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Control de procesos multicapa en fusión selectiva por láser: un enfoque de aprendizaje por refuerzo

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El aprendizaje por refuerzo mejora el control de la impresión 3D de fusión en lecho de polvo (PBF). Este enfoque basado en datos gestiona eficazmente los complejos problemas térmicos en la fusión selectiva por láser, lo que permite un mejor control de las piezas 3D completas.

Palabras clave:
fusión en lecho de polvocontrol de procesosaprendizaje por refuerzofusión selectiva por láserTi–6Al–4V

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Área de la Ciencia:

  • Fabricación Aditiva
  • Ciencia de Materiales
  • Aprendizaje Automático

Sus antecedentes:

  • La fusión en lecho de polvo (PBF) es una técnica clave de fabricación aditiva para piezas 3D.
  • Los complejos fenómenos físicos en PBF dificultan el modelado analítico y los modelos 3D orientados al control.
  • El control de procesos PBF actual se queda atrás de otros sectores de fabricación debido a estos desafíos de modelado.

Objetivo del estudio:

  • Introducir un marco de aprendizaje por refuerzo (RL) para el control avanzado de procesos en PBF.
  • Abordar la falta de modelos integrados orientados al control en PBF.
  • Demostrar la eficacia de RL para controlar procesos PBF multicapa, específicamente la fusión selectiva por láser.

Principales métodos:

  • Se utilizó un marco de aprendizaje por refuerzo (RL), un enfoque basado en datos adecuado para modelos de procesos intrincados o desconocidos.
  • Se centró en la fusión selectiva por láser, un proceso PBF basado en láser.
  • Se enfatizó la importancia de la estabilidad del entrenamiento en RL para aplicaciones PBF.

Principales resultados:

  • Se demostraron con éxito los beneficios de un marco de control de procesos de RL para piezas 3D completas (múltiples capas).
  • Se mostró una gestión eficaz de los problemas de acumulación de calor inherentes a PBF.
  • Se confirmó la capacidad del marco para lograr un control general robusto del proceso.

Conclusiones:

  • El aprendizaje por refuerzo ofrece una solución viable para el control avanzado de procesos en la fusión en lecho de polvo.
  • El marco de RL aborda eficazmente los desafíos de la gestión térmica, mejorando el control sobre la fabricación de piezas 3D complejas.
  • Esta investigación abre nuevas vías para mejorar la estabilidad y la calidad del proceso PBF a través de estrategias de control inteligentes.