Novel monitoring method for material removal rate considering quantitative wear of abrasive belts based on LightGBM learning algorithm

Wang, Nina; Zhang, Guangpeng; Pang, Wanjing; Ren, Lijuan; Wang, Yupeng

doi:10.1007/s00170-021-06988-6

Cited by 26 publications

(9 citation statements)

References 20 publications

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“…Xiong [12] et al used the XGBoost model to predict the cement specific surface area , and their results had smaller errors compared to GBM and RF. Wang [13] et al used the LightGBM model to predict the wear of sand belt, and their results showed that LightGBM not only excelled in prediction accuracy, but also its computational speed was relatively fast. Although the traditional machine learning approach is widely used in data-driven prediction models, it is only applicable to small sample data modeling and performs well in semi-supervised learning to effectively fill in missing data.…”

Section: Related Workmentioning

confidence: 99%

A prediction model for cement specific surface area based on Dual Temporal Extraction Network

Sun

Guo

Liu

et al. 2022

Preprint

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The accuracy of the predicted value of specific surface area plays a significant guiding function in the production and scheduling of cement, as it is a major index impacting the quality of cement. However, due to data characteristics in the cement grinding process, such as time delay, strong coupling and nonlinearity, and the traditional prediction model being a static small sample feature acquisition method based on time correlation, problems such as poor data feature representation and low accuracy exist. In order to achieve an accurate prediction of cement specific surface area, we propose a deep learning prediction model based on Dual Temporal Extraction Network (DTENet). This network is divided into two parts: encoder and decoder. The network uses a dual temporal feature extraction mechanism in the encoder part. Different from the LSTM network which only focuses on the correlation information of a single time step, we realize the mechanism of temporal feature extraction in the short term and long term respectively by constructing a two-stage sliding window to send the data into different temporal feature extraction networks. This network can greatly improve the prediction accuracy for cement specific surface area data with large time lag, redundancy and variable working conditions. In the decoder part, we use the channel attention approach to enhance the spatial information extraction capability of the model. The result of the experiments shows that our model has superior accuracy in cement specific surface area prediction when compared to LSTM, XGBoost and ARIMA models.

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Section: Related Workmentioning

confidence: 99%

A prediction model for cement specific surface area based on Dual Temporal Extraction Network

Sun

Guo

Liu

et al. 2022

Preprint

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“…et al [ 19 ] studied spark images generated during the abrasive belt blasting process, extracted characteristics including spark image area and illumination, and studied the relationship between spark field characteristics and the material removal rate. Wang Nina et al [ 20 , 21 ] studied the characteristics of sound and spark images during the abrasive belt grinding process, fused the signals of images and sound, and established a material removal rate model.…”

Section: Introductionmentioning

confidence: 99%

A Study of an Online Tracking System for Spark Images of Abrasive Belt-Polishing Workpieces

Huang

Zhang

2023

Sensors

Self Cite

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During the manual grinding of blades, the workers can estimate the material removal rate based on their experiences from observing the characteristics of the grinding sparks, leading to low grinding accuracy and low efficiency and affecting the processing quality of the blades. As an alternative to the recognition of spark images by the human eye, we used the deep learning algorithm YOLO5 to perform target detection on spark images and obtain spark image regions. First the spark images generated during one turbine blade-grinding process were collected, and some of the images were selected as training samples, with the remaining images used as test samples, which were labelled with LabelImg. Afterwards, the selected images were trained with YOLO5 to obtain an optimisation model. In the end, the trained optimisation model was used to predict the images of the test set. The proposed method was able to detect spark image regions quickly and accurately, with an average accuracy of 0.995. YOLO4 was also used to train and predict spark images, and the two methods were compared. Our findings show that YOLO5 is faster and more accurate than the YOLO4 target detection algorithm and can replace manual observation, laying a specific foundation for the automatic segmentation of spark images and the study of the relationship between the material removal rate and spark images at a later stage, which has some practical value.

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“…The algorithm used is another key factor affecting the accuracy of MRR prediction. With the development of computer science and technology, various machine learning regression algorithms have been proposed and used in MRR prediction, including SVM [15], CNN [22], XGBoost [20], LightGBM [23], categorical boosting (CatBoost), etc. In the gradient boosting algorithm family, XGBoost, LightGBM, and CatBoost are the most popular and effective algorithms.…”

Section: Introductionmentioning

confidence: 99%

In-process belt-image-based material removal rate monitoring for abrasive belt grinding using CatBoost algorithm

Wang

Huang

Ren

et al. 2022

Int J Adv Manuf Technol

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A reliable material removal rate (MRR) prediction method significantly optimizes the grinding surface quality and improves the processing efficiency for robotic abrasive belt grinding. Using worn-belt image features to predict MRR is a direct and reliable method; however, this method is rarely reported at present. This paper proposes an MRR prediction method for Inconel 718 grinding based on the abrasive belt image analysis and categorical boosting (CatBoost) algorithm. During belt grinding, four wear types of abrasive belts, namely fracture, adhesion, rubbing wear, and fall-off, are identified and analyzed. Under various grinding parameters, the experimental MRR rapidly decreases at first, then in a gradual manner. For an effective evaluation of belt wear severity, cutting grain area ratio, color moments, and texture features are extracted from belt images. MRR and abrasive belt image features are strongly correlated after normalization. All image features are taken into account for MRR prediction model training. Verification experiments indicate that the predicted data is in good agreement with the experimental data. The maximum absolute error, mean absolute error, root mean square error, and determination coefficient of the MRR prediction model are 0.17 μm, 0.4 μm, 0.2 μm, and 99.42%, respectively, which are superior to those of other popular machine learning algorithms. In this study, we present a comprehensive understanding of the relationship between MRR and abrasive belt characteristics, as well as demonstrate the feasibility of accurately predicting MRR using the CatBoost algorithm.

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Novel monitoring method for material removal rate considering quantitative wear of abrasive belts based on LightGBM learning algorithm

Cited by 26 publications

References 20 publications

A prediction model for cement specific surface area based on Dual Temporal Extraction Network

A prediction model for cement specific surface area based on Dual Temporal Extraction Network

A Study of an Online Tracking System for Spark Images of Abrasive Belt-Polishing Workpieces

In-process belt-image-based material removal rate monitoring for abrasive belt grinding using CatBoost algorithm

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