In this work, a topology optimization procedure is proposed and applied to the TEAM 25 problem, i.e., a model of a die press with an electromagnet for orientation of magnetic powder. The shape of the press is described as a free discretized profile, and its relation to the flux density in the cavity is simulated by finite element analysis (FEA) and learned by a deep neural network (DNN) model. The DNN is used as a surrogate model for optimization, aiming to obtain a desired flux density distribution in the cavity. Results are promising, as better accuracy is obtained with respect to the full FEA-based optimization approach with the reduced time cost. Once trained, the surrogate model can be used to efficiently solve a whole family of problems where a different target field distribution is defined.
A Deep Learning Surrogate Model for Topology Optimization
Barmada, S;Fontana, N;Thomopulos, D;Tucci, M
2021-01-01
Abstract
In this work, a topology optimization procedure is proposed and applied to the TEAM 25 problem, i.e., a model of a die press with an electromagnet for orientation of magnetic powder. The shape of the press is described as a free discretized profile, and its relation to the flux density in the cavity is simulated by finite element analysis (FEA) and learned by a deep neural network (DNN) model. The DNN is used as a surrogate model for optimization, aiming to obtain a desired flux density distribution in the cavity. Results are promising, as better accuracy is obtained with respect to the full FEA-based optimization approach with the reduced time cost. Once trained, the surrogate model can be used to efficiently solve a whole family of problems where a different target field distribution is defined.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
