DESENVOLVIMENTO DE UMA METODOLOGIA COM APOIO COMPUTACIONAL PARA AVALIAÇÃO DE RISCO ERGONÔMICO EM MÁQUINAS DE COLHEITA FLORESTAL

• Ano de defesa: 2021
• Autor(a) principal: Nascimento, Glícia Silvania Pedroso
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Doutorado em Ciências Florestais; Centro de Ciências Agrárias e Engenharias; UFES; Programa de Pós-Graduação em Ciências Florestais
• Palavras-chave em Português: Ergonomia; mecanização florestal; avaliação ergonômica; índice ergonômico; análise multicritério; subject.br-rjbn; Recursos Florestais e Engenharia Florestal
• Link de acesso: http://repositorio.ufes.br/handle/10/14667

Given the high degree of mechanization of forestry activities, it is expected that the machines are aligned with ergonomic quality, given that there is a relationship between worker comfort and the performance and productivity of operations. In this context, the objective of this research was to develop a methodology for the analysis of ergonomic risks in forest harvesting machines, which would estimate and classify the risk level of unhealthy conditions of the evaluated machines and act as support for ergonomic diagnosis. Therefore, the developed methodology considered the following criteria: noise; vibration, thermal environment, cab layout, operator seat, visibility, safety and biomechanics. Based on the input variables, through multicriteria analysis methods, the risk relationship was structured by associating the fuzzy system and the AHP method, to predict the Ergonomic Risk Index (IRE) as an output variable, WHERE the safety of the forest machine is quantified and rated for ergonomic efficiency. The proposed mathematical method is operationalized by the ERGOforest software. Different models and brands of forest machines typical of forest harvesting activities were evaluated by the method: Feller-Bunchers (cutting), Forwarders (extraction) and Havesters (cutting). The index qualified the Harvester HV3 machine with the most satisfactory ergonomic performance among the evaluated machines, its highlight was the cabin design capable of offering better working conditions (thermal comfort, good visibility, low vibration levels inside the cabin and compliance in the cabins safety items) for operators. The mathematical model presented itself as a satisfactory methodology, being able to assist in decision making and objective analysis.