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.

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.

Dressing monitoring with a digital signal processor

Grinding is a finishing process in machining operations, and the topology of the grinding tool is responsible for producing the desired result on the surface of the machined material The tool topology is modeled in the dressing process and precision is therefore extremely important This study presents a solution in the monitoring of the dressing process, using a digital signal processor (DSP) operating in real time to detect the optimal dressing moment To confirm the monitoring efficiency by DSP, the results were compared with those of a data acquisition system (DAQ) and offline processing The method employed here consisted of analyzing the acoustic emission and electrical power signal by applying the DPO and DPKS parameters The analysis of the results allowed us to conclude that the application of the DPO and DPKS parameters can be substituted by processing of the mean acoustic emission signal, thus reducing the computational effort

Transverse cylindrical grinding of a eutectic alloy

We report herein on a comparison of the performance of two different grinding wheels (conventional and CBN) in the transverse cylindrical grinding of a eutectic alloy. Three cutting conditions were tested: rough, semi-finishing and finishing. The parameters of evaluation were the cutting force, roughness and wheel wear. The optimal cutting force and roughness values were obtained when grinding with the conventional wheel, due to the superior dressing operation performed under every cutting condition tested. Although the CBN wheel presented the best G ratio values, they were lower than expected owing to the inappropriate dressing operation applied. Excessive wheel corner wear was detected in both wheels, caused by the grinding kinematics (transverse grinding) employed. In terms of cutting force and roughness, the conventional wheel proved to be the better choice under the conditions tested. However, in terms of the G ratio, a cost analysis is crucial to determine whether the differences between the wheels justify the use of the CBN wheel, in which case the dressing operation requires improvement.

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.

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)

Análise de um processo abrasivo combinando em algumas características da lapidação e retificação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Proposta de uma máquina para processo de abrasão baseado na combinação de algumas características da lapidação e retificação

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)

Aplicação de redes neurais artificiais no monitoramento da operação de dressagem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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)

Large apical periodontitis healing following root canal dressing with calcium hydroxide: a case report

PURPOSE: The objective of this paper is to report the clinical case of a patient who presented a chronic apical periodontitis, arising from internal inflammatory resorption followed by pulp necrosis, and a long-term success of a root canal therapy using calcium hydroxide as root canal dressing. CASE DESCRIPTION: A 20-year-old male patient presented for routine dental treatment. By radiographic examination we noted an extensive radioluscent area, laterally to the permanent maxillary right lateral incisor, with possibility of communication with the lateral periodontium, suggestive of a chronic apical periodontitis. Due to external root resorption detection, we used a calcium hydroxide root canal dressing, changed every 15 days, for a period of 2 months. Root canal filling was performed using gutta-percha cones by lateral condensation technique Radiographic follow up held after 19 years of treatment indicated a periodontium in conditions of normality, with the presence of lamina dura. CONCLUSION: Calcium hydroxide is a suitable material to be used as root canal dressing in teeth with apical periodontitis. Long-term evaluation demonstrated the satisfactory clinical outcome following root canal treatment.

Effect of Laser (670 nm) on Healing of Wounds Covered with Occlusive Dressing: A Histologic and Biomechanical Analysis

Objectives: To analyze the effects of low-level laser therapy (LLLT), 670 nm, with doses of 4 and 7 J/cm(2), on the repair of surgical wounds covered by occlusive dressings. Background Data: The effect of LLLT on the healing process of covered wounds is not well defined. Materials and Methods: For the histologic analysis with HE staining, 50 male Wistar rats were submitted to surgical incisions and divided into 10 groups (n=5): control; stimulated with 4 and 7 J/cm(2) daily, for 7 and 14 days, with or without occlusion. Reepithelization and the number of leukocytes, fibroblasts, and fibrocytes were obtained with an image processor. For the biomechanical analysis, 25 rats were submitted to a surgical incision and divided into five groups (n=5): treated for 14 days with and without occlusive dressing, and the sham group. Samples of the lesions were collected and submitted to the tensile test. One-way analysis of variance was performed, followed by post hoc analysis. A Tukey test was used on the biomechanical data, and the Tamhane test on the histologic data. A significance level of 5% was chosen (p <= 0.05). Results: The 4 and 7J/cm(2) laser with and without occlusive dressing did not alter significantly the reepithelization rate of the wounds. The 7 J/cm(2) laser reduced the number of leukocytes significantly. The number of fibroblasts was higher in the groups treated with laser for 7 days, and was significant in the covered 4 J/cm(2) laser group. Conclusions: Greater interference of the laser-treatment procedure was noted with 7 days of stimulation, and the occlusive dressing did not alter its biostimulatory effects.