A machine learning approach for the pbkp_rediction of melting efficiency in wire arc additive manufacturing
Abstract:
Wire arc additive manufacturing (WAAM) appears as one of the most promising technologies due to its capacity to process all types of materials used in welding, its high production rate, and capacity to process large geometries of particular interest in the aeronautical industry. Since this technology is still under investigation, it is important to determine the efficiency of the process; in this sense, the melting efficiency stands out not only as a parameter of interest in energy terms but also as a measure of the stability of the process. For calculating melting efficiency, it is necessary to use tailored colorimeters or apply models requiring specific dimensions that involve destructive testing. For this reason, in the development of this work, the melting efficiency is evaluated through machine learning algorithms. Processing parameters such as wire diameter, wire feed speed, travel speed, and net power are used to determine …
Año de publicación:
2022
Keywords:
Fuente:
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Tipo de documento:
Other
Estado:
Acceso abierto
Áreas de conocimiento:
- Aprendizaje automático
- Ingeniería de manufactura
- Ingeniería de fabricación
Áreas temáticas:
- Métodos informáticos especiales
- Ingeniería y operaciones afines
- Instrumentos de precisión y otros dispositivos