In-process grinding monitoring through acoustic emission

Aguiar, Paulo Roberto de; Serni, Paulo José Amaral; Dotto, Fábio Romano Lofrano; Bianchi, Eduardo Carlos

doi:10.1590/s1678-58782006000100014

Cited by 16 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most used statistics in the analysis of the acoustic emission signal is the root mean square (RMS). The application of the RMS statistic in the identification of failures in the grinding and dressing processes has been extensively studied in the specific literature [47,48,49,50]. According to Webster et al [51], the best integration interval for calculating the RMS statistic in the monitoring of the grinding process is 1 ms.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, signal data might present a multimode pattern caused by frequent changes of the cutting parameters during each grinding cycle, and this situation makes chatter detection a troublesome task using traditional control charting methods. Despite of a body of literature devoted to the chatter detection problem, few automatic methods have been considered for actual industrial implementation. In this framework, multimode SPC techniques may represent a valuable alternative to common approaches.…”

Section: A Real Test Casementioning

confidence: 99%

A Comparison Study of Distribution‐Free Multivariate SPC Methods for Multimode Data

Grasso

Colosimo

Semeraro

et al. 2014

Quality & Reliability Eng

View full text Add to dashboard Cite

The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data.Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications.

show abstract

“…The statistics known as Constant False Alarm Rate (CFAR) and Mean Value Deviance (MVD) were employed successfully for detection of grinding burn (Wang et al 2001;Aguiar et al, 2006b). The equation 3 represents the CFAR and the equation 4 the MVD.…”

Section: Grinding Burn Monitoringmentioning

confidence: 99%

“…However, monitoring techniques still fails in certain situations where the phenomenon changes are not completely obtained by the employed signals. Several monitoring systems which use force or power and acoustic emission sensors have been assessed by researchers to control surface burn in grinding (Aguiar et al,2002;Aguiar et al, 1998;Kwak & Song, 2001;Wang et al, 2001;Kwak & Ha, 2004;Dotto et al, 2006;Aguiar et al, 2006a;Aguiar et al, 2006b). However, those techniques still need improvements where the phenomenon variations are not entirely acquired by the signals used.…”

Section: Introductionmentioning

confidence: 99%