¿Qué ocurriría si se pudiera seleccionar de manera automática el mejor modo de funcionamiento en procesos físicos complejos, como la fabricación a microescala? Que la producción mejoraría, tanto en calidad como en tiempo. Esto es lo que perseguía un grupo de investigadores del Consejo Superior de Investigaciones Científicas (CSIC) que ha desarrollado un nuevo sistema basado en técnicas de inteligencia artificial que lo permite. Dicho sistema computacional “alcanza una nueva frontera en la fabricación a microescala”, explican desde el organismo español.
El estudio realizado al respecto, en el que también ha participado el Centro de Estudios de Fabricación Avanzada y Sostenible de la Universidad de Matanzas (Cuba), ha sido publicado en la revista IEEE Transactions on Industrial Informatics.
Un avance clave para la transformación de la industria
Como explica el investigador del CSIC Rodolfo Haber, del Centro de Automática y Robótica (centro mixto del CSIC y la Universidad Politécnica de Madrid), “el desarrollo de estrategias integradoras e híbridas para el modelado, control y optimización basadas en técnicas de inteligencia computacional es clave en la transformación digital y un eslabón fundamental de la cuarta revolución industrial que estamos viviendo”.
El nuevo método se basa en tres pilares: modelado, optimización y toma de decisiones. El modelado se realiza a partir de los datos adquiridos durante el propio proceso físico, obtenidos mediante la combinación de redes neuronales artificiales con un método de optimización de los parámetros de funcionamiento. La optimización, explican desde el CSIC, se lleva a cabo considerando dos objetivos: la minimización del tiempo de producción y la maximización de la calidad superficial del producto. Las soluciones óptimas obtenidas lo son en cuanto a que ninguna otra solución factible mejora uno de los objetivos sin empeorar el otro. La toma de decisiones se lleva a cabo en tiempo real y de forma totalmente automatizada.
Según Haber, lo bueno del sistema propuesto es que tiene “capacidad de autoaprendizaje, ya que los modelos, que sirven de base a la optimización y a la posterior selección los parámetros más convenientes se pueden actualizar periódicamente a partir de los datos adquiridos en tiempo real”.
Fuente: www.computerworld.es
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