Process Prediction and Feature Visualization of Meltblown Nonwoven Fabrics Using Scanning Electron Microscopic (SEM) Image-Based Deep Neural Network Algorithms

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 pro...

Full description

Saved in:
Bibliographic Details
Published in:Processes 2023-12, Vol.11 (12), p.3388
Main Authors: Cho, Kyung-Chul, Park, Si-Woo, Lee, Injun, Shim, Jaesool
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Correlation
Deep learning
Efficiency
Fabrics
Fault detection
Image classification
Mathematical models
Model accuracy
Neural networks
Nonwoven fabrics
Object recognition
Production processes
Protective clothing
Scanning devices
Scanning electron microscopy
Textile industry
Variables
Visualization
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11123388