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.

Topologically massive Yang-Mills: A Hamilton-Jacobi constraint analysis

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)

Evaluation of different methods of cooling-lubrication in cylindrical grinding of advanced ceramic dip

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.

Bianchi identities for an N=2, d=5 supersymmetric Yang-Mills theory on the group manifold

The purpose of this note is the construction of a geometrical structure for a supersymmetric N = 2, d = 5 Yang-Mills theory on the group manifold. From a general hypothesis proposed for the curvatures of the theory, the Bianchi identities are solved, whose solution will be fundamental for the construction of the geometrical action for the N = 2, d = 5 supergravity and Yang-Mills coupled theory.

Perturbative super-Yang-Mills from the topological AdS 5 × S 5 sigma model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Caos e termalização na teoria de Yang-Mills-Higgs em uma rede espacial

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

Grinding of AISI 4340 steel with interrupted cutting by aluminum oxide grinding wheel

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

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.