In-process thermal damage detection in grinding with monitoring via Internet

This work aims the development of a dedicated system for detection of burning in surface grinding process, where the process will constantly be monitored through the acoustic emission and electric power of the induction motor drive. Acquired by an analog-digital converter, algorithms process the signals and a control signal is generated to inform the operator or interrupt the process in case of burning occurrence. Moreover, the system makes possible the process monitoring via Internet. Additionally, a comparative study between parameters DPO and FKS is carried through. In the experimental work one type of. steel (ABNT-1020 annealed) and one type of grinding wheel referred to as TARGA, model ART 3TG80.3 NVHB, were employed.

Automatic system for thermal damage detection in manufacturing process with Internet monitoring

This work involved the development of a smart system dedicated to surface burning detection in the grinding process through constant monitoring of the process by acoustic emission and electrical power signals. A program in Visual Basic® for Windows® was developed, which collects the signals through an analog-digital converter and further processes them using burning detection algorithms already known. Three other parameters are proposed here and a comparative study carried out. When burning occurs, the newly developed software program sends a control signal warning the operator or interrupting the process, and delivers process information via the Internet. Parallel to this, the user can also interfere in the process via Internet, changing parameters and/or monitoring the grinding process. The findings of a comparative study of the various parameters are also discussed here. Copyright © 2006 by ABCM.

Monitoring the dressing operation in the grinding process

This work was based on a methodology of development and experimentation, and involved monitoring the dressing operation by processing the acoustic emission and electric power signals to detect the optimal dressing moment. Dressing tests were performed in a surface grinding machine with an aluminium grinding wheel. Dressing analysis software was developed and used to process the signals collected earlier in order to analyse not only the dressing parameters but also the software's ability to indicate the instant when the dressing operation could be concluded. Parameters used in the study of burn in grinding were implemented in order to ascertain if they would also prove efficient in monitoring dressing. A comparative study revealed that some parameters are capable of monitoring the dressing operation. It was possible to verify the parameters effectiveness that today are utilised in burning to monitor dressing as well as to create new parameters for monitoring this operation. Copyright © 2009, Inderscience Publishers.

Finishing of flat parts led by lapping kinematics on abrasive disc dressed in agreement with overlap factors

This paper discusses the investigation of an abrasive process for finishing flat workpieces, based on the combination of important grinding and lapping characteristics. Instead of loose abrasive grains between the workpiece and the lapping plate, a resinoid grinding wheel of hot-pressed silicon carbide is placed on the plate of a device resembling a lapping machine. The resin bond grinding wheel is dressed with a single-point diamond. In addition to keeping the plate flat, dressing also plays the role of interfering in the behavior of the process by varying the overlap factor (Ud). It was found that the studied process simplify the set-up and can be controlled more easily than in lapping, whose is a painstaking process. The surface roughness and flatness deviation proved comparable to those of lapping, or even finer than it, with the additional advantage of a less contaminated workpiece surface with a shiny appearance. The process was also monitored by acoustic emission (AE), which indicates to be a promissing and suitable technique for use in this process. Copyright © 2008 by ASME.

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.

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.

Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Desenvolvimento de uma câmara para combustão de combustíveis líquidos, operando sob regime de combustão sem chama visível

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

Composite materials impact damage detection using neural networks

This thesis considers two basic aspects of impact damage in composite materials, namely damage severity discrimination and impact damage location by using Acoustic Emissions (AE) and Artificial Neural Networks (ANNs). The experimental work embodies a study of such factors as the application of AE as Non-destructive Damage Testing (NDT), and the evaluation of ANNs modelling. ANNs, however, played an important role in modelling implementation. In the first aspect of the study, different impact energies were used to produce different level of damage in two composite materials (T300/914 and T800/5245). The impacts were detected by their acoustic emissions (AE). The AE waveform signals were analysed and modelled using a Back Propagation (BP) neural network model. The Mean Square Error (MSE) from the output was then used as a damage indicator in the damage severity discrimination study. To evaluate the ANN model, a comparison was made of the correlation coefficients of different parameters, such as MSE, AE energy, AE counts, etc. MSE produced an outstanding result based on the best performance of correlation. In the second aspect, a new artificial neural network model was developed to provide impact damage location on a quasi-isotropic composite panel. It was successfully trained to locate impact sites by correlating the relationship between arriving time differences of AE signals at transducers located on the panel and the impact site coordinates. The performance of the ANN model, which was evaluated by calculating the distance deviation between model output and real location coordinates, supports the application of ANN as an impact damage location identifier. In the study, the accuracy of location prediction decreased when approaching the central area of the panel. Further investigation indicated that this is due to the small arrival time differences, which defect the performance of ANN prediction. This research suggested increasing the number of processing neurons in the ANNs as a practical solution.

