Monitoring the dressing operation in the grinding process

Aguiar, Paulo Roberto de; Souza, André Gustavo Oliveira; Bianchi, Eduardo Carlos; Leite, Ricardo R.; Dotto, Fábio Romano Lofrano

doi:10.1504/ijmmm.2009.023110

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…In practice the grinding process is carried out with cutting parameters that are safe but not optimal. Another and no less important parameter to be controlled in grinding is dressing, which is the process of conditioning the grinding wheel surface in order to reshape the wheel when it has lost its original shape through wear [3][4].…”

Section: Monitoring and Process Control Of Machining Processesmentioning

confidence: 99%

Digital Signal Processing for Acoustic Emission

Aguiar¹,

Martins²,

Marchi³

et al. 2012

Data Acquisition Applications

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Section: Monitoring and Process Control Of Machining Processesmentioning

confidence: 99%

Digital Signal Processing for Acoustic Emission

Aguiar¹,

Martins²,

Marchi³

et al. 2012

Data Acquisition Applications

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“…Köning apud Aguiar [9] report that due to the wear that occurs at the tip of the dresser, adjustment of the process based solely on a d and f d is inadequate, since it does not consider the width of action, which varies with use. Therefore, the U d parameter is more suitable for the adjustment of the dressing process, because it takes into account the real width of action according to the wear at the tip, making the process more efficient.…”

Section: Monitoring Of the Dressing Processmentioning

confidence: 99%

“…According to Xue apud Aguiar [9], the number of dressing passes depends on the intensity of wear of the grinding wheel, the grain size, and the depth of wear to be removed. Normally two or three successive passes eliminate the effects of previous dressing and grinding.…”

Section: Monitoring Of the Dressing Processmentioning

confidence: 99%

“…For example, acoustic emission sensors have been utilized to detect the contact between the grinding wheel and the dresser (Oliveira et al [14]; Karpuschewski et al [4]), monitor the dressing depth (Inasaki [15]), monitor the dresser lead (Inasaki [15]; Webster et al [16]), diagnose dressing failures (Karpuschewski et al [4]; Konig and Meyen [17]; Kwak and Ha [18]; Lee et al [19]), model the dressing process (Linke [20] [9]), and a method without contact has been proposed (Young and Chen [21]). …”

Section: Monitoring Of the Dressing Processmentioning

confidence: 99%

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Dressing monitoring with a digital signal processor

Luzia¹,

Aguiar²,

Bianchi³

et al. 2010

Tribology and Design

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Grinding is a finishing process in machining operations, and the topology of the grinding tool is responsible for producing the desired result on the surface of the machined material. The tool topology is modeled in the dressing process and precision is therefore extremely important. This study presents a solution in the monitoring of the dressing process, using a digital signal processor (DSP) operating in real time to detect the optimal dressing moment. To confirm the monitoring efficiency by DSP, the results were compared with those of a data acquisition system (DAQ) and offline processing. The method employed here consisted of analyzing the acoustic emission and electrical power signal by applying the DPO and DPKS parameters. The analysis of the results allowed us to conclude that the application of the DPO and DPKS parameters can be substituted by processing of the mean acoustic emission signal, thus reducing the computational effort.

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“…The advantage of using AE to monitor the grinding process is that the frequency range of the AE signal is much higher than the frequencies of machine vibrations and environmental noises and does not interfere with the grinding operation [13]. Some studies that have used AE sensors to monitor the grinding process can be cited, such as, detection of the grinding wheel wear [14] and the thermal damage detection [15].…”

Section: Introductionmentioning

confidence: 99%

Correlation between surface roughness and AE signals in ceramic grinding based on spectral analysis

et al. 2018

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The study and monitoring of the workpiece surface roughness is one of the most important parameters of the grinding process. This paper proposes a method for analysing the surface condition of ground ceramic components by means of the acoustic emission (AE) signal analysis along with frequency domain techniques. Tests were performed using a surface-grinding machine equipped with a resin-bond diamond grinding wheel, where signals were collected at 2 MHz. Alumina workpieces were machined under six different depth of cut values, covering slight, medium and severe grinding conditions. Frequency content was studied in order to select bands closely related to the process conditions. An analysis of the root mean square values (RMS) of the signals was performed, seeking for a correlation with the surface roughness. Digital filters were applied to the raw signals. The RMS values filtered for two frequency bands presented a better fitting to the linear regression, which is highly desirable for setting a threshold to detect the workpiece surface conditions and implementing into a monitoring system. Results showed that the amplitude of the signals presented different characteristics in the frequency domain according to the workpiece surface condition. It was also observed a higher spectral activity in the severe grinding conditions.

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Monitoring the dressing operation in the grinding process

Cited by 10 publications

References 18 publications

Digital Signal Processing for Acoustic Emission

Digital Signal Processing for Acoustic Emission

Dressing monitoring with a digital signal processor

Correlation between surface roughness and AE signals in ceramic grinding based on spectral analysis

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