Intelligent layout design of curvilinearly stiffened panels via deep learning-based method
TLDR
Numerical examples demonstrate that the proposed intelligent optimization framework significantly improves the optimization efficiency compared to traditional models, and indicates the extraordinary promise of deep learning-based methods in the field of engineering optimization.About:
This article is published in Materials & Design.The article was published on 2021-01-01 and is currently open access. It has received 36 citations till now. The article focuses on the topics: Engineering optimization & Surrogate model.read more
Citations
More filters
Journal ArticleDOI
Pragmatic generative optimization of novel structural lattice metamaterials with machine learning
TL;DR: In this paper, machine learning is used to guide the discovery of new unit cells that are Pareto optimal for multiple competing objectives, such as maximizing elastic stiffness during static loading and minimizing wave speed through the metamaterial during an impact event.
Journal ArticleDOI
Manufacturable insight into modelling and design considerations in fibre-steered composite laminates: state of the art and perspective
TL;DR: In this article, the authors summarize and discuss underlying fiber placement technologies including tailored fiber placement (TFP), continuous tow shearing (CTS), and automated fibre placement (AFP), followed by a detailed discussion on the manufacturing limitations and constraints of the AFP process.
Journal ArticleDOI
Generative design of stiffened plates based on homogenization method
TL;DR: A generative design method of stiffened plates (GDMSP) based on the homogenization method is proposed in this paper, which optimizes the stiffener layout based on an equivalent model.
Journal ArticleDOI
Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach
TL;DR: In this article, a CNN-based surrogate is used for manufacturing feasibility assessment for components to be formed using the Hot Forming and cold die quenching (HFQ) process.
Journal ArticleDOI
Dynamic analysis of prestressed variable stiffness composite shell structures
TL;DR: In this paper , a multi-domain Ritz method for eigenfrequency, transient and dynamic instability analysis of prestressed variable stiffness laminated doubly-curved shell structures is presented.
References
More filters
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Related Papers (5)
Accelerated optimization of curvilinearly stiffened panels using deep learning
Bionic design and verification of high-precision machine tool structures
Suna Yan,Baotong Li,Jun Hong +2 more