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Fabrication and Design of Wood-Based High-Performance Composites
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Deep Learning Methods for Wood Composites Failure Predication.

Bin Yang1, Xinfeng Wu1, Jingxin Hao2

  • 1College of Material Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China.

Polymers
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning (DL) method for rapid wood failure percentage (WFP) assessment in glulam composites. The UNet model accurately predicts WFP, improving quality control in wood composite manufacturing.

Keywords:
deep learningglulammeasurementwood failure percentage

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Area of Science:

  • Materials Science
  • Engineering
  • Computer Science

Background:

  • Traditional manual measurement of wood failure percentage (WFP) is time-consuming and insufficient for glulam bonding performance assessment.
  • Developing rapid and accurate methods for WFP assessment is crucial for quality control in wood composite manufacturing.

Purpose of the Study:

  • To develop and validate a deep learning (DL) based approach for rapid prediction of WFP in bamboo/larch laminated wood composites.
  • To compare the performance of different deep convolutional neural network (DCNN) models for WFP prediction.

Main Methods:

  • Bamboo/larch laminated wood composites bonded with phenolic resin (PF) or methylene diphenyl diisocyanate (MDI) were subjected to shear failure tests.
  • Digital images of failure surfaces were obtained using an electronic scanner and used to train DCNN models.
  • The UNet, PSPNet, and DeepLab_v3+ models were evaluated for their accuracy in predicting localized failures.

Main Results:

  • The DL technique accurately predicted localized failures in wood composites.
  • The UNet model achieved the highest performance metrics (MIou: 98.87%, Accuracy: 97.13%, F1: 94.88%) compared to PSPNet and DeepLab_v3+.
  • Optimal test conditions were identified, leading to a predicted WFP of 4.3%, matching the experimental value.

Conclusions:

  • The developed DL method, particularly the UNet model, significantly enhances the accuracy, speed, and stability of WFP assessment in wood composites.
  • This advanced approach can facilitate quality identification processes in the wood composite industry.