the FSBR process and the quality of the joints by integrating feature extraction, feature selection, and classifier fusion. This study investigates the effectiveness of lower rank tensor decomposition methods in extracting features from multi-sensor, high-dimensional, heterogeneous profile data. The extracted features are combined with process parameters, material stack-up sequence, and engineering-driven features such as the peak force to provide rich information about the FSBR process. Sparse group lasso regression is adopted to select the optimal monitoring features. The selected features are fed into weighted classification fusion to estimate the quality of the joints. The fusion method integrates five individual classifiers with optimal weights. The correct classification rates resulted from various feature extraction and selection methods are assessed and compared. The proposed method can also be applied to other manufacturing processes with online sensing capabilities for the purpose of process monitoring and quality prediction.
UR - http://www.scopus.com/inward/record.url?scp=85054988671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054988671&partnerID=8YFLogxK
U2 - 10.1115/MSEC2018-6507
DO - 10.1115/MSEC2018-6507
M3 - Conference contribution
AN - SCOPUS:85054988671
SN - 9780791851371
T3 - ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
BT - Manufacturing Equipment and Systems
PB - American Society of Mechanical Engineers (ASME)
ER -