Caractérisation des propriétés mécaniques de la pâte de carbone à 150°C dans le but d’optimiser la mise en forme des anodes utilisées dans les cuves Hall-Héroult

Les anodes de carbone sont des éléments consommables servant d’électrode dans la réaction électrochimique d’une cuve Hall-Héroult. Ces dernières sont produites massivement via une chaine de production dont la mise en forme est une des étapes critiques puisqu’elle définit une partie de leur qualité. Le procédé de mise en forme actuel n’est pas pleinement optimisé. Des gradients de densité importants à l’intérieur des anodes diminuent leur performance dans les cuves d’électrolyse. Encore aujourd’hui, les anodes de carbone sont produites avec comme seuls critères de qualité leur densité globale et leurs propriétés mécaniques finales. La manufacture d’anodes est optimisée de façon empirique directement sur la chaine de production. Cependant, la qualité d’une anode se résume en une conductivité électrique uniforme afin de minimiser les concentrations de courant qui ont plusieurs effets néfastes sur leur performance et sur les coûts de production d’aluminium. Cette thèse est basée sur l’hypothèse que la conductivité électrique de l’anode n’est influencée que par sa densité considérant une composition chimique uniforme. L’objectif est de caractériser les paramètres d’un modèle afin de nourrir une loi constitutive qui permettra de modéliser la mise en forme des blocs anodiques. L’utilisation de la modélisation numérique permet d’analyser le comportement de la pâte lors de sa mise en forme. Ainsi, il devient possible de prédire les gradients de densité à l’intérieur des anodes et d’optimiser les paramètres de mise en forme pour en améliorer leur qualité. Le modèle sélectionné est basé sur les propriétés mécaniques et tribologiques réelles de la pâte. La thèse débute avec une étude comportementale qui a pour objectif d’améliorer la compréhension des comportements constitutifs de la pâte observés lors d’essais de pressage préliminaires. Cette étude est basée sur des essais de pressage de pâte de carbone chaude produite dans un moule rigide et sur des essais de pressage d’agrégats secs à l’intérieur du même moule instrumenté d’un piézoélectrique permettant d’enregistrer les émissions acoustiques. Cette analyse a précédé la caractérisation des propriétés de la pâte afin de mieux interpréter son comportement mécanique étant donné la nature complexe de ce matériau carboné dont les propriétés mécaniques sont évolutives en fonction de la masse volumique. Un premier montage expérimental a été spécifiquement développé afin de caractériser le module de Young et le coefficient de Poisson de la pâte. Ce même montage a également servi dans la caractérisation de la viscosité (comportement temporel) de la pâte. Il n’existe aucun essai adapté pour caractériser ces propriétés pour ce type de matériau chauffé à 150°C. Un moule à paroi déformable instrumenté de jauges de déformation a été utilisé pour réaliser les essais. Un second montage a été développé pour caractériser les coefficients de friction statique et cinétique de la pâte aussi chauffée à 150°C. Le modèle a été exploité afin de caractériser les propriétés mécaniques de la pâte par identification inverse et pour simuler la mise en forme d’anodes de laboratoire. Les propriétés mécaniques de la pâte obtenues par la caractérisation expérimentale ont été comparées à celles obtenues par la méthode d’identification inverse. Les cartographies tirées des simulations ont également été comparées aux cartographies des anodes pressées en laboratoire. La tomodensitométrie a été utilisée pour produire ces dernières cartographies de densité. Les résultats des simulations confirment qu’il y a un potentiel majeur à l’utilisation de la modélisation numérique comme outil d’optimisation du procédé de mise en forme de la pâte de carbone. La modélisation numérique permet d’évaluer l’influence de chacun des paramètres de mise en forme sans interrompre la production et/ou d’implanter des changements coûteux dans la ligne de production. Cet outil permet donc d’explorer des avenues telles la modulation des paramètres fréquentiels, la modification de la distribution initiale de la pâte dans le moule, la possibilité de mouler l’anode inversée (upside down), etc. afin d’optimiser le processus de mise en forme et d’augmenter la qualité des anodes.

