Journal ArticleDOI
Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption
TLDR
In this article , the authors provide a comprehensive review on the recent advances in additively manufactured materials and structures as well as their mechanical properties with an emphasis on energy absorption applications and highlight significant challenges and future directions in this area.About:
This article is published in International Journal of Mechanical Sciences.The article was published on 2023-01-01. It has received 18 citations till now. The article focuses on the topics: 3D printing & Absorption (acoustics).read more
Citations
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Additive Manufacturing-Oriented Concurrent Robust Topology Optimization Considering Size Control
Zeshang Li,Lei Wang,Tangqi Lv +2 more
TL;DR: In this paper , a concurrent robust topology optimization method considering manufacturing factors and hybrid uncertainties is proposed, where the minimum size is controlled based on the boundary gradient under the level set frame and the influence of random defects on the mechanical property is analyzed via the homogenization method.
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Gradient scaffolds developed by parametric modeling with selective laser sintering
TL;DR: In this article , a parameterized method for developing scaffolds with customized compression modulus distribution was proposed based on selective laser sintering (SLS) and the Primitive triply periodic minimal surfaces (TMPS).
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Research of Surface Oxidation Defects in Copper Alloy Wire Arc Additive Manufacturing Based on Time-Frequency Analysis and Deep Learning Method
TL;DR: In this paper , the defects of surface oxidation in copper alloy wire arc additive manufacturing (WAAM) were detected using voltage sensor using deep learning method and continuous wavelet transform (CWT) method.
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On the crashworthiness of thin-walled multi-cell structures and materials: State of the art and prospects
TL;DR: In this paper , a comprehensive overview of recent advances in the development of thin-walled multi-cell structures and materials (TWMCSM) for crashworthiness and protection applications is provided.
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Crushing behaviour of corrugated tilted honeycomb core inspired by plant stem
TL;DR: In this article , a new corrugated tilted honeycomb (CTH) core for sandwich structure is proposed by implementing the corrugation and tapered shape inspired by plant stems and its crushing responses and energy absorption capacity are numerically investigated by using finite element software LS-DYNA.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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.
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A variational approach to the theory of the elastic behaviour of multiphase materials
Zvi Hashin,S. Shtrikman +1 more
TL;DR: In this paper, the authors derived upper and lower bounds for the effective elastic moduli of quasi-isotropic and quasi-homogeneous multiphase materials of arbitrary phase geometry.
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Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures
TL;DR: In this paper, a cumulative-damage fracture model is introduced which expresses the strain to fracture as a function of the strain rate, temperature and pressure, and the model is evaluated by comparing computed results with cylinder impact tests and biaxial (torsion-tension) tests.
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Artificial neural networks: a tutorial
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.