Search for new resonances decaying via WZ to leptons in proton–proton collisions √s=8TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for long-lived neutral particles decaying to quark-antiquark pairs in proton-proton collisions at root s=8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Differential cross section measurements for the production of a W boson in association with jets in proton-proton collisions at √s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Long-range two-particle correlations of strange hadrons with charged particles in pPb and PbPb collisions at LHC energies

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for monotop signatures in proton-proton collisions at root s=8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurements of jet multiplicity and differential production cross sections of Z plus jets events in proton-proton collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the production cross section ratio sigma(chi b2(1P))/sigma(chi b1(1P)) in pp collisions at √s=8TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for a standard model-like Higgs boson in the mu(+)mu(-) and e(+)e(-) decay channels at the LHC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for physics beyond the standard model in final states with a lepton and missing transverse energy in proton-proton collisions at root s = 8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the cross section ratio σttbb/σttjj in pp collisions at √s = 8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of diffractive dissociation cross sections in pp collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The cosmetic dye quinoline yellow causes DNA damage in vitro

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

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.