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.

Study of thresholds to burning in surface grinding process

This work aims at finding out the threshold to burning in surface grinding process. Acoustic emission and electric power signals are acquired from an analog-digital converter and processed through algorithms in order to generate a control signal to inform the operator or interrupt the process in the case of burning occurrence. The thresholds that dictate the situation of burn and non-burn were studied as well as a comparison between the two parameters was carried out. In the experimental work one type of steel (ABNT-1045 annealed) and one type of grinding wheel referred to as TARGA model 3TG80.3-NV were employed. Copyright © 2005 by ABCM.

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.

Methodology for automatic selection of passes in surface grinding

The main purpose of this work is the development of computational tools in order to assist the on-line automatic detection of burn in the surface grinding process. Most of the parameters currently employed in the burning recognition (DPO, FKS, DPKS, DIFP, among others) do not incorporate routines for automatic selection of the grinding passes, therefore, requiring the user's interference for the choice of the active region. Several methods were employed in the passes extraction; however, those with the best results are presented in this article. Tests carried out in a surface-grinding machine have shown the success of the algorithms developed for pass extraction. Copyright © 2007 by ABCM.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.

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)

Chemical Modification of Palm Fibres Surface with Zirconium Oxychloride

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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.

Surface modified Mg-doped ZnO QDs for biological imaging

Fundação Amparo à Pesquisa Estado de São Paulo (FAPESP)

Effect of silica surface on luminescence properties of tb3+-glutamic acid.

We studied the effect of silica surface on luminescence properties of terbium complex by spectroscopy characterization, where microparticles of mesoporous silica type MSU-X was prepared. We used silica with different surface: calcined, washed, functionalized with 3- aminopropyl-triethoxysilane (APTES), and 3-glycidoxypropyl-trimethoxysilane (GPTMS); impregnated with Tb3+-glutamic acid complex. The obtained materials were characterized by scanning electron microscopy, porosity measurements, small-angle X-ray scattering, as structural characterization; Fourier transform infrared and luminescence spectroscopy, as spectroscopy characterization. Finally, we observed that functional groups at the silica surface lead to changes on luminescent properties of the final materials. The observed shift of the absorption and emission bands can be assigned to the effect of the functional groups of mesoporous silica.

Moisture content and its influence on the roughness and noise emission during wood machining

Numerous factors influencing the surface quality of wood after machining, among them we highlight the machining parameters and the properties of the wood. In the analysis of the influence of these factors on machining and in determining the quality measurement systems are used to obtain surface characteristics, these systems are divided into methods of contact and non-contact. The method for mechanical contact performed with the aid of the surface roughness tester is the most valued in the measurement of roughness of wood, however, aiming at a greater agility in these measurements, there is a need to seek alternatives for evaluation of surface quality, and one of these options is to use the forms of indirect measurements of this quality, as for example, the use of noise emission during the machining process. With this, the aim was to analyze the influence of the moisture content of the wood, at different levels, on surface quality of the species Pinus elliottii, determined by the method of mechanical probing move and relate this roughness with the sound emission issued for each class of humidity, during machining. The planning of experiments and statistical analyses were performed with the help of Taguchi method. The specimens were conditioned in greenhouses climatizadoras automatics for obtaining three classes of humidity. Machining tests of wooden pieces were performed on a machining center specific for this type of material. The roughness values were measured by a roughness verifier and the noise emission values were measured by for a measurer sound pressure level. Statistically significant differences were observed, the significance level of 10 %, on roughness and noise emission between the three levels of moisture. It was observed that with the increase in the moisture content occurred an increase of roughness and a reduction in noise emission. Monitoring of surface quality through noise level is an interesting alternative to the method of mechanical contact.