Vision and sound fusion-based material removal rate monitoring for abrasive belt grinding using improved LightGBM algorithm

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

doi:10.1016/j.jmapro.2021.04.014

Cited by 33 publications

(10 citation statements)

References 29 publications

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“…Light Gradient Boosting Machine (LGB) was originally developed by researchers at Microsoft and Peking University to solve the efficiency and scalability problems of GBDT and XGBoost when applied to high-dimensional input features and large data volume problems (Wen, Xie, Wu, & Jiang, 2021). The core concepts of LGB are histogram algorithm, leaf growth strategy with depth limitations, support for category features, histogram feature optimization, multithreading optimization, and cache hit ratio optimization (N. N. Wang, Zhang, Ren, Pang, & Wang, 2021).The algorithm bins the original continuous eigenvalues and uses these bins to build the model, and the histogram greatly reduces the time consumption of the split point selection and improves the training and prediction efficiency of the model (Liu, Gao, & Hu, 2021). With a supervised training set…”

Section: Classification Modelmentioning

confidence: 99%

Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Zou

WU²,

Wang³

et al. 2023

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Section: Classification Modelmentioning

confidence: 99%

Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Zou

WU²,

Wang³

et al. 2023

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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“…CNN's are well known as a first applied method for tool wear prediction compared to other DL methods such as recurrent neural network (RNN), gated neural network (GNN) and long short-term memory (LSTM) [10,11,21,26]. CNN can have different architectures, and it depends based on the problem at hand.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…Spectrograms computed from the sound signals with DL method have been used to classify wear states of the abrasive belt [10]. A multi-sensor fusion method of vision and sound have been used along with a light gradient boosting machine (LightGBM) algorithm to monitor in-process grinding material removal rate (MRR) [21,22].…”

Section: Introductionmentioning

confidence: 99%

A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

et al. 2021

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This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. Degradation of the belt condition is a critical phenomenon that affects the workpiece quality during grinding. This work focuses on the identifation and the study of force and vibrational signals taken from sensors along an axis or combination of axes that carry important information of the contact conditions, i.e., belt wear. Three axes of the two sensors are aligned and labelled as X-axis (parallel to the direction of the tool during the abrasive process), Y-axis (perpendicular to the direction of the tool during the abrasive process) and Z-axis (parallel to the direction of the tool during the retract movement). The grinding process was performed using a customized abrasive belt grinder attached to a multi-axis robot on a mild-steel workpiece. The vibration and force signals along three axes (X, Y and Z) were acquired for four discrete sequential belt wear conditions: brand-new, 5-min cycle time, 15-min cycle time, and worn-out. The raw signals that correspond to the sensor measurement along the different axes were used to supervisedly train a 10-Layer CNN architecture to distinguish the belt wear states. Different possible combinations within the three axes of the sensors (X, Y, Z, XY, XZ, YZ and XYZ) were fed as inputs to the CNN model to sort the axis (or combination of axes) in the order of distinct representation of the belt wear state. The CNN classification results revealed that the combination of the XZ-axes and YZ-axes of the accelerometer sensor provides more accurate predictions than other combinations, indicating that the information from the Z-axis of the accelerometer is significant compared to the other two axes. In addition, the CNN accuracy of the XY-axes combination of dynamometer outperformed that of other combinations.

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“…For instance, Gao et al [20] proposed an MRR prediction model of robotic belt grinding based on the extreme gradient boosting (XGBoost) algorithm, taking sound signals as input. Wang et al [21] predicted MRR with the light gradient boosting machine (LightGBM) algorithm by integrating sound signals and grinding spark images, which are essentially indirect monitoring signals. Compared with indirect monitoring methods, direct monitoring focuses on signals that are insensitive to grinding parameters and ambient conditions, such as the features of abrasive belt images.…”

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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Vision and sound fusion-based material removal rate monitoring for abrasive belt grinding using improved LightGBM algorithm

Cited by 33 publications

References 29 publications

Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning

A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

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

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