Método para otimização do planejamento de produção de peças que utilizam corte de chapas metálicas com foco em redução de custos e resíduos

• Ano de defesa: 2020
• Autor(a) principal: Chagas, Ruan Rithelle de Faria Franco
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Mecânica e de Materiais; UTFPR
• Palavras-chave em Português: Produção - Planejamento; Indústria 4.0; Algorítmos genéticos; Redução de resíduos; Metais - CorteMetal-cutting; Pesquisa operacional; Processos de fabricação; Production - Planning; Industry 4.0.; Genetic algorithms; Waste minimization; Operations research; Manufacturing processes; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA::PROCESSOS DE FABRICACAO::PROCESSOS DE FABRICACAO, SELECAO ECONOMICA; Engenharia Mecânica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/24720

The reduction of unnecessary expenses and the growth of environmental awareness are factors that directly impact the decision of large companies to seek new ways to improve the use of raw materials in their factories. This work was developed in partnership with a company that produces agricultural machines, and that sought to reduce the amount of waste generated in the process of cutting sheet metal. The partner company discards tons of steel every month. The company already has a software that performs the best physical distribution of parts within their respective plates. However, after conducting a bibliometric and systemic analysis, the opportunity was identified to develop a method that performs this distribution of parts on the plates considering that a defined number of parts could be stored. An analysis of the case was carried out. Thus, the company’s production plan could be changed to allow it to be possible to increase the use of the plates. For the development of this method, the methodological framework used was Design Science Research. Thus, six steps were taken: (i) problem identification and motivation; (ii) definition of the solution’s objectives; (iii) design and development; (iv) demonstration; (v) evaluation; and (vi) communication of results. It was defined at the beginning of the method creation that it would be a genetic algorithm written in Python. To demonstrate the method, the response obtained by the partner company’s software was compared to the result estimated by the proposed method. In this way, it was possible to measure the representativeness of the presented method. Then, the method was evaluated for its efficiency, ease of use, operability, and generality. The efficiency assessment was carried out quantitatively, comparing the results obtained by the method with the results obtained by the company in the same period. For this scenario, the method obtained better results. The evaluations of ease of use, operability, and generality were carried out qualitatively from the answers obtained in a questionnaire delivered to the engineers of the partner company. When analyzing the results, it is possible to notice that although it may suffer some resistance, it could be implemented in the partner company. At the same time, it was proven that for the evaluated context, the method fulfilled its objective.