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Investigation of manufacturing process effects on microstructure and fatigue prediction in composite automotive tailgate design

Article dans une revue avec comité de lecture
Author
ccFITOUSSI, Joseph
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccNOUIRA, Samia
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccBENFRIHA, Khaled
127758 Laboratoire Conception de Produits et Innovation [LCPI]
LARIBI, Mohamed-Amine
259761 Université de Bordeaux [UB]
KALLEL, Achraf
307373 Pôle Universitaire Léonard de Vinci [PULV]
TIE BI, Robert
ccSHIRINBAYAN, Mohammadali
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/25463
DOI
10.1007/s00170-024-12988-z
Date
2024-01
Journal
The International Journal of Advanced Manufacturing Technology

Abstract

Manufacturing processes significantly influence microstructural variations in short fiber reinforced composites, which affect damage characteristics and fatigue life. Accurate fatigue life prediction is critical, especially when considering the impact of these manufacturing induced microstructural nuances. In this study, we investigate how manufacturing processes shape microstructures and their impact on fatigue life prediction. We present an advanced micromechanical model for predicting fatigue life in tangible structures, considering the microstructure distribution influenced by manufacturing dynamics. Our model links damage from monotonic loading to fatigue life, resulting in a multi-scale fatigue prediction model. This approach builds a database revealing the interaction between Tsai-Wu failure criterion parameters, manufacturing-induced microstructural variations, and target fatigue life. Using these insights, we fine-tune material properties in finite element simulations for precise design optimization. We illustrate our method using an automotive tailgate made from a sheet molding compound. This research highlights the critical role of manufacturing processes in microstructure variation and fatigue life prediction. It offers the potential for significant vehicle weight reduction, energy savings, and reduced emissions in automotive design and promises to be a valuable tool for optimizing manufacturing process parameters.

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