Air-coupled ultrasound detection of natural defects in wood using ferroelectret and piezoelectric sensors

Tiitta, Markku; Tiitta, Valtteri; Gaal, Mate; Heikkinen, Jorma; Lappalainen, Reijo; Tomppo, Laura

doi:10.1007/s00226-020-01189-y

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…High-attenuation materials such as wood (Tiitta et al 2020) make measurements and evaluation complicated due to their complex orthotropic nature. According to Saadatnia et al (2015), successful evaluation of wood properties becomes more difficult when the age and diameter of a tree increase because of increased inhomogeneity by adding annual growth rings.…”

Section: Resultsmentioning

confidence: 99%

Effects of annual ring number and width on ultrasonic waves in some softwood species

Aydın

2022

BioRes

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The effect of annual ring number and width on the longitudinal (P) and transverse (S) ultrasonic wave velocities in the radial direction of black pine, Scots pine, Turkish red pine, and cedar softwoods was evaluated in this study. Annual rings were evaluated using high-resolution images captured with a Lumix camera. The 2.25 MHz P and 1 MHz S wave frequencies were propagated through the radial direction of small clear samples. An increase in ring number caused different changes in the P and S wave velocities. Only Scots pine and cedar presented continuous decreases in P and S wave velocities with the increase in annual rings. On the contrary, VR of Red pine slightly decreased and surpassed the initial value when the ring numbers increased from 5 to 10 and 15, respectively. Furthermore, the greatest decrease (4%) in the velocities was observed for VRL of Red pine. According to one-way ANOVA results, significant relations were only observed for VR vs. ring number of Black pine and cedar. R2 values ranged from 0.0001 (Red pine VR) to 0.18 (Cedar VR) for ring number and 0.0002 (Cedar VR) to 0.44 (Scots pine VR) for ring width. Furthermore, ANOVA results for linear regression analysis showed that VR of Scots pine and VR, VRL, VRT of Red pine can be statistically significantly predicted by the ring widths.

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Section: Resultsmentioning

confidence: 99%

Effects of annual ring number and width on ultrasonic waves in some softwood species

Aydın

2022

BioRes

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“…This indicates that there is a gap between working to generalize and characterize all the types of defects that are frequently found. [14]- [19], [21], [22], [25] Hole [13], [14], [19], [20], [23], [24] Pocket [5], [13], [17], [29] Stain [13], [25], [30] Decay / Rot…”

Section: Methodsmentioning

confidence: 99%

“…Besides, the identification of wood defects using statistical classifier techniques such as machine learning and deep learning can provide such robustness [35]. These machine learning approaches classify wood defects by factoring the statistical variations of the defect images to learn about the desired defects with the assistance of several classifiers such as neural networks [59], k-nearest neighbors (k-NN), decision trees and SVM [17]. On the contrary, deep learning has been shown to be highly effective in a wide range of image-based applications, including object detection and identification, facial detection and pattern identification due to their network flexibility in discovering custom defects based on the dataset [60]- [64].…”

Section: Approaches For the Identification Of Timber Defectsmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.

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“…Therefore, it becomes a reliable and effective non-destructive detection method. The air-coupled ultrasonic technique is suitable for industrial detection applications, such as the natural defects in wood (Tiitta et al, 2020 ), corn seed with hole (Yanyun et al, 2016 ) and food engineering (Fariñas et al, 2021a , b ).…”

Section: Methodsmentioning

confidence: 99%

Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding

Zhang¹,

Huang²,

Liang

et al. 2022

Front. Plant Sci.

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Slight crack of cottonseed is a critical factor influencing the germination rate of cotton due to foamed acid or water entering cottonseed through testa. However, it is very difficult to detect cottonseed with slight crack using common non-destructive detection methods, such as machine vision, optical spectroscopy, and thermal imaging, because slight crack has little effect on morphology, chemical substances or temperature. By contrast, the acoustic method shows a sensitivity to fine structure defects and demonstrates potential application in seed detection. This paper presents a novel method to detect slightly cracked cottonseed using air-coupled ultrasound with a light-weight vision transformer (ViT) and a sound-to-image encoding method. The echo signal of air-coupled ultrasound from cottonseed is obtained by non-contact and non-destructive methods. The intrinsic mode functions (IMFs) of ultrasound signal are obtained as the sound features using variational mode decomposition (VMD) approach. Then the sound features are converted into colorful images by a color encoding method. This method uses different colored lines to represent the changes of different values of IMFs according to the specified encoding period. A light-weight MobileViT method is utilized to identify the slightly cracked cottonseeds using encoding colorful images corresponding to cottonseeds. The experimental results show an average overall recognition accuracy of 90.7% for slightly cracked cottonseed from normal cottonseed, which indicates that the proposed method is reliable to applications in detection task of cottonseed with slight crack.

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Air-coupled ultrasound detection of natural defects in wood using ferroelectret and piezoelectric sensors

Cited by 12 publications

References 21 publications

Effects of annual ring number and width on ultrasonic waves in some softwood species

Effects of annual ring number and width on ultrasonic waves in some softwood species

A review of the automated timber defect identification approach

Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding

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