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Rapid advancement in additive manufacturing capabilities and computational resources has catalyzed multiscale topology optimization (TO). Recently, TO in general and multiscale TO in particular have received increased attraction due to its systematic approach to design globally and locally optimized structures. The multiscale TO computational paradigm brings additional new challenges, including geometric frustration, non-smooth boundaries, and higher computational time that needs to be handled. In this paper, a novel deep learning-based computational pipeline is developed to overcome the challenges encountered in the multiscale TO framework. The outlined computational pipeline employs two neural networks (NNs) architecture: (1) SILONet and (2) ConnectivetyNet. SILONet predicts optimized microstructure for elements of macroscale optimized structure, and ConnectivetyNet improves connectivity for multiscale topology fitted with SILONet outputs. The main novelty of this paper is the utilization of two NNs architecture for alleviating challenges, such as geometric frustration, non-smooth edges, dangling structures at boundaries, and accelerating multiscale TO computations while capturing essential physics and ensuring optimized solutions. The effectiveness of the proposed approach is demonstrated by its application to several two-dimensional and three-dimensional test cases. [ABSTRACT FROM AUTHOR]

Topology optimization is computationally demanding for times of finite element analysis during iteration, especially for 3D structure optimization in intelligent design and construction, where a large amount of concept designs are executed. To this end, this paper proposes a deep Convolutional Neural Network (CNN) for 3D structural topology optimization, which can predict a near-optimal structure in negligible time without using any iterative scheme. To train the neural network, the Simplified Isotropic Material with Penalization (SIMP) method was adopted to generate the dataset of the optimized structures under the randomness loading conditions and volume fractions. The performance evaluation results show that compared with the SIMP method, the proposed method achieved a significant reduction in computation cost with little sacrifice on the performance of design solutions. Furthermore, the generalization ability of the proposed method trained on single boundary condition as well as multiple boundary conditions are explored. Finally, a stress-based topology optimization of the L-shape design domain is performed, which shows the portability of the proposed method to other more complex constraints. [ABSTRACT FROM AUTHOR]

This paper introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. The main novelty of this framework is the utilization of parameters such as density and nodal deflections to predict optimized topologies for a wide range of design space. In this research, a three-stage NN framework is employed, wherein the first stage is a feedforward deep neural network. The second stage uses a convolutional neural network (CNN), and the final stage is the post-processing of results obtained from CNN. The first stage maps input design variables vector to output density distribution image. The image output of the first stage and design variables vector inputted to the first network serve as inputs to the second stage, which outputs optimized structure. Finally, post-processing of this optimized structure is done to ensure volume fraction constraint and optimal solution. These structures can be tessellated per the nodal displacement of an FEA solved larger structure. Because of this framework, the proposed approach makes TO computations faster and captures essential physics while using NNs to determine the final structure. The application of the proposed model to three-dimensional problems is also demonstrated. [ABSTRACT FROM AUTHOR]

The classical method of optimising structures for strength is computationally expensive due to the requirement of performing complex non-linear finite element analysis (FEA). This study aims to optimise an artificial neural network (ANN) architecture to perform the task of predicting the strength of adhesively bonded joints in place of non-linear FEA. A manual multi-objective optimisation was performed to find a suitable ANN architecture design space. Then a genetic algorithm optimisation of the reduced design space was conducted to find an optimum ANN architecture. The generated optimum ANN architecture predicts efficiently the strength of adhesively bonded joints to a high degree of accuracy in comparison with the legacy method using FEA with a 93% savings in computational cost. [ABSTRACT FROM AUTHOR]

A vital necessity when employing state-of-the-art deep neural networks (DNNs) for topology optimization is to predict near-optimal structures while satisfying pre-defined optimization constraints and objective function. Existing studies, on the other hand, suffer from the structural disconnections which result in unexpected errors in the objective and constraints. In this study, a two-stage network model is proposed using convolutional encoder-decoder networks that incorporate a new way of loss functions to reduce the number of structural disconnection cases as well as to reduce pixel-wise error to enhance the predictive performance of DNNs for topology optimization without any iteration. In the first stage, a single DNN model architecture is proposed and used in two parallel networks using two different loss functions for each called the mean square error (MSE) and mean absolute error (MAE). Once the priori information is generated from the first stage, it is instantly fed into the second stage, which acts as a rectifier network over the priori predictions. Finally, the second stage is trained using the binary cross-entropy (BCE) loss to provide the final predictions. The proposed two-stage network with the proposed loss functions is implemented for both two-dimensional (2D) and three-dimensional (3D) topology optimization datasets to observe its generalization ability. The validation results showed that the proposed two-stage framework could improve network prediction ability compared to a single network while significantly reducing compliance and volume fraction errors. [ABSTRACT FROM AUTHOR]

Due to their designable properties, microstructural materials have emerged as an important class of materials that have the potential for used in a variety of applications. The design of such materials is challenged by the multifunctionality requirements and various constraints stemmed from manufacturing limitations and other practical considerations. Traditional design methods such as those based on topological optimization techniques rely heavily on high-dimensional physical simulations and can be inefficient. In addition, it is difficult to impose geometrical constraints in those methods. In this work, we propose a deep learning model based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN) for the design of microstructural materials. The DCGAN is used to generate design candidates that satisfy geometrical constraints and the CNN is used as a surrogate model to link the microstructure to its properties. Once trained, the two networks are combined to form the design network which is utilized to for the inverse design. The advantages of the method include its high efficiency and the simplicity in handling geometrical constraints. In addition, no high-dimensional sensitivity simulations are required. The performance of the method is demonstrated on the design of microstructural materials with desired compliance tensor, subject to specified geometrical constraints. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel deep learning-based method to predict an optimized structure for a given boundary condition and optimization setting without using any iterative scheme. For this purpose, first, using open-source topology optimization code, datasets of the optimized structures paired with the corresponding information on boundary conditions and optimization settings are generated at low (32 × 32) and high (128 × 128) resolutions. To construct the artificial neural network for the proposed method, a convolutional neural network (CNN)-based encoder and decoder network is trained using the training dataset generated at low resolution. Then, as a two-stage refinement, the conditional generative adversarial network (cGAN) is trained with the optimized structures paired at both low and high resolutions and is connected to the trained CNN-based encoder and decoder network. The performance evaluation results of the integrated network demonstrate that the proposed method can determine a near-optimal structure in terms of pixel values and compliance with negligible computational time. [ABSTRACT FROM AUTHOR]