Applied Research on Prediction Methods of Properties of Particleboard Based on Data-Driven Methods

Yang, Cuiping; Lai, Wei-Wen; Su, Jilai; He, Wei; Gao, Zhenhua

doi:10.1166/jbmb.2021.2035

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…2 College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China. 3 Nanjing University of Information Science and Technology, 333 Xishan Rd., Avenue, Wuxi 214105, China.…”

Section: Abbreviationsmentioning

confidence: 99%

“…Particleboards (PB) have advantages of low cost, good processing performance, renewable raw materials, and reasonable mechanical properties, which have become one of the commonly used panels for furniture and indoor decoration [1]. In the PB production process, the particle gluing process operation has a great influence on the mechanical properties of PB [2], so operators often adjust the operating parameters of particle gluing according to the mechanical properties test results of PB [3]. However, depending only on the actual production experience of operators, it is difficult to accurately control the particle gluing operating parameters.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

et al. 2022

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Particle gluing operating parameters in particleboard (PB) production have an important influence on the mechanical properties of PBs. This study developed a multi-objective optimization model based on support vector regression (SVR) optimized by the non-dominated sorted genetic algorithm-II (NSGA2) to realize the multi-objective accurate prediction of PB mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bonding (IB) strength) by adjusting particle gluing operating parameters. The NSGA2-SVR multi-objective prediction model was trained by 496 groups of experimental data of particle gluing operating parameters and PB mechanical properties. The prediction results of the NSGA2-SVR multi-objective prediction model were evaluated by 124 groups of experimental data and compared with the prediction results of the back propagation neural network (BPNN) model, general regression neural network (GRNN) model, and SVR model. The mean absolute percentage errors (MAPEs) of the NSGA2-SVR model were 49.11%, 33.64%, and 24.20% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The Theil’s inequality coefficients (TICs) of the NSGA2-SVR model were 40.93%, 27.39%, and 18.58% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The results showed that the multi-objective prediction model based on NSGA2-SVR has a superior fitting and higher prediction accuracy for the prediction performance of particle gluing operating parameters, and the NSGA2-SVR model can be applied to the multi-objective synchronous prediction of particle gluing operating parameters in the PB production line.

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Section: Abbreviationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

et al. 2022

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Research on the Identification of Particleboard Surface Defects Based on Improved Capsule Network Model

Wang

Liu

Wang

et al. 2023

Forests

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Aiming at the problems of low classification accuracy and overfitting caused by the limited number of particleboard image samples, a Capsule Network algorithm based on the improved CBAM (Convolutional Block Attention Module) attention model is proposed. The improved algorithm utilizes the GELU equation to improve the CBAM attention model and incorporates it into the convolutional layer of the Capsule Network. In this way, the improved algorithm optimizes the feature maps of surface defects and, meanwhile, improves the training efficiency and stability of the model. The improved algorithm alleviates the overfitting problem by adding a dropout layer, which makes the model more suitable for small sample classification. The effectiveness of the method proposed in this paper is verified by classification experiments on the dataset of particleboard surface defect images.

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Intelligent Detection Method for Surface Defects of Particleboard Based on Super-Resolution Reconstruction

Zhou,

Xia,

Fan

et al. 2024

Forests

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To improve the intelligence level of particleboard inspection lines, machine vision and artificial intelligence technologies are combined to replace manual inspection with automatic detection. Aiming at the problem of missed detection and false detection on small defects due to the large surface width, complex texture and different surface defect shapes of particleboard, this paper introduces image super-resolution technology and proposes a super-resolution reconstruction model for particleboard images. Based on the Transformer network, this model incorporates an improved SRResNet (Super-Resolution Residual Network) backbone network in the deep feature extraction module to extract deep texture information. The shallow features extracted by conv 3 × 3 are then fused with features extracted by the Transformer, considering both local texture features and global feature information. This enhances image quality and makes defect details clearer. Through comparison with the traditional bicubic B-spline interpolation method, ESRGAN (Enhanced Super-Resolution Generative Adversarial Network), and SwinIR (Image Restoration Using Swin Transformer), the effectiveness of the particleboard super-resolution reconstruction model is verified using objective evaluation metrics including PSNR, SSIM, and LPIPS, demonstrating its ability to produce higher-quality images with more details and better visual characteristics. Finally, using the YOLOv8 model to compare defect detection rates between super-resolution images and low-resolution images, the mAP can reach 96.5%, which is 25.6% higher than the low-resolution image recognition rate.

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Applied Research on Prediction Methods of Properties of Particleboard Based on Data-Driven Methods

Cited by 3 publications

References 16 publications

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

Research on the Identification of Particleboard Surface Defects Based on Improved Capsule Network Model

Intelligent Detection Method for Surface Defects of Particleboard Based on Super-Resolution Reconstruction

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