Analysis and characterization of composites materials with continuous fiber manufactured with additive manufacturing

• Grant number: 24/10683-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2024
• End date: August 31, 2025
• Field of knowledge: Engineering - Aerospace Engineering
• Principal Investigator: Marcelo Leite Ribeiro
• Grantee: Murilo Bisterzo Crepaldi
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The fabrication of parts by additive manufacturing is a process that is becoming the subject of research and study, with many applications on the market, due to its great efficiency in the manufacture of complex parts, allowing the creation of different types of geometries for printing, reducing costs and material waste. New studies are also related to the use of different materials for additive manufacturing, the most common of which are Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA), used in FDM (Fused Deposition Modeling) 3D printing. And with new technologies and tools, 3D printing also started to use composite materials, both as a base filament for printing and to reinforce parts using continuous fibers from different materials, such as glass, aramid and carbon fibers. However, these materials present great complexity in their mechanical behavior due to their anisotropy and the direct dependence of several 3D printing factors (such as manufacturing orientation and types of part filling) on the strength of the final part, requiring a detailed study and carrying out various tests to characterize these materials. Thus, this project seeks to obtain the properties of continuous fiber composite materials used in a 3D printer in the linear-elastic regime to characterize their mechanical behavior under different manufacturing conditions, and also to study the behavior of materials in the presence of damage, through cyclical tests, in an attempt to obtain a failure prediction model for these materials.