A novel topology design approach using an integrated deep learning network architecture

TL;DR

This paper introduces a new topology design method that combines GANs and CNNs to rapidly generate optimal structures, overcoming the computational limitations of traditional optimization techniques.

Contribution

It presents an innovative integrated deep learning architecture for topology optimization, enabling faster and more effective initial design generation.

Findings

The proposed method significantly reduces computation time.

It produces high-quality topology designs comparable to traditional methods.

The approach demonstrates potential for practical engineering applications.

Abstract

Topology design optimization offers tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the topology design problem using a standard topology optimization process, such as SIMP (Simplified Isotropic Material with Penalization) is possible. In reality, an estimated over thousands of design iterations is often required for just a few design variables, the conventional optimization approach is in general impractical or computationally unachievable for real world applications significantly diluting the development of the topology optimization technology. There is, therefore, a need for a different approach that will be able to optimize the initial design topology effectively and rapidly. Therefore, this work presents a new topology design procedure to generate optimal structures using an integrated Generative Adversarial Networks (GANs) and convolutional neural network architecture.