Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence

Qin, Aoping; Guo, Lan-Ping; You, Zhichao; Gao, Hongli; Wu, Xiangdong; Xiang, Shoubing

doi:10.1007/s00170-020-05955-x

Cited by 17 publications

(9 citation statements)

References 40 publications

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“…However, the physical model is often very complicated. Another piece of research began with the popular image recognition idea in recent years [13], which, by capturing tool images, analyzes tool-wear states based on digital image processing [14]. However, the monitoring accuracy of this method is easily affected by light and physical monitoring angles [15].…”

Section: Introductionmentioning

confidence: 99%

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

2021

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In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

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Section: Introductionmentioning

confidence: 99%

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

2021

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“…However, for the machining process, the physical model is often complicated. Another study started from the idea of image recognition [13,14], which has become popular in recent years. The image of the tool is captured, and then, the tool wear state is analyzed based on digital image processing [15,16].…”

Section: Introductionmentioning

confidence: 99%

Tool condition monitoring in the milling process based on multisource pattern recognition model

Dai

Liang

Huang

et al. 2021

Int J Adv Manuf Technol

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In the milling process of metallic parts, appropriate tool conditions are essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states during milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a tool condition monitoring (TCM) method in the milling process based on multisource pattern recognition and state transfer paths. First, the improved K-means clustering method is used to generate multiple patterns of tool wear. Second, a multisource pattern recognition model framework is developed, and multiple observation windows and the pattern transfer path are considered in the multisource pattern recognition model. Finally, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

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“…But for the machining process, the physical model is often complicated. Another work started from the idea of image recognition [11,12], which is popular in recent years. The image of the tool is captured, and then analyzing the tool wear state based on digital image processing [13,14].…”

Section: Introductionmentioning

confidence: 99%

Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

Liang

Dai

Huang

et al. 2021

Preprint

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In the milling process of metallic parts, appropriate tool condition is essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states in milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a Tool condition monitoring (TCM) method in milling process based on multi-source pattern recognition and state transfer path. Firstly, improved K-Means clustering method is used to generate multiple patterns of tool wear. Secondly, a multi-source pattern recognition model framework is developed, and the multiple observation windows and the pattern transfer path are considered in multi-source pattern recognition model. Lastly, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

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Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence

Cited by 17 publications

References 40 publications

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Tool condition monitoring in the milling process based on multisource pattern recognition model

Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

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