In-process grinding monitoring by acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. Many statistics have shown effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS.

Production grinding burn detection using acoustic emission and electric power signals

This work uses a monitoring system based on a PC platform, where the acoustic emission and electric power signals generated during the grinding process are used to investigate superficial burning occurrence in a surface grinding operation using two types of steel, three grinding conditions and an Al203 vitrified grinding wheel. Acoustic emission signals on the workpiece and grinding power were measured during a surface plunge operation until the grinding burn happened. From the results the standard deviation of the acoustic emission signal and the maximum electric power were calculated for each grinding pass. The proposed DPO parameter is the product between the power level and acoustic emission standard deviation. The results show that both signals can be used for burning detection, and the parameter DPO is the best indicator for the burning studied in this work. This can be explained by the high dispersion of the acoustic emission RMS level associated to the high power consumption when the grinding wheel lose its sharpness.

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece `burn', the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.

Lead zirconate titanate/polyurethane (PZT/PU) composite for acoustic emission sensors

Piezoelectric composite, made from ferroelectric ceramic lead zirconate titanate (PZT) and vegetable based polyurethane (PU) polymer, was doped with a semiconductor filler, graphite. The resulting composite (PZT/C/PU) with 49/1/50- vol. % composition could be poled at lower field and shorter time due to the increased conductivity of the polymer phase following the introduction of graphite. The PZT/C/PU composite showed higher pyroelectric coefficient in comparison with the undoped PZT/PU composite with 50/50-vol. % composition. Also, the PZT/C/PU composite has shown the ability to detect both extensional and flexural modes of simulated acoustic emission (AE) at a distance up to 8.0 m from the source, thus indicating that it may be used for detection of structural damages.

Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

Calcium modified lead titanate/poly-ether-ether-ketone composite for acoustic emission sensors

Composites made of calcium modified lead titanate ceramic powder and poly (ether-ether-ketone) high performance polymer matrix were prepared in the film form using a hot press. The acoustic and electromechanical properties of the composites have been determined using the ultrasonic immersion technique and piezoelectric spectroscopy, respectively. The composite film with 60 - 40 vol.% PTCa/PEEK was tested as acoustic emission detector. Preliminary results shown that the piezo composite can be used as sensor to evaluate the behavior of materials.

In-process grinding monitoring through acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding processes. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 Steel as work material. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system working at 2.5 MHz was used to collect the raw acoustic emission instead of the root mean square value usually employed. Many statistical analyses have shown to be effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm rate (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS. Copyright © 2006 by ABCM.

PTCa/PEEK composite acoustic emission sensors

Composites made of Calcium-modified lead titanate (PTCa) and poly (ether-etherketone) (PEEK) high performance polymer matrix were prepared in the film form using a hot press. The ceramic volume fraction reaches up to 60 percent providing a composite with 0-3 and 1-3 mixed connectivities due to the high ceramic content and the resulting materials could be considered PEEK-bonded PTCa particulate composite. The composites were characterized using piezoelectric spectroscopy and ultrasonic immersion techniques. Values up to 38.5 pC/N were obtained for the longitudinal d33 piezoelectric coefficient. The composite was surface-mounted on a carbon fiber plate-like specimen and the ability of the PTCa/PEEK composite to detect acoustic emission (AE) is reported. © 2006 IEEE.

Piezoelectric composite film for acoustic emission detection

The continuous technological advances require materials with properties that conventional material cannot display. Material property combinations are being the focus to the development of composite materials, which are considered a multiphase material that exhibits properties of the constituent phases. One interesting material to be studied as sensing material is the composite made of ferroelectric ceramic and polymeric matrix as a two-phases composite material. In that case, the combinations properties intended are the high piezo and pyroelectric activities of the dense ceramic with the impact resistance, flexibility, formability and low densities of the polymer. Using the piezoelectric property of the composite film, it can be used to detect acoustic emission (AE), which is a transient elastic wave generated by sudden deformation in materials under stress. AE can be applied for evaluating the health of structures in a nondestructive way and without any lapse of time. The preliminary result indicates that the composite Pz34/PEEK can be used as sensing material for nondestructive evaluation. ©2009 IEEE.

Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

Ensayo no destructivo de soldaduras en pernos conectores mediante inspección acústica = Non-destructive test of welding headed studs through acoustic emission

Los pernos conectores aportan múltiples ventajas de uso, entre las que se encuentra el elevado margen de seguridad que ofrecen sus soldaduras ejecutadas mediante arco eléctrico. Estas soldaduras, aunque ampliamente fiables, son difícilmente comprobadas mediante ensayos no destructivos. Aparte de la inspección visual, que aporta gran información sobre la calidad de ejecución de la soldadura, el resto de ensayos no destructivos (líquidos penetrantes, partículas magnéticas, ultrasonidos, radiografías, etc.) resultan inviables en estos elementos. Por otro lado, los ensayos acústicos de piezas metálicas han existido siempre. Su comprobación se basaba en el análisis por medio de ¿un oído fino¿ del sonido resultante tras ser golpeado el elemento a evaluar. Con estas premisas se plantea el presente estudio de inspección de las soldaduras en pernos conectores mediante su espectro acústico. Analíticamente, la investigación se ha centrado en el cálculo informático de los primeros modos propios de vibración mediante elementos finitos. Se han modelizado diferentes grados de penetración de la soldadura mediante la modificación de las condiciones de contorno. Se ha observado que variando el número de movimientos coaccionados en los nodos pertenecientes a la soldadura se produce una reducción en su frecuencia de vibración.

The effects of body position on distortion-product Otoacoustic emission testing

Otoacoustic emissions are frequently acquired from patients in a variety of body positions aside from the standard, seated orientation. Yet little knowledge is available regarding whether these deviations will produce nonpathological changes to the clinical results obtained. The present study aimed to describe the effects of body position on the distortion-product otoacoustic emissions of 60 normal-hearing adults. With particular attention given to common clinical practice, the Otodynamics ILO292, and the measurement parameters of amplitude, signal-to-noise ratio, and noise were utilized. Significant position-related effects and interactions were revealed for all parameters. Specifically, stronger emissions in the mid frequencies and higher noise levels at the extreme low and high frequencies were produced by testing subjects while lying on their side compared with the seated position. Further analysis of body position effects on emissions is warranted, in order to determine the need for clinical application of position-dependent normative data.

Acoustic emission and yield in sand

An experimental investigation into the Acoustic Emission (AE) response of sand has been undertaken, and the use of AE as a method of yield point identification has been assessed. Dense, saturated samples of sand were tested in conventional triaxial apparatus. The measurements of stresses and strains were carried out according to current research practice. The AE monitoring system was integrated with the soil mechanics equipment in such a way that sample disturbance was minimised. During monotonically loaded, constant cell pressure tests the total number of events recorded was found to increase at an increasing rate in a manner which may be approximated by a power law. The AE response of the sand was found to be both stress level and stress path dependent. Undrained constant cell pressure tests showed that, unlike drained tests, the AE event rate increased at an increasing rate; this was shown to correlate with the mean effective stress variation. The stress path dependence was most noticeable in extension tests, where the number of events recorded was an order of magnitude less than that recorded in comparable compression tests. This stress path dependence was shown to be due to the differences in the work done by the external stresses. In constant cell pressure tests containing unload/reload cycles it was found that yield could be identified from a discontinuity in the event rate/time curve which occurred during reloading. Further tests involving complex stress paths showed that AE was a useful method of yield point identification. Some tests involving large stress reversals were carried out, and AE identified the inverse yield points more distinctly than conventional methods of yield point identification.