A multi-sensor integrated smart tool holder for cutting process monitoring

Xie, Zhengyou; Lü, Yong; Xin-long, Chen

doi:10.1007/s00170-020-05905-7

Cited by 26 publications

(17 citation statements)

References 20 publications

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“…In machining processes, a sensor node can be mounted on the tool holder, as shown in Figure 4 , close to the source of the signal at the cutting zone to provide a better quality of the detected signal than the conventional sensor mounting approach on the machine spindle or worktable [ 78 ]. This requires the sensor node to be able to operate in a harsh and confined space close to the signal source with minimum intervention in the workspace for successful integration into TCM systems [ 79 ]. It should also provide the required high-resolution data sampling for accurate and reliable tool state health decisions.…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

“…It should also provide the required high-resolution data sampling for accurate and reliable tool state health decisions. Additionally, such a system should have the potential to integrate multiple sensors to increase the TCM system accuracy [ 79 ]. The universal sensor node concept is still in the proof-of-concept stage and has been utilized in very few studies to estimate either the tool wear or the surface roughness of the machined part [ 80 , 81 ].…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

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Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Xie, Z. et al use capacitance sensors and accelerometers to make a smart tool holder for cutting process monitoring. The maximum sensitivity of the smart tool holder is 2353 aF/N [ 10 ]. The actual measurement of milling force is carried out using the smart tool holder, and the effect is relatively good.…”

Section: Introductionmentioning

confidence: 99%

Design and Manufacturing of a High-Sensitivity Cutting Force Sensor Based on AlSiCO Ceramic

Gong

Zhao

et al. 2021

Micromachines

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On-line cutting force measurement is an effective way to monitor processing quality, improve processing accuracy, and protect the tool. In high-speed and ultra-precision machining, status monitoring is particularly necessary to ensure machining accuracy. However, the cutting force is very small in high speed and ultra-precision machining. Therefore, high-sensitivity cutting force sensors are needed. Current commercial cutting force sensors have defects such as large volume, low compatibility, and high price. In particular, the sensitivity of cutting force sensor needs to be improved for high-speed and ultra-precision machining status monitoring. This paper provides a possible solution by embedding the sensor in the tool and selecting sensitive materials with high piezoresistive coefficient. In this paper, the structural design of the sensor and the fabrication of the sensitive material SiAlCO ceramic are carried out, and then the sensor is packaged and tested. The test results show that the cutting force sensor’s sensitivity was as high as 219.38 mV/N, which is a feasible way to improve cutting force sensor’s compatibility and sensitivity.

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“…Now, more popular studies are conducted based on signal processing and data mining [17,18]. On the one hand, this approach benefits from previous research, and scholars have studied a variety of signals that can reflect condition changes in the tool wear process [19][20][21]. On the other hand, the tool wear state recognition method based on signal data has been gradually proposed [10,[22][23][24][25].…”

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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A multi-sensor integrated smart tool holder for cutting process monitoring

Cited by 26 publications

References 20 publications

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Design and Manufacturing of a High-Sensitivity Cutting Force Sensor Based on AlSiCO Ceramic

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

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