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.

Fuzzy logic to predict thermal damages of ground parts

One of the critical problems in implementing an intelligent grinding process is the automatic detection of workpiece surface burn. This work uses fuzzy logic as a tool to classify and predict burn levels in the grinding process. Based on acoustic emission signals, cutting power, and the mean-value deviance (MVD), linguistic rules were established for the various burn situations (slight, intermediate, severe) by applying fuzzy logic using the Matlab Toolbox. Three practical fuzzy system models were developed. The first model with two inputs resulted only in a simple analysis process. The second and third models have an additional MVD statistic input, associating information and precision. These two models differ from each other in terms of the rule base developed. The three developed models presented valid responses, proving effective, accurate, reliable and easy to use for the determination of ground workpiece burn. In this analysis, fuzzy logic translates the operator's human experience associated with powerful computational methods.

Influence of optimized lubrication-cooling and minimum quantity lubrication on the cutting forces, on the geometric quality of the surfaces and on the micro-structural integrity of hardened steel parts

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

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.

Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação de redes neurais artificiais na predição de diâmetro e rugosidade durante o processo de furação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação de níveis de desgaste de dressadores de ponta única utilizando sinais de emissão acústica e redes neurais artificiais

Pós-graduação em Engenharia Elétrica - FEB

Retificação da alumina com rebolo diamantado usando diferentes métodos de lubri-refrigeração

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise de superfícies de peças retificadas com o uso de redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoramento da dressagem na retificação através do sinal puro de emissão acústica

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)

Estudo da predição da circularidade e rugosidade de peças retificadas utilizando as redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)