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Wood Products

Wood Products

Wood products encompass a broad range of materials crafted from wood strands, veneers, lumber, and even waste wood-like shreds, designed for both structural and nonstructural purposes. Various specialized wood products have been developed to enhance strength, durability, and versatility in building applications.
Glue-laminated wood, often referred to as glulam, combines multiple smaller pieces of dimensional lumber using adhesives to form a single, larger piece. Cross-laminated timber consists...

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Updated: May 28, 2025

Fabrication and Design of Wood-Based High-Performance Composites

Fabrication and Design of Wood-Based High-Performance Composites

Published on: November 9, 2019

A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates.

Muhammad Haris Yazdani¹, Muhammad Muzammil Azad¹, Salman Khalid²

¹Department of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

This study introduces a hybrid deep transfer learning framework for structural health monitoring (SHM) in composite laminates. The method effectively detects delamination using limited data, improving accuracy and robustness.

Keywords:

deep learning delamination detection delamination identification hybrid model transfer learning vibration signals

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Published on: November 9, 2019

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Published on: May 17, 2018

Area of Science:

Materials Science
Mechanical Engineering
Artificial Intelligence

Background:

Structural health monitoring (SHM) is crucial for laminated composite safety.
Machine learning (ML) and deep learning (DL) are increasingly used in sensor-based SHM.
ML requires manual feature extraction, while DL needs large datasets, posing limitations.

Purpose of the Study:

To present a hybrid deep transfer learning (HTL) framework for delamination identification in composite laminates.
To overcome data limitations in deep learning for SHM.
To enhance the accuracy and robustness of delamination detection.

Main Methods:

Utilized pre-trained EfficientNet and ResNet models for deep feature extraction.
Employed a hybrid approach combining EfficientNet's multi-scale feature capture and ResNet's hierarchical representation.
Validated the framework using vibration signals from piezoelectric (PZT) sensors on composite laminates with three health states.

Main Results:

The HTL framework demonstrated superior performance compared to existing transfer learning methods.
Achieved improved accuracy in delamination detection.
Showcased enhanced robustness in identifying structural damage.

Conclusions:

The proposed HTL framework effectively addresses data limitations in DL-based SHM.
This approach offers a promising solution for reliable delamination detection in composite structures.
The study highlights the potential of transfer learning in advancing SHM techniques.