We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Process Prediction and Feature Visualization of Meltblown Nonwoven Fabrics Using Scanning Electron Microscopic (SEM) Image-Based Deep Neural Network Algorithms.
- Authors
Cho, Kyung-Chul; Park, Si-Woo; Lee, Injun; Shim, Jaesool
- Abstract
Meltblown nonwoven fabrics are used in various products, such as masks, protective clothing, industrial filters, and sanitary products. As the range of products incorporating meltblown nonwoven fabrics has recently expanded, numerous studies have been conducted to explore the correlation between production process conditions and the performance of meltblown nonwoven fabrics. Deep neural network algorithms, including convolutional neural networks (CNNs), have been widely applied in numerous industries for tasks such as object detection, recognition, classification, and fault detection. In this study, the correlation between the meltblown nonwoven fabric production process and performance was analyzed using deep neural network algorithms for classifying SEM images. The SEM images of meltblown nonwovens produced under various process conditions were trained using well-known convolutional neural network models (VGG16, VGG19, ResNet50, and DenseNet121), and each model showed high accuracy ranging from 95% to 99%. In addition, LRP (Layer-wise Relevance Propagation) and Gradient-weighted Class Activation Mapping (Grad-CAM) models were applied to visualize and analyze the characteristics and correlation of the SEM images to predict the meltblown nonwoven fabric production process.
- Subjects
ARTIFICIAL neural networks; CONVOLUTIONAL neural networks; NONWOVEN textiles; OBJECT recognition (Computer vision); DATA visualization
- Publication
Processes, 2023, Vol 11, Issue 12, p3388
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr11123388