Tool wear monitoring system in belt grinding based on image-processing techniques

Oo, HtunHtun; Wang, Wei; Liu, Zhaoheng

doi:10.1007/s00170-020-06254-1

Cited by 34 publications

(11 citation statements)

References 34 publications

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“…Hence, the monitoring of the tool wear and the surface roughness is important. Signal processing has been used a lot for this purpose where several parameters including cutting force [2 -4], temperature [5 -9], current consumption [10], image processing [11], sound and vibration signals [1] [12 -20], have been exploited for tool wear surveillance.…”

Section: Introductionmentioning

confidence: 99%

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Bouhalais¹,

Nouioua²

2021

Int J Adv Manuf Technol

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Surface finish quality is becoming even more critical in modern manufacturing industry. In machining processes, surface roughness is directly linked to the cutting tool condition, a worn tool generally produces low quality surfaces, incurring additional costs in material and time. Therefore, tool wear monitoring is critical for a cost-effective production line. In this paper, the feasibility of a vibration-based approach for tool wear monitoring has been checked for turning process. AISI 1045 unalloyed carbon steel has been machined with TNMG carbide insert twenty-one times for a total of 27 minutes of machining, which was a necessary amount of time to exceed (300 µm) as a flank wear threshold. Vibration signals have been acquired during the operation and then processed in order to extract a correlation between the surface roughness, tool wear level and vibration comportment. First, Spectral kurtosis has been calculated for the twenty-one performed runs, the signals has then been decomposed with ICEEMDAN, the energy of the high frequency modes has been finally calculated. The Spectral Kurtosis has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it couldn't have been used for wear monitoring. On the other hand, the energy of optimal frequency ICEEMDAN modes has increased in direct proportion to the increase of surface roughness degradation and thus, to tool wear.

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Section: Introductionmentioning

confidence: 99%

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Bouhalais¹,

Nouioua²

2021

Int J Adv Manuf Technol

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“…The most primitive TCM method is that the operator estimates the process condition by the processing noise, chip shape, or cutting vibration differences. This method completely relies on the operator's own experience, which is inefficient and difficult to meet the requirements of complex processes [9,10]. In the manufacturing process, with the development of related fields in the past few decades, many TCM methods have been proposed and developed.…”

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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“…In this context Shahabi & M. Ratnam [5] tried to monitor the wear using High-resolution CCD camera, they found that the developed control system is influenced by several incontrollable factors such as the work environment, misalignment of cutting tool, presence of micro-dust particles, vibration and intensity variation of ambient light. Other authors used the intelligent image processing for tool wear monitoring [6], whereas, Wan-Hao Hsieh et al [7] used the vibration of the spindle for the monitoring system. Babouri et al [8] proposed the Wavelet Multi-Resolution Analysis to improve the sensitivity of the vibration scalar indicators for the identification of the wear state during the machining of X200Cr12 steel, they have proven that there is a possibility to associate the tool wear with the vibration generated when machining.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes

Nouioua¹,

Bouhalais²

2021

Preprint

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In machining processes various phenomena occur during cutting operation. These phenomena can disturb the production through the reduction of part quality and accuracy. Therefore, a mastery of this cutting phenomena is needed to define the machining parameters and take full advantage of manufacturing process. An easy way to classify these phenomena is by monitoring incontrollable parameters, such as generated temperature and vibration. The acquired vibration signals can provide information regarding tool life, cutting performances and workpiece defects. This paper evaluates the possibility of monitoring the tool life during the turning process of AISI 1045 steel using Laser Doppler Vibrometer (LDV), the surface roughness has been measured along with the tool-wear until reaching its limit value of 300µm. Furthermore, this paper also outlines the application of CEEMDAN technique to process the acquired signals for the monitoring processes. RMS and SCI indicators have been used to describe the wear progress, then, the artificial neural network has been adopted to achieve a real time wear monitoring. The obtained results qualified the SCI indicator and ANN for online monitoring.

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Tool wear monitoring system in belt grinding based on image-processing techniques

Cited by 34 publications

References 34 publications

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

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

Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes

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Tool wear monitoring system in belt grinding based on image-processing techniques

Cited by 34 publications

References 34 publications

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

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

Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator &amp; RMS of CEEMDAN modes

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Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes