Mechanical and Shape-Memory Properties of TPMS with Hybrid Configurations and Materials
INTERNATIONAL JOURNAL OF SMART AND NANO MATERIALS(2024)
Abstract
Triply periodic minimal surface (TPMS) structures with excellent properties of stable energy absorption, light weight, and high specific strength could potentially spark immense interest for novel and programmable functions by combining smart materials, e.g. shape memory polymers (SMPs). This work proposes TPMS lattices with hybrid configurations and materials that are composed of viscoelastic and shape-memory materials with the aim to bring temperature-dependent mechanical properties and additional dissipation mechanisms. Different configurations and diverse materials of polylactic acid (PLA), fiber-reinforced PLA, and polydimethylsiloxane (PDMS) are induced, generating five types of TPMS lattices, including (Schoen’s I-WP) IWP uniform lattice, IWP lattice with density gradient, hybrid configurations, hybrid materials, and filled PDMS, which are fabricated by 3D printing. The fracture morphologies and the distribution of carbon fibers are demonstrated via scanning electron microscopy with a focus on the influence of carbon fiber on shape-memory and mechanical properties. Shape recovery tests are conducted, which proves good shape memory properties and reusable capability of TPMS lattice. The combined methods of experiments and numerical simulation are adopted to evaluate mechanical properties, which presents multi-stage energy absorption ability and tunable vibration isolation performances associated with temperature and hybridization designs. This work can promote extensive research and provide substantial opportunities for TPMS lattices in the development of functional applications.
MoreTranslated text
Key words
Triply periodic minimal surface,shape memory property,hybridization,multi-stage energy absorption
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1987
被引用159 | 浏览
2014
被引用176 | 浏览
2009
被引用499 | 浏览
2018
被引用146 | 浏览
2017
被引用461 | 浏览
2019
被引用475 | 浏览
2019
被引用43 | 浏览
2020
被引用119 | 浏览
2020
被引用34 | 浏览
2017
被引用57 | 浏览
2021
被引用27 | 浏览
2019
被引用223 | 浏览
2020
被引用263 | 浏览
2022
被引用28 | 浏览
2023
被引用22 | 浏览
2024
被引用13 | 浏览
2024
被引用11 | 浏览
2024
被引用5 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper