Prediction of cutting temperature in the milling of wood-plastic composite using artificial neural network

Zhang, Feng; Wu, Zhanwen; Hu, Yong; Zhu, Zhi‐Biao; Guo, Xiaolei

doi:10.15376/biores.16.4.6993-7005

Cited by 4 publications

(2 citation statements)

References 21 publications

(21 reference statements)

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“…Dong [19] et al analyzed the use of neural networks to accurately monitor the wear of woodworking tools under different milling parameters during furniture production. On this basis, ZHANG [20] et al proposed a BP neural network model for predicting cutting temperature YOLOv7 as a target detection network has been successfully used for a variety of detection tasks [21][22][23].In this paper, YOLOv7 target detection network [24] was used to carry out deep learning on the appearance quality of edge banding plates. based on the anchor-based method, YOLOv7 upgraded some internal structural components, etc., to improve the algorithm architecture of detection speed and accuracy.…”

Section: Machine Vision and Yolov7 Algorithmmentioning

confidence: 99%

Research on Visualization Method of Edge Banding Appear-ance Quality Based on YOLOv7

LU,

Xiong,

2023

Preprint

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Edge banding plays a critical role in contemporary panel furniture production, as its quality significantly affects the aesthetic appeal of furniture. However, the current method of quality inspection and classification relies primarily on manual inspection and screening, which can be time-consuming, subjective, and lead to inaccurate and inefficient detection results. To address this issue, this study collected and processed pictures of defects in banded board edges, accurately labeling them into six categories, including open glue, shortage, chipping, uneven trimming, glue line, and banding indentation, using the LabelImg software. The YOLOv7 network architecture was then leveraged to train the network on the accurately processed surface defect dataset of banded edge panels. Subsequently, samples of panels produced through the manufacturing process were analyzed and tested, with the training outcomes effectively visualized and analyzed using the TensorFlow tool, resulting in a mean average accuracy (mAP) rate of 74.8% and an average detection rate of 57.63 FPS. The test findings revealed that the YOLOv7 target detection network successfully identified defects during the production of banded edge panel parts, and thus, improved the accuracy and efficiency of detecting banded edge panel defects. Therefore, this method is feasible and holds immense potential for practical application, as indicated by its capability to immensely enhance the accuracy and efficiency of detecting banded edge panel defects.

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Section: Machine Vision and Yolov7 Algorithmmentioning

confidence: 99%

Research on Visualization Method of Edge Banding Appear-ance Quality Based on YOLOv7

LU,

Xiong,

2023

Preprint

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“…Many intelligent models have been developed in the study of time series prediction problems, such as support vector regression, 6 fuzzy neural network, back propagation neural network, 9 and gray model. 10 However, using the above prediction model alone is usually not suitable for different conditions or circumstances.…”

Section: Introductionmentioning

confidence: 99%

Fast identification of machine tool spindle system temperature rise based on multi-model fusion and improved D-S evidence theory

Chen,

Fang,

Deng

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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Thermal equilibrium test is the key means to obtain the thermal characteristics of machine tools. In order to shorten the test period and reduce the research and development cost, a novel fast temperature rise identification method for machine tool spindle systems is proposed. The existing prediction identification methods ignore the limitation of the single prediction model, leading to large error fluctuations in different environments. In this study, various intelligent prediction models are combined with the improved D-S evidence theory to improve the accuracy and robustness of the prediction. Firstly, based on the virtual prediction, the evidence identification framework is established through the multiple evaluations of the data information in the evidence segment. Then, the weight allocation of each basic prediction model is carried out by the evidence combination theory. In this process, the evidence identification framework is reconstructed according to the improved strategy to avoid the high conflict problem in classical evidence theory. Finally, the fusion prediction of multiple models can be realized. The VM-850L machining center was selected as the research object for the thermal equilibrium test to evaluate the proposed method. The results show that the proposed multi-model fusion prediction method can accurately predict the temperature rise of selected points in a short time. Moreover, the prediction accuracy is significantly improved compared with the traditional single model. The proposed method has good universality and is expected to be popularized and applied more widely.

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WITHDRAWN: Modeling and simulation in wooden furniture manufacturing: technologies, scenarios, changes and challenges

Xu,

Xiong,

Zhang

et al. 2024

Computers & Industrial Engineering

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Prediction of cutting temperature in the milling of wood-plastic composite using artificial neural network

Cited by 4 publications

References 21 publications

Research on Visualization Method of Edge Banding Appear-ance Quality Based on YOLOv7

Research on Visualization Method of Edge Banding Appear-ance Quality Based on YOLOv7

Fast identification of machine tool spindle system temperature rise based on multi-model fusion and improved D-S evidence theory

WITHDRAWN: Modeling and simulation in wooden furniture manufacturing: technologies, scenarios, changes and challenges

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