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Prediction of the Composite Required in the Design of a Toroidal Vessel Using an Artificial Neural Network

Abstract:

Context: Within the design of toroidal vessels, minimizing the amount of material is very important for reducing production costs; Conventional methods used to minimize the amount of material are computationally time consuming each time a new container is tested. New techniques based on artificial intelligence require the prediction of the minimum amount of material required for the design of a container in the shortest possible time. Method: The prediction methodology is based on a linear regression model through an artificial neural network, which is implemented with the Keras model of Python; In its first phase, a data set created by a script with ANSYS APDL code is handled. Results: An artificial neural network model that learned to predict the minimum amount of material, adequate accuracy and loss of the model. Conclusions: A better performance of the model was obtained, the partition of the dataset into training and testing data, a level of precision was obtained that ensures the reliability of the machine learning model compared to the traditional ones. Contexto: Dentro del diseño de los recipientes toroidales, minimizar la cantidad de material, es muy importante para la reducción de costos de producción; los métodos convencionales que se usan para minimizar la cantidad de material, consumen mucho tiempo de computación cada vez que se ensaya sobre un nuevo recipiente. Las nuevas técnicas basadas en inteligencia artificial, garantizan la predicción de la cantidad mínima de material requerido para el diseño de un recipiente en el menor tiempo posible. Método: La metodología de predicción, está basada en un modelo de …