Chatter detection algorithm based on machine vision

Szydłowski, Michał; Powałka, B.

doi:10.1007/s00170-011-3816-2

Cited by 24 publications

(5 citation statements)

References 13 publications

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“…On the other hand, the transformation of sensor data into an image has facilitated the use of DL techniques in TCM, as these methods have been extensively studied for image processing tasks. Image processing has been applied to a T-F representation in [136,152,159,196] and to pictures of the surface roughness using vision-based techniques in [135,161,188,190,192]. Analysis of cutting information as an image, instead of as a signal, has been successfully applied for other machining tasks.…”

Section: Additional Analysis Approachesmentioning

confidence: 99%

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Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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Section: Additional Analysis Approachesmentioning

confidence: 99%

“…* Two or more signal processing methods combined[91,136,152,159,165,179,192,196,220,348] Other methods[72,73,84,95,101,135,137,147,148,150,155,157,161,164,168,173,190,227,258,259] …”

mentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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“…In the machining process, the workpiece is machined by using different cutting tools, and during machining, vibrations are generated in the machine called chatter. Szydłowski and Powałka [18] worked on an experiment to determine the chatter using a machine vision algorithm. An image was captured for the machined surface and analyzed by a developed algorithm where ridges or valleys are determined based on which chatter is determined.…”

Section: Introductionmentioning

confidence: 99%

A novel IoT based machine vision system for on-machine diameter measurement and optimization

Zende,

Pawade

2023

Eng. Res. Express

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The use of machine vision systems has been made user-friendly, cost-effective, and flawless by the rapid development in the fields of advanced electro-optical and camera systems, electronics systems, and software systems. One such application of machine vision systems in the field of manufacturing is the inspection of a semi-finished or finished component during an ongoing manufacturing process. In this study, the camera's intrinsic and extrinsic parameters were maintained constant, while red, green, and blue light sources were employed to measure the component diameter using pixel analysis. A novel approach was used in an IoT-based machine vision system where, on the same image, the smartphone camera was calibrated and the image diameter of the component under study was measured, which was found to be quite accurate. Four different cases were used in the error analysis of image diameter, in which experimental results show that under blue light, the percentage pixel error span is the largest at 0.2624 % followed by 0.1422 % under green light and 0.0903 % under red light. Further, the use of four different cases was followed by the 'Weighted Sum Model', which optimized the percentage errors in estimated actual diameter precisely and effectively, where outcome results showed that the approximate percentage errors were determined within 0.8 % for blue light, 0.5 % for a red light, and 0.1 % for a green light. The proposed IoT-based machine vision system was found to be robust and effective for on-machine measurement.

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“…Li et al [14] utilized image processing and pattern recognition techniques to accurately identify and predict the processing status of thin-walled parts through milling surface images. Szydlowski et al [15] proposed using the local gradient method in image processing to identify chatter, but due to the complexity of the algorithm, the on-time monitoring performance is imperfect. Khalifa et al [16] used image analysis to describe the roughness of the machined surface and establish a correlation with machining chatter, so the detection of chatter is obtained.…”

Section: Introductionmentioning

confidence: 99%

Research on milling chatter monitoring and suppression based on IWPEE and VASS dual indicators

Li,

Huang,

Zhao

et al. 2023

Int J Adv Manuf Technol

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Chatter is harmful for cutting processing, which can cause the surface quality of the workpiece to decline, violent tool wear, even broken tools and workpiece scrap. In order to achieve real-time monitoring and suppression of chatter during the machining process, a milling machining is employed as the research object in this study, and an online monitoring and suppression system for chatter is proposed. Firstly, an improved wavelet packet energy entropy(IWPEE) algorithm is proposed, the recognition accuracy and robustness is improved by this algorithm while the computational complexity is reduced. Secondly, in order to ensure the robustness of the monitoring system in practical application, a variance algorithm for the milling signal sampling is synchronized with the spindle speed(VASS) is proposed by analyzing milling cutter motion through Poincare section. In addition, the position of the spindle rotation is indirectly obtained through mathematical calculations, the signal sampling is ensured synchronized with the spindle rotation period, and the sensors are not necessary so the generalization besides economy is improved. Furthermore, VASS overcomes the defects of IPWEE which is vulnerable to high-frequency noise interference, thereby improving the reliability of chatter monitoring. Then, a dynamic modeling of the cutting system is conducted, and a chatter suppression method based on speed iteration is obtained. Finally, the effectiveness of the proposed chatter monitoring and suppression system is verified through milling experiments.

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Chatter detection algorithm based on machine vision

Cited by 24 publications

References 13 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

A novel IoT based machine vision system for on-machine diameter measurement and optimization

Research on milling chatter monitoring and suppression based on IWPEE and VASS dual indicators

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