Chatter Vibration Monitoring in the Surface Grinding Process through Digital Signal Processing of Acceleration Signal

Alexandre, Felipe Aparecido; Lopes, Wenderson Nascimento; Ferreira, Fábio Isaac; Dotto, Fábio Romano Lofrano; Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos

doi:10.3390/ecsa-4-04927

Cited by 10 publications

(3 citation statements)

References 10 publications

(22 reference statements)

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“…For example, whenever grinding hard materials (difficult to ECS Transactions, 107 (1) 1327-1354 (2022) machine), it will tend to form excitation vibration by increasing the cutting forces. This causes higher chatter vibrations on the machine tool structure (45,46).…”

Section: Measuring the Vibration Of The Grinding Machinementioning

confidence: 99%

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

Anand¹,

Sivakumar²,

Prabadevi³

2022

ECS Trans.

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Recently, the development of the grinding process is improved by including automatic sensors, actuators, control systems, artificial intelligence, and industrial internet of things in the grinding machine. Already existing contact type techniques are replaced with the automatic sensor system to incorporate artificial intelligence in the normal grinding process. Therefore, optical and laser technology is emerging as a smart device to observe the surface topography of the grinding wheel, surface finish of the ground surface, and automatic dressing process. Moreover, artificial intelligence consists of machine learning, which teaches the grinding machine based on the existing data available to improve the quality and the production rate of the grinding process. This can be achieved by controlling the process parameter, monitoring the machine health, and attaining optimum condition. The present review paper addresses the existing problems associated with the contact type and non-contact type measurement. The study also emphasizes the importance of machine learning algorithms to predict the failure of the grinding wheel and surface roughness of workpiece material in the grinding process.

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Section: Measuring the Vibration Of The Grinding Machinementioning

confidence: 99%

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

Anand¹,

Sivakumar²,

Prabadevi³

2022

ECS Trans.

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“…With regard to the signal analysis, the frequency domain is generally used in identifying events that are difficult to observe in the time domain. The most commonly used frequency domain techniques are the fast Fourier transform (FFT) [54], the short-time Fourier transform (STFT) [55] and the wavelet transform (WT) [56]. The FFT is an algorithm developed to improve the computational efficiency of the discrete Fourier transform (DFT), being a popular method used in spectral analysis and digital signal processing.…”

Section: Rms and Counts In Ae Signal Processingmentioning

confidence: 99%

A Novel Ultrasound Technique Based on Piezoelectric Diaphragms Applied to Material Removal Monitoring in the Grinding Process

Alexandre

Aguiar

Götz

et al. 2019

Sensors

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The interest of the scientific community for ultrasound techniques has increased in recent years due to its wide range of applications. A continuous effort of researchers and industries has been made in order to improve and increase the applicability of non-destructive evaluations (NDE). In this context, the monitoring of manufacturing processes, such as the grinding process, arises. This work proposes a novel technique of ultrasound monitoring (chirp-through-transmission) through low-cost piezoelectric diaphragms and digital signal processing. The proposed technique was applied to the monitoring of material removal during the grinding process. The technique is based on changes in ultrasonic waves when propagated through the material under study, with the difference that this technique does not use traditional parameters of ultrasonic techniques but digital signal processing (RMS and Counts). Furthermore, the novelty of the proposed technique is also the use of low-cost piezoelectric diaphragms in the emission and reception of ultrasonic waves, enabling the implementation of a low-cost monitoring system. The results show that the monitoring technique proposed in this work, when used in conjunction with the frequency band selection, is sensitive to the material removal in the grinding process and therefore presents an advance for monitoring the grinding processes.

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“…Therefore, timely and accurate rolling bearing vibration monitoring and fault diagnosis existing standards only regulate the vibration characteristic amount of the main bearing (rolling bearing) of the generator set and do not achieve comprehensive vibration monitoring and fault diagnosis of the rolling bearing of the generator set [5][6]. With the advantages of wide applicability, various signal processing methods and reliable diagnosis results, vibration monitoring has become one of the most used and effective methods in rolling bearing condition detection and fault diagnosis [7][8]. Rolling bearings are subject to internal and external excitation during operation.…”

Section: Introductionmentioning

confidence: 99%

Research on fault feature extraction and early warning of rolling bearing vibration signal of generator set

Ma,

Si,

Yan

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The study of fault feature extraction and early warning of rolling bearing vibration signal of generator sets is beneficial for the timely diagnosis of bearing faults, thus improving the service life of generators. In this paper, a combined EEMD-GRU-MC prediction method is adopted to predict the model based on GRU through the data decomposition of EEMD, and the predicted model residuals are corrected using MC. The analysis and diagnosis of the algorithmic model are used to determine the fault characteristics of the generator’s vibration signals for diagnosis, and the analysis and diagnosis of the characteristics are verified using experiments with publicly available data sets from the Bearing Data Center at the Paderborn University School of Mechanical Engineering in Paderborn, Germany. Diagnosis can be performed with an accuracy of 99.6% under condition load K 0 and 99.9% close to 100% under condition load K 3 . The accuracy is the highest at this point, while the diagnostic accuracy at conditional load K 1 is only 99.8%, the lowest at this point. However, the accuracy under condition load K 2 is also 99.5%. Therefore, the diagnostic accuracy with EEMD-GRU-MC is around 99%. In this study, the EEMD-GRU-MC model can be used to extract the fault characteristics and early warning of the rolling bearing vibration signal of generator sets.

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Chatter Vibration Monitoring in the Surface Grinding Process through Digital Signal Processing of Acceleration Signal

Cited by 10 publications

References 10 publications

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

A Novel Ultrasound Technique Based on Piezoelectric Diaphragms Applied to Material Removal Monitoring in the Grinding Process

Research on fault feature extraction and early warning of rolling bearing vibration signal of generator set

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