Spectra Measurements Using Piezoelectric Diaphragms to Detect Burn in Grinding Process

• Published in: IEEE transactions on instrumentation and measurement 2017-11, Vol.66 (11), p.3052-3063
• Main Authors: Ribeiro, Danilo M. S., Aguiar, Paulo R., Fabiano, Luiz F. G., D'Addona, Doriana M., Guimaraes Baptista, Fabricio, Bianchi, Eduardo C.
• Format: Article
• Language: English
• Subjects: Acoustic emission; Acoustic emission (AE); Automotive engineering; Automotive parts; Boron steels; Burn-in; condition monitoring; Cubic boron nitride; Diaphragms; Digital filters; Feature extraction; Grinding machines; Grinding wheels; Lead zirconate titanates; Machinery condition monitoring; Machining; manufacturing processes; Mean square values; Medium carbon steels; Monitoring; piezoelectric transducer; Piezoelectricity; Regression analysis; Rough surfaces; Sensors; Structural steels; Surface grinding machines; Surface roughness; Surface treatment; Wheels; Workpieces
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2017.2731038

Researchers have evaluated a great number of monitoring techniques in order to control the surface condition of ground parts. Piezoelectric diaphragms of lead zirconate titanate are used in many fields, but these sensors are not common in the monitoring of the machining processes. This paper proposes a method for monitoring the workpiece surface condition (normal grinding and burn) by using a piezoelectric diaphragm and feature extraction techniques. A comparison is made with a conventional acoustic emission sensor, which is a traditional sensor in the monitoring of the machining processes. Grinding tests were performed in a surface-grinding machine with Society of Automotive Engineers (SAE) 1045 steel and cubic boron nitride (CBN) grinding wheel, where the signals were collected at 2 MHz. The workpieces were thoroughly analyzed through visual inspection, surface roughness and hardness measurements, and metallographic analyses. Study on the frequency content of both signals was carried out in order to select bands closely related to the workpiece surface condition. Digital filters were applied to the raw signals and features were extracted and analyzed. The root mean square values filtered in the selected bands for both sensors presented a better fitting to the linear regression, which is highly desirable for setting a threshold to detect burn and implementing into a monitoring system. Also, the basic damage index results show an excellent behavior for grinding burn monitoring for both sensors. The method was verified by using a different grinding wheel, which clearly shows its effectiveness and demonstrates the potential use of the low-cost piezoelectric diaphragm for grinding burn monitoring.