About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Anomaly Detection in Composite Manufacturing Using Zero-bias Deep Neural Network |
Author(s) |
Deepak Kumar, Sirish Namilae, Yongxin Liu, Houbing Herbert Song |
On-Site Speaker (Planned) |
Deepak Kumar |
Abstract Scope |
The manufacturing of high-performance composites is rapidly increasing to fulfill the needs of several industrial areas. Methods to detect and correct the processing defects during the composite manufacturing are needed to further expand their usage. Our novel experimental approach utilizes a custom autoclave with borosilicate glass viewports equipped with a 3D digital image correlation (DIC) system. The DIC is used to take timed pictures of the composite specimen during cure, which are analyzed using conventional and deep learning methods. A zero-bias Deep Neural Network (DNN) abnormality detection model is used to identify and categorize the anomalies that emerge during manufacturing. The model is trained and tested using the DIC images of composite structures generated during processing. The precise location and size of defects are determined using, a mask region based convolutional neural network (R-CNN) image segmentation technique. The model detects defects like wrinkles with a classification accuracy of 93.9 percent. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Composites, Other |