Desarrollo de modelos basados en la Fusión de Datos de Multisensor para la supervisión del proceso de Fabricación de Aditivos Metálicos = Development of Multi-Sensor Data Fusion based models for Metallic Additive Manufacturing process monitoring

Arranz Domínguez, Álvaro (2021). Desarrollo de modelos basados en la Fusión de Datos de Multisensor para la supervisión del proceso de Fabricación de Aditivos Metálicos = Development of Multi-Sensor Data Fusion based models for Metallic Additive Manufacturing process monitoring. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Desarrollo de modelos basados en la Fusión de Datos de Multisensor para la supervisión del proceso de Fabricación de Aditivos Metálicos = Development of Multi-Sensor Data Fusion based models for Metallic Additive Manufacturing process monitoring
Author/s:
  • Arranz Domínguez, Álvaro
Contributor/s:
  • Evrard, Marc
  • Menasalvas Ruíz, Ernestina
  • Yaacoubi, Slah
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: August 2021
Subjects:
Freetext Keywords: Multi-Sensor Data Fusion, Metallic Additive Manufacturing, Laser welding
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The process of Metallic Additive Manufacturing (MAM) in particular and welding in general needs to be monitored to improve the quality of the product to be manufactured or assembled, and so saving time and cost. To do so, and since the possible causes of manufacturing defects are numerous, many sensors are required. For reliable decision-making, the well-known Multi-Sensor Data Fusion (MSDF) seems to be the most appropriate method in this current study. During this master thesis, the goal is to perform a bibliographic study of using MSDF in welding and then in MAM by summarizing the most progressive approaches to develop its own MSDF based model or select the most appropriate for the current topic. In this field, we will pay special attention to the quantity of data needed for each approach.

More information

Item ID: 68793
DC Identifier: https://oa.upm.es/68793/
OAI Identifier: oai:oa.upm.es:68793
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 11 Oct 2021 07:23
Last Modified: 11 Oct 2021 07:23
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