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.

Neural tool condition estimation in the grinding of advanced ceramics

Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.

Grinding process monitoring based on electromechanical impedance measurements

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.

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq)

Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks

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

Isolation of Shiga toxigenic Escherichia coli from butcheries in Taquaritinga city, State of São Paulo, Brazil

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

Rectificacion de aceros fragiles y ductiles: Parametros de entrada para obtener los mejores resultados

In this work, the behavior of the steel surface grinding process with conventional grinding wheels was studied for various grinding conditions and a fixed dressing condition. The input parameters (wheel speed, workpiece speed, depth of cut), that set up the grinding conditions were determined as a function of the characteristics of the grinding machine. A test bench was established where the grinding power was measured in real time as a function of the volume of material removed. The results showed that in the grinding of fragile steels with a soft wheel and medium grit size, the average power of the cut shows a tendency to increase and become stabilized. The surface roughness of the piece decreased in all the tests and was proportional to the increase in the number of active grits. In tests on the grinding of ductile steels with a soft wheel and fine grit size, the wheel became dull until the moment that the abrasive grits were released from the cut surface.

Presence of extraintestinal pathogenic Escherichia coli in butcheries in Taquaritinga, SP, Brazil

The study was conducted in twenty-three butcheries in the city of Taquaritinga, State of São Paulo, Brazil, surveyed during a 10 months period. Among two hundred and eighty-seven Escherichia coli strains isolated from samples of ground beef, meat-grinding-machines and the hands of manipulators, five were recognized as extraintestinal pathogenic E. coli (ExPEC), showing virulence factors (P and S fimbriae, hemolysin and aerobactin) and presenting multidrug resistance. Retail-sold food may constitute an important vehicle for the dissemination of ExPEC in communities, giving rise to reasons for concern.

Avaliação de modelos neurais aplicados na estimação de parâmetros da retificação de cerâmicas avançadas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Investigação de novas ferramentas estatísticas e utilização de microcontrolador no monitoramento da queima na retificação plana tangencial

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoramento da dressagem do processo de retificação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise da influência da profundidade de corte e de diferentes métodos de lubri-refrigeração na retificação plana de aço ABNT 4340

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação de redes neurais artificiais no monitoramento da operação de dressagem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Otimização da retificação tangencial plana de compósitos plásticos reforçados com fibras de carbono

Pós-graduação em Ciência e Tecnologia de Materiais - FC