Evidence map›Paper›PMID 39893044›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Fast Reverse Design of 4D-Printed Voxelized Composite Structures Using Deep Learning and Evolutionary Algorithm.

Mengtao Wang, Zaiyang Liu, Hidemitsu Furukawa, Zhuo Li, Yifei Ge, Yifan Xu, Zhe Qiu, Yang Tian, Zhongkui Wang, Ren Xu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Deformation Prediction of 4D-Printed Active Composite Structures Based on Data Mining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Mengtao WangDepartment of Electronic and Computer Engineering, Ritsumeikan University, Shiga, 525-8577, Japan.
Zaiyang LiuDepartment of Robotics, Ritsumeikan University, Shiga, 525-8577, Japan.
Hidemitsu FurukawaDepartment of Mechanical Systems Engineering, Yamagata University, Yonezawa, 992-8510, Japan.
Zhuo LiDepartment of Electronic and Computer Engineering, Ritsumeikan University, Shiga, 525-8577, Japan.
Yifei GeDepartment of Electronic and Computer Engineering, Ritsumeikan University, Shiga, 525-8577, Japan.
Yifan XuDepartment of Electronic and Computer Engineering, Ritsumeikan University, Shiga, 525-8577, Japan.
Zhe QiuDepartment of Robotics, Ritsumeikan University, Shiga, 525-8577, Japan.
Yang TianDepartment