Technical data-driven tool condition monitoring challenges for CNC milling: a review

Wong, Shi Yuen; Chuah, Joon Huang; Yap, Hwa Jen

doi:10.1007/s00170-020-05303-z

Cited by 76 publications

(39 citation statements)

References 115 publications

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“…PHM is a contemporary maintenance strategy that can help equipment sellers, integrator or operators to dynamically maintain their critical engineering assets [12,26].…”

Section: Prognostic and Health Management For Tcmmentioning

confidence: 99%

Tool Wear Conditions Monitoring Based on Kernel Smoothing Density and Deep Learning

Bazi¹,

Benkedjouh²,

Habbouche³

et al. 2021

Preprint

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Life prediction and health assessment of cutting tools is a challenging problem in industrial manufacturing, and plays an important role for Prognostic activities. The degradation of this tool can cause significant economic losses and risks for machine users. However, due to the random nature of system degradation and the sensitivity of various features characterizing cutter degradation, may vary considerably under different operation conditions. This paper propose a new data driven approach for tool condition monitoring, based on kernel smoothing density to model the random phenomena of degradation progression. The health assessment is insured using one directional neural network to extract useful information’s. Deep learning allows relating between extracted features and health indicator progression, and estimate remaining useful life. This methodology is evaluated using regression metrics and compared with recent frameworks, based on an experimental dataset from PHM Data Challenge 2010. The obtained results shows that the proposed approach can effectively predict the tool wear and obtain higher prediction accuracy than other comparison models.

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“…PHM is a contemporary maintenance strategy that can help equipment sellers, integrator or operators to dynamically maintain their critical engineering assets [12,26].…”

Section: Prognostic and Health Management For Tcmmentioning

confidence: 99%

Tool Wear Conditions Monitoring Based on Kernel Smoothing Density and Deep Learning

Bazi¹,

Benkedjouh²,

Habbouche³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Data are extremely important for this method, and because of the complementation of different signals, a huge amount of multisource data is often collected by various sensors to ensure the monitoring accuracy of tool wear conditions, but it easily causes data redundancy [27][28][29]. Although the accuracy can be guaranteed, the massive amount of data places a burden on the calculation cost for equipment [30]. Actually, for tool wear monitoring, we can meet the need for the milling process only by obtaining its approximate wear level rather than precise wear value in actual industrial manufacturing.…”

Section: Introductionmentioning

confidence: 99%

“…For the stage characteristic of the process, some studies consider the natural characteristic of the monitored object itself and divide its process into multiple stages or levels for monitoring [37][38][39]. However, for real-time abnormal state monitoring, the existing methods of abnormal state monitoring are almost based on constructing a fixed threshold [30]. This method has a large deficiency in recognizing intermediate abnormal states and sudden faults in milling processing, and it cannot recognize abnormal states in advance.…”

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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“…Although accuracy can be guaranteed, the massive data put a burden on the calculate cost for equipment. [29] Actually, for tool wear monitoring, we can meet the need for milling process only by obtaining its approximately wear level rather than precise wear value in the actual industrial manufacturing. Therefore, in order to solve the above problems, the pattern recognition has introduced into TCM eld [24].…”

Section: Introductionmentioning

confidence: 99%

“…However, in the aspect of real-time abnormal state monitoring, the existing methods of abnormal state monitoring almost based on constructing xed threshold. [29] This method has a large de ciency in recognizing intermediate abnormal states and sudden fault in milling processing, which cannot recognize abnormal states in advance. So are less able to advance to con gure and optimize the condition monitoring resource of process.…”

Section: Introductionmentioning

confidence: 99%

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

Liang

Dai

Huang

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Technical data-driven tool condition monitoring challenges for CNC milling: a review

Cited by 76 publications

References 115 publications

Tool Wear Conditions Monitoring Based on Kernel Smoothing Density and Deep Learning

Tool Wear Conditions Monitoring Based on Kernel Smoothing Density and Deep Learning

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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