Revista Brasileira de Engenharia Agrícola e Ambiental
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/10362
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Item Desempenho operacional de derriçadores mecânicos portáteis derriçadores portáteis, em diferentes condições de lavouras cafeeiras diferentes lavouras cafeeiras(Departamento de Engenharia Agrícola - UFCG, 2005-01) Barbosa, Jackson A.; Salvador, Nilson; Silva, Fábio M. daDentre as operações realizadas na colheita do café, a de derriça é considerada a mais complexa, por sua grande influência no custo de colheita, sendo que a mecanização da operação de derriça do café pode ter efeito significativo em redução do custo final da saca de café colhido, além de ser na região do sul de Minas Gerais, um dos sistemas mais utilizados por produtores de pequeno e médio portes, que utilizam, nesta operação, o sistema de derriçadores mecânicos portáteis, o qual pode apresentar rendimento até oito vezes superior ao da colheita manual. Este trabalho teve como objetivo avaliar o desempenho operacional de derriçadores mecânicos portáteis utilizados na colheita de café e avaliar o custo operacional do sistema de colheita semi-mecanizado, comparativamente ao sistema de colheita manual. Conclui-se que a colheita de café semi-mecanizada apresentou desempenho operacional superior ao da colheita manual, tornando-se uma alternativa viável para os produtores de pequeno a médio porte, visando minimizar os custos de colheita.Item Structural static and modal frequency simulations in a coffee harvester’s chassis(Departamento de Engenharia Agrícola - UFCG, 2018-07) Silva, Evandro P. da; Silva, Fábio M. da; Andrade, Ednilton T. de; Magalhães, Ricardo R.Coffee harvesters are subject to stresses and vibrations in their structure, originating from engines and harvesting system. These structures must be designed to avoid rupturing of the components due to fragility of the materials, inadequate geometries, or the phenomenon of resonance, which increases the displacements/deformations of the components. In this scenario, the main objective of this study is to analyse the results of stresses and displacements from two static simulations and to present results of natural vibration frequencies from two modal simulations in a self-propelled coffee harvester. For this purpose, 20 modal shapes were generated, considering coffee harvester reservoir as empty and full. The simulations were carried out using the finite element method in which actual boundary conditions were applied to the motor chassis of the harvester. As results, stresses above the material’s yield strength were observed in some regions of the components. Greater displacements were observed at the rear of the chassis, suggesting a new positioning of the rear wheel to obtain a uniform load distribution. Some natural frequencies, which presented greater displacements/deformations, can be affected by the operation of the main motor, also associated with the vibrating system in the coffee harvest, which may cause rupture of components.Item Use of classifier to determine coffee harvest time by detachment force(Departamento de Engenharia Agrícola - UFCG, 2018-09) Barros, Murilo M. de; Silva, Fábio M. da; Costa, Anderson G.; Ferraz, Gabriel A. e S.; Silva, Flávio C. daCoffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.