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.

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.

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.

Type of grinding of the main cereal of the diet affects production of brown egg-laying hens

The influence of method of grinding of the cereal of the diet on production and egg quality was studied in 420 Hy-line brown egg-laying hens. The design was completely randomized with six treatments arranged as a 3 × 2 factorial with three cereals (barley, dented maize and soft wheat) and two grinding procedures (hammer mill vs. roller mill). Each treatment was replicated seven times and the experimental unit was an enriched cage with ten hens. Production was recorded every four weeks from 24 to 59 weeks of age and egg quality was measured at 40 and 56 weeks of age. For the entire experiment, feed intake was higher in hens fed wheat or maize than in hens fed barley (110.8 and 110.7 vs. 109.7 g/d; P = 0.014) but most of the differences were observed when the cereal was roller milled (P = 0.009 for the interaction). Also, egg production was similar for the three diets when the cereal was hammer milled but tended to be lower for the barley than for the wheat or maize diets when the cereal was roller milled (P = 0.09 for the interaction). None of the other productive or egg quality traits was affected by dietary treatment. We conclude that roller mills are useful to grind low fiber cereals, such as maize or wheat. However, the use of the roller mill might not be adequate when barley is the main cereal in diets for egg-laying hens.

Shakeout, cleaning, grinding, and inspection departments : health hazards in a foundry.

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