Extended frequency range hearing thresholds and otoacoustic emissions in acute acoustic trauma

We sought to evaluate the relative value of pure tone audiometry (PTA), extended high-frequency audiometry (EFA) and transiently evoked otoacoustic emissions (OAE) and distortion products when monitoring acute acoustic trauma (AAT).

Acoustic emission activity from the two main types of small multi-cylinder diesel engine fuel injection systems

Effective fuel injector operation and efficient combustion are two of the most critical aspects when Diesel engine performance, efficiency and reliability are considered. Indeed, it is widely acknowledged that fuel injection equipment faults lead to increased fuel consumption, reduced power, greater levels of exhaust emissions and even unexpected engine failure. Previous investigations have identified fuel injector related acoustic emission activity as being caused by mechanisms such as fuel line pressure build-up; fuel flow through injector nozzles, injector needle opening and closing impacts and premixed combustion related pulses. Few of these investigations however, have attempted to categorise the close association and interrelation that exists between fuel injection equipment function and the acoustic emission generating mechanisms. Consequently, a significant amount of ambiguity remains in the interpretation and categorisation of injector related AE activity with respect to the functional characteristics of specific fuel injection equipment. The investigation presented addresses this ambiguity by detailing a study in which AE signals were recorded and analysed from two different Diesel engines employing the two commonly encountered yet fundamentally different types of fuel injection equipment. Results from tests in which faults were induced into fuel injector nozzles from both indirect-injection and direct-injection engines show that functional differences between the main types of fuel injection equipment results in acoustic emission activity which can be specifically related to the type of fuel injection equipment used.

Passive control of combustion instability in a low emissions aeroderivative gas turbine

This paper describes the conceptual ideas, the theoretical validation, the laboratory testing and the field trials of a recently patented fuel-air mixing device for use in high-pressure ratio, low emissions, gaseous-fueled gas turbines. By making the fuel-air mixing process insensitive to pressure fluctuations in the combustion chamber, it is possible to avoid the common problem of positive feedback between mixture strength and the unsteady combustion process. More specifically, a mixing duct has been designed such that fuel-air ratio fluctuations over a wide range of frequencies can be damped out by passive design means. By scaling the design in such a way that the range of damped frequencies covers the frequency spectrum of the acoustic modes in the combustor, the instability mechanism can be removed. After systematic development, this design philosophy was successfully applied to a 35:1 pressure ratio aeroderivative gas turbine yielding very low noise levels and very competitive NOx and CO measurements. The development of the new premixer is described from conceptual origins through analytic and CFD evaluation to laboratory testing and final field trials. Also included in this paper are comments about the practical issues of mixing, flashback resistance and autoignition.

Field Testing a Full-Scale Tidal Turbine Part 3: Acoustic Characteristics

Like any new technology, tidal power converters are being assessed for potential environmental impacts. Similar to wind power, where noise emissions have led to some regulations and limitations on consented installation sites, noise emissions of these new tidal devices attract considerable attention, especially due to the possible interaction with the marine fauna. However, the effect of turbine noise cannot be assessed as a stand-alone issue, but must be investigated in the context of the natural background noise in high flow environments. Noise measurements are also believed to be a useful tool for monitoring the operating conditions and health of equipment. While underwater noise measurements are not trivial to perform, this non-intrusive mon- itoring method could prove to be very cost effective. This paper presents sound measurements performed on the SCHOTTEL Instream Turbine as part of the MaRINET testing campaign at the QUB tidal test site in Portaferry during the summer of 2014. This paper demonstrates a comparison of the turbine noise emissions with the normal background noise at the test site and presents possible applications as a monitoring system.