Análise da influência da velocidade de mergulho na retificação de aços endurecidos utilizando refrigeração otimizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aprimoramento da técnica da mínima quantidade de lubrificante (MQL) no processo de retificação cilíndrica externa de mergulho de aços endurecidos

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Utilização de diferentes métodos de lubri-refrigeração na retificação cilíndrica externa de mergulho de cerâmica avançada com rebolo diamantado

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Influência dos métodos de lubri-refrigeração na retificação do aço ABNT 4340 usando rebolo de CBN

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Utilization of teflon and aluminum oxide for wheel cleaning in Minimum Quantity Lubrication (MQL) grinding

Researches concerning cooling-lubrication optimization in grinding have been conducted to contribute to a more sustainable process. An alternative to flood coolant is minimum quantity lubrication (MQL), which spray oil droplets in a compressed air jet. However, problems related to wheel cleaning were reported, due to wheel loading by a mixture of chips and oil, resulting in worsening of surface quality. This work aims to evaluate the viability of Teflon and aluminum oxide for wheel cleaning, compared to MQL without cleaning and MQL with cleaning by compressed air, through the following output variables: surface roughness, roundness, wheel wear, grinding power and acoustic emission. Vickers microhardness measurements and optical microscopy were also carried out. The results showed that both materials were efficient in cleaning the wheel, compared to MQL without cleaning, but not as satisfactory as compressed air. Much work is to be done in order to select the right material for wheel cleaning.

Spherical, cylindrical and tetrahedral symmetries; hydrogenic states at high magnetic field in Si:P

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

In Vivo Osteogenesis and In Vitro Streptococcus mutans Adherence: Porous-Surfaced Cylindrical Implants vs Rough-Surfaced Threaded Implants

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

Utilization of Minimum Quantity Lubrication (MQL) with Water in CBN Grinding of Steel

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

Effect of grinding with diamond-disc and -bur on the mechanical behavior of a Y-TZP ceramic

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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)

Ultra-precision face grinding with constant pressure, lapping kinematics, and SiC grinding wheels dressed with overlap factor

Several machining processes have been created and improved in order to achieve the best results ever accomplished in hard and difficult to machine materials. Some of these abrasive manufacturing processes emerging on the science frontier can be defined as ultra-precision grinding. For finishing flat surfaces, researchers have been putting together the main advantages of traditional abrasive processes such as face grinding with constant pressure, fixed abrasives for two-body removal mechanism, total contact of the part with the tool, and lapping kinematics as well as some specific operations to keep grinding wheel sharpness and form. In the present work, both U d-lap grinding process and its machine tool were studied aiming nanometric finishing on flat metallic surfaces. Such hypothesis was investigated on AISI 420 stainless steel workpieces U d-lap ground with different values of overlap factor on dressing (Ud=1, 3, and 5) and grit sizes of conventional grinding wheels (silicon carbide (SiC)=#800, #600, and #300) applying a new machine tool especially designed and built for such finishing. The best results, obtained after 10 min of machining, were average surface roughness (Ra) of 1.92 nm, 1.19-μm flatness deviation of 25.4-mm-diameter workpieces, and mirrored surface finishing. Given the surface quality achieved, the U d-lap grinding process can be included among the ultra-precision abrasive processes and, depending on the application, the chaining steps of grinding, lapping, and polishing can be replaced by the proposed abrasive process.

A study on the viability of minimum quantity lubrication with water in grinding of ceramics using a hybrid-bonded diamond wheel

With the currently strict environmental law in present days, researchers and industries are seeking to reduce the amount of cutting fluid used in machining. Minimum quantity lubrication is a potential alternative to reduce environmental impacts and overall process costs. This technique can substantially reduce cutting fluids in grinding, as well as provide better performance in relation to conventional cutting fluid application (abundant fluid flow). The present work aims to test the viability of minimum quantity lubrication (with and without water) in grinding of advanced ceramics, when compared to conventional method (abundant fluid flow). Measured output variables were grinding power, surface roughness, roundness errors and wheel wear, as well as scanning electron micrographs. The results show that minimum quantity lubrication with water (1:1) was superior to conventional lubrication-cooling in terms of surface quality, also reducing wheel wear, when compared to the other methods tested.