Evaluation of the Timber Internal Crack Using CNN

Toyosaki, Renon; Kato, Shigeru; Tamaki, Takashi; Wada, Naoki; Kagawa, Tomomichi; Shiogai, Kazuki; Nobuhara, Hajime

doi:10.1007/978-3-031-46970-1_25

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…The CNN and conditional generation antagonism model were utilized by Liu et al to predict the cross-sectional shape and damage morphology of self-piercing riveted joints in carbon fiber-reinforced composites and aluminum alloy [ 31 ]. Recently, Kato et al evaluated the internal cracks of timbers using CNNs [ 32 , 33 ]. The optimal thickness of blending composite laminates was determined by Huynh et al using the CNN and genetic algorithm [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks

Rogalka,

Grabski,

Garbowski

2024

Sensors

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The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of layers, and environmental conditions such as humidity and temperature. This study introduces an innovative application of convolutional neural networks (CNNs) for analyzing and classifying images of corrugated boards, particularly those with deformations. For this purpose, a special device with advanced imaging capabilities, including a high-resolution camera and image sensor, was developed and used to acquire detailed cross-section images of the corrugated boards. The samples of seven types of corrugated board were studied. The proposed approach involves optimizing CNNs to enhance their classification performance. Despite challenges posed by deformed samples, the methodology demonstrates high accuracy in most cases, though a few samples posed recognition difficulties. The findings of this research are significant for the packaging industry, offering a sophisticated method for quality control and defect detection in corrugated board production. The best classification accuracy obtained achieved more than 99%. This could lead to improved product quality and reduced waste. Additionally, this study paves the way for future research on applying machine learning for material quality assessment, which could have broader implications beyond the packaging sector.

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