Characterization of Grinding Wheel Condition by Acoustic Emission Signals

Lin, Yu-Kun; Wu, Bing-Fei; Chen, Chia-Meng

doi:10.1109/icsse.2018.8520249

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…According to Lin et al [57], the ratio of power (ROP) is a statistical method for analyzing the ratio of a given frequency band with the total power spectrum. The ROP statistic can be defined as ROP= ∑k=n1n2|xk|2∑k=0N−1|xk|2 where N is the block data size; n1 and n2 define the frequency range for the analysis; and xk is the k th discrete Fourier transform [58].…”

Section: Signal Processingmentioning

confidence: 99%

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Viera

Aguiar

Oliveira

et al. 2019

Sensors

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Innovative monitoring systems based on sensor signals have emerged in recent years in view of their potential for diagnosing machining process conditions. In this context, preliminary applications of fast-response and low-cost piezoelectric diaphragms (PZT) have recently emerged in the grinding monitoring field. However, there is a lack of application regarding the grinding of ceramic materials. Thus, this work presents an analysis of the feasibility of using the acoustic emission signals obtained through the PZT diaphragm, together with digital signal processing in the time–frequency domain, in the monitoring of the surface quality of ceramic components during the surface grinding process. For comparative purpose, an acoustic emission (AE) sensor, commonly used in industry, was used as a baseline. The results obtained by the PZT diaphragm were similar to the results obtained using the AE sensor. The time–frequency analysis allowed to identify irregularities throughout the monitored process.

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Section: Signal Processingmentioning

confidence: 99%

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Viera

Aguiar

Oliveira

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The wheel surface is dulled when cutting chips fill the surface pores or the grits are worn down, thus losing the ability to grind [10]. Next, the bonder grade of the abrasive can be distinguished by the AE signal; in general, for diamond wheels the bond grade is distinguished by calculating the wear ratio after grinding [11], [12], [13]. In addition, the AE signal is often used to monitor workpiece conditions by identifying defects such as workpiece surface burning, chatter vibration marks, and roughness [14], [15], [16].…”

Section: Grinding Process Monitoringmentioning

confidence: 99%

Time-Series Forecast-Based Endpoint Thickness Compensation for Thinning of Sapphire Wafer

Lin

2023

IEEE Access

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The grinder consults measurement modules to control the endpoint thickness of the sapphire wafer thinning process, but these modules have shortcomings. We propose a forecast-based endpoint thickness and online error compensation approach for grinding hard, brittle material. We leverage forecasts to improve grinding efficiency, and address the shortcomings of conventional probe gauges, which can be used only when grinding is paused, as well as those of contact gauges, which require correction from time to time due to wear. We construct a multi-signal time-series forecast model and compare the prediction performance using features from various signals. We implement the forecast model in a vertical grinder for actual grinding to evaluate the effectiveness of the proposed approach. The results show that online error compensation reduces contact times for the contact gauge and maintains precise thickness, greatly facilitating the wafer thinning process. The proposed measurement approach applies not only to wafer thinning but can also be extended to other grinding processes.INDEX TERMS Wafer thinning process, acoustic emission (AE), tri-axial vibration, wafer endpoint thickness, time-series forecasting.

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“…Figure 6 presents the power spectra of the AE signals for the five grinding wheels, where feature bands are observed in the segments of (ii) 200 to 500 kHz and (i) 550 to 950 kHz. Wheel of different grades have different power spectrum densities [34]. The ROP, as expressed by (2), is a statistical method used to estimate the band power density ratios in the power spectrum.…”

Section: Characteristic Signals Analysis and Features Selection Amentioning

confidence: 99%

Machine Learning-Based Wheel Monitoring for Sapphire Wafers

Lin

2021

IEEE Access

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The thinning of sapphire wafers is a key process that affects the quality of optoelectronic devices. In the grinding process for sapphire, a hard and brittle material, the grade and surface conditions (wearing and chip loading) of the grinding wheel are the key to continuous processing and reduction of defects. In industry, a common approach is to assume the possible causes of defects by observing the spindle current during the grinding process and the finished product after processing. Thus far, there has been no effective method to quantify the grade and no real-time monitoring technology for grinding wheels. This research proposes a wheel monitoring system that utilizes acoustic emission (AE) signals and radial/axial vibration signals to extract characteristic parameters via popular machine learning classification algorithms (k-NN, ANN, and SVM) in order to identify the signal and characteristic parameters of the wheel during the grinding process. The experimental results show that the AE signal identification accuracy of the grade is excellent, at 99%. The vibration signal of the wheel surface condition during the grinding process is significant as well, yielding an identification accuracy of 91%. The extracted signal characteristics in this research not only quantify the state of the grinding wheel, but also facilitate a wheel monitoring system based on the identification technology. The proposed method for grinding wheel processing status monitoring can be used to establish an automated intelligent grinding system. INDEX TERMS Acoustic emission, feature extraction, grinding wheel grade, machine learning, radial and axial vibration, wheel loading.

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Characterization of Grinding Wheel Condition by Acoustic Emission Signals

Cited by 11 publications

References 26 publications

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Time-Series Forecast-Based Endpoint Thickness Compensation for Thinning of Sapphire Wafer

Machine Learning-Based Wheel Monitoring for Sapphire Wafers

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