Different methods of cutting fluid application on turning of a difficult-to-machine steel

Different methods of cutting fluid application are used on turning of a difficult-tomachine steel (SAE EV-8). A semi-synthetic cutting fluid was applied using a conventional method, minimum quantity of cutting fluid (MQCF), and pulverization. By the minimum quantity method was also applied a lubricant of vegetable oil (MQL). Thereafter, a cutting fluid jet under high pressure (3.0 MPa) was singly applied in the following regions: chip-tool interface; top surface of the chip; and tool-workpiece contact. Two other methods were used: an interflow between conventional application and chip-tool interface jet and, finally, three jets simultaneously applied. In order to carry out these tests, it was necessary to set up a high pressure system using a piston pump for generating a cutting fluid jet, a Venturi for fluid application (MQCF and MQL), and a nozzle for cutting fluid pulverization. The output variables analyzed included tool life, surface roughness, cutting tool temperature, cutting force, chip form, chip compression rate and machined specimen microstructure. It can be observed that the tool life increases and the cutting force decreases with the application of cutting fluid jet, mainly when it is directed to the chip-tool interface. Excluding the methods involving jet fluid, the conventional method seems to be more efficient than other methods of low pressure. © (2013) Trans Tech Publications, Switzerland.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Efeito de resíduos vegetais de milheto (Pennisetum americanum) e da calagem em algumas propriedades físicas de um nitossolo vermelho em sistema de semeadura direta

Pós-graduação em Agronomia (Agricultura) - FCA

Aplicação de redes neurais artificiais na predição de diâmetro e rugosidade durante o processo de furação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeitos da imobilização, remobilização livre e por meio de exercício físico sobre as propriedades mecânicas histológicas do músculo de ratos de duas faixas etárias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Produção de biomassa, rendimento e composição do óleo essencial de manjericão (Ocimum basilicum L.) em função de reguladores vegetais

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

Sistema plantio direto: avaliação de semeadora em função do manejo da palhada e velocidade de trabalho na cultura da soja

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Hipomineralização molar incisivo em escolares de Botelhos, Minas Gerais, Brasil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeitos da exposiçao à fumaça de cigarro e a ingestão crônica de etanol sobre a mucosas da língua e da faringe: estudo histopatológico em ratos

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

Influência in vitro da clorexidina e do chá verde na longevidade de união adesiva resina composta – dentina

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistência adesiva de bráquetes ortodônticos colados com sistemas adesivos convencionais e auto-condicionantes ao esmalte bovino após ciclos térmicos e termomecânicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação da resistência de união entre dentina preparada por laser e um sistema adesivo a base de silorano, variando a técnica de aplicação e umidade do substrato

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistência adesiva de sistemas autocondicionante e convencional frente a diferentes substratos e associação ao laser Nd:YAG: estudo in vitro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistência adesiva de reparos em resina composta: tratamento da superfície com ponta diamantada, jato de óxido de alumínio e laser de Er:YAG

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)