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

Martin Antonio Aulestia Viera ,R B Silva ,Paulo Roberto De Aguiar ,Felipe Aparecido Alexandre ,Eduardo Carlos Bianchi

doi:10.1051/matecconf/201824903003

Martin Antonio Aulestia Viera , R B Silva + Show 3 more

Open Access

https://doi.org/10.1051/matecconf/201824903003

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Abstract

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.

Highlights

The grinding process is located in the final stage of the machining process and is performed by a cutting tool known as grinding wheel
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
The root mean square values (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

Summary

Introduction

The grinding process is located in the final stage of the machining process and is performed by a cutting tool known as grinding wheel. It is known that the performance of the grinding process basically depends upon the operator’s skills as well as the dressing condition of the grinding wheel. Grinding is considered the most efficient technique in machining brittle materials [3]. Some studies that deal with the advanced ceramics grinding process can be cited, such as, the vibration signal application for diagnosing the grinding parameters and the workpiece surface [5] and the modelling of surface roughness [6]. The efficient grinding of advanced ceramics requires a careful selection of the machining parameters in order to maximize the material removal rate without compromising the quality of the workpiece surface [7]

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