Ruído de Barkhausen como ferramenta para identificação de modificações microestruturais em aço AISI 1020 soldado

• Ano de defesa: 2019
• Autor(a) principal: Melo, Gudson Nicolau de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA E ENGENHARIA DE MATERIAIS
• Palavras-chave em Português: Ruído de Barkhausen; Aço AISI 1020 soldado; Microestrutura e ensaio não destrutivo; CNPQ::ENGENHARIAS::ENGENHARIA DE MATERIAIS E METALURGICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/28081

Magnetic Barkhausen Noise (MBN) has been studied as an alternative for application to principles of techniques for non-destructive testing (NDT) of ferromagnetic materials. These materials are widely used in industry critical components that require stringent monitoring and inspection by international standards. The MBN phenomenon consists of electrical voltage pulses that occur in the release of energy due to the movement and annihilation of the magnetic domain walls in the magnetization process of ferromagnetic materials. The behavior of MBN have be directly related to the microstructural aspects in the materials because they act by counteracting the movement of the domain walls. Therefore, this work proposes to relate the microstructure obtained in heat-affected regions when welding an AISI 1020 steel plate through specimens extracted from points of interest of the weld bead to use them in magnetization experiments by means of open circuit induction technique. Through the aid of a sensor coil, it is possible to identify voltage pulses along the magnetization that are relative to the effect of MBN. Microscopic analyzes were performed on the specimens to identify specific weld zones and the predominant microconstituents. The Backscattered Electron Diffraction (EBSD) technique made possible to estimate microstructure data regarding grain size and distribution and phase percentage. The MBN behavior have measured by the RMS (mean square root) curves calculated for the noise detected by the sensor coil. Grain boundary length, possible bainitic microconstituents and dislocation density significantly influenced the noise RMS profiles, especially in the amplitude and position of the main peaks.