Detalhes bibliográficos
| Ano de defesa: |
2024 |
| Autor(a) principal: |
Lemos, Fernando de Oliveira
 |
| Orientador(a): |
Vecchia, Felipe Dalla
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| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Tese
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
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| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia e Tecnologia de Materiais
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| Departamento: |
Escola Politécnica
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| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://tede2.pucrs.br/tede2/handle/tede/11206
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Resumo: |
The present study addressed a technology with great potential for impact on the environmental performance of Production Systems: Additive Manufacturing (AM). The identification of research opportunities on the assessment of the environmental performance of additive processes based on the application of hybrid computational models, integrating Discrete Event Simulation (DES) and Life Cycle Assessment (Life Cycle Assessment - LCA), motivated the Thesis proposal. The study answered the research question: how to integrate DES and LCA models to evaluate environmental performance in production systems with Additive Manufacturing? It was a proposed framework for developing a hybrid DES-LCA model, which was applied to identify and evaluate critical environmental factors in a case study on centralized and decentralized production. The model generated results that reflect the variability of operations time, energy consumption and transportation time in indicators of CO2 emissions, Accumulated Energy Demand and Global Warming, allowing a dynamic view of the system evaluated when compared with the results of the LCA model. The results obtained from reductions in indicators (between 74.05 and 75.26%), with the change from AM-HUB to AM-CLUSTER, indicate a positive effect with decentralization. The DES-LCA model allowed evaluating trade-offs in the use of different system configurations and evaluating the sensitivity of results in relation to changes in critical parameters. The proposed framework can be used in studies aimed at developing DES-LCA models, guiding and supporting the structuring and validation of models, data collection, generation of results and analysis of alternative scenarios |
