Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Li, Yiting; Xie, Qingsheng; Huang, Haisong; Chen, Qipeng

doi:10.3390/sym11060809

Cited by 12 publications

(7 citation statements)

References 17 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this part, eight state-of-the-art models, including two machine learning models, random forests (RF), and support vector regression (SVR) [34] and six deep learning model, deep convolution neural network (DCNN) [35], residual dense network (RDN) [36], multiscale convolutional neural network (MSCNN) [37], convolutional long-short-term memory network (CLSTM) [24], deep belief networks (DBN) [38], and multiscale convolutional attention network (MSCAN) [39] are utilized to estimate the RUL for the comparison analysis. For the RF and SVR, features listed in [34] are extracted from all the monitoring data.…”

Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

Liu

Jiang

et al. 2023

Shock and Vibration

View full text Add to dashboard Cite

Remaining useful life prediction of a milling tool is one of the determinants in making scientific maintenance decision for the CNC machine tool. Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL prediction and are extensively used. Multiscale CNN, a typical deep learning model in RUL prediction, has a large number of parameters because of its parallel convolutional pathways, resulting in high computing cost. Besides, the MSCNN ignores various influences of different scales of degradation features on RUL prediction accuracy. To address the issue, a pyramid CNN (PCNN) is proposed for RUL prediction of the milling tool in this paper. Group convolution is used to replace parallel convolutional pathways to extract multiscale features without additional large number of parameters. And the channel attention with soft assignment is used to select the key degradation features, considering different sensors and scales. The milling tool wear experiments show that the score value of the proposed method achieved 51.248 ± 1.712 and the RMSE achieved 19.051 ± 0.804, confirming better performance of the proposed method compared with the traditional MSCNN and other deep learning methods. Besides, the number of parameters of the proposed method is reduced by 62.6% and 54.8% compared with the MSCNN with self-attention and the MSCNN methods, confirming its lower computing cost.

show abstract

Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 99%

“…Because of the remarkable ability of extracting degradation features from monitoring data, CNN-based RUL prediction methods become a research hotspot, especially the multiscale CNN (MSCNN) [31][32][33][34][35][36][37][38][39]. Te architecture of traditional MSCNN with self-attention is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

Liu

Jiang

et al. 2023

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Audible sound signals were used as a sensing approach to detect the cutting tool wear and failure during end milling using the Support Vector Machine (SVM) model. Li, et al [13] presented a deep convolutional neural network by combining the concepts of a recursive residual network and a dense network to perform tool wear monitoring and prediction. Chen, et al [14] integrated a Convolutional Neural Network (CNN) and a bidirectional Long Short-term Memory (BiLSTM) network to monitor the tool wear state during milling.…”

Section: Studies Regarding Tool Wear Predictionmentioning

confidence: 99%

Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process

et al. 2021

View full text Add to dashboard Cite

Accurate monitoring and prediction of tool wear conditions have an important influence on the cutting performance, thereby improving the machining precision of the workpiece and reducing the production cost. However, traditional methods cannot easily achieve exact supervision in real time because of the complexity and time-varying nature of the cutting process. A method based on Digital Twin (DT), which establish a symmetrical virtual tool system matching exactly the actual tool system, is presented herein to realize high precision in monitoring and predicting tool wear. Firstly, the framework of the cutting tool system DT is designed, and the components and operations rationale of the framework are detailed. Secondly, the key enabling technologies of the framework are elaborated. In terms of the cutting mechanism, a virtual cutting tool model is built to simulate the cutting process. The modifications and data fusion of the model are carried out to keep the symmetry between physical and virtual systems. Tool wear classification and prediction are presented based on the hybrid-driven method. With the technologies, the physical–virtual symmetry of the DT model is achieved to mapping the real-time status of tool wear accurately. Finally, a case study of the turning process is presented to verify the feasibility of the framework.

show abstract

“…However, these traditional models still have to face some problems [26][27][28][29]. On the one hand, feature extraction and selection are essential but labor intensive, and require both sufficient domain expertise and rich experience [1,28,30].…”

Section: Introductionmentioning

confidence: 99%

Multi-frequency-band deep CNN model for tool wear prediction

Duan

Duan²,

Zhou

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

A reliable data-driven tool condition monitoring system is more and more promising for cutting down on machine downtime and economic losses. However, traditional methods are not able to address machining big data because of low model generalizability and laborious feature extraction by hand. In this paper, a novel deep learning model, named multi-frequency-band deep convolution neural network (MFB-DCNN), is proposed to handle machining big data and to monitor tool condition. First, samples are enlarged and a three-layer wavelet package decomposition is applied to obtain wavelet coefficients in different frequency bands. Then, the multi-frequency-band feature extraction structure based on a deep convolution neural network structure is introduced and utilized for sensitive feature extraction from these coefficients. The extracted features are fed into full connection layers to predict tool wear conditions. After this, milling experiments are conducted for signal acquisition and model construction. A series of hyperparameter selection experiments is designed for optimization of the proposed MFB-DCNN model. Finally, the prediction performance of typical models is evaluated and compared with that of the proposed model. The results show that the proposed model has outstanding generalizability and higher prediction performance, and a well designed structure can remedy the absence of complicated feature engineering.

show abstract

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Cited by 12 publications

References 17 publications

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process

Multi-frequency-band deep CNN model for tool wear prediction

Contact Info

Product

Resources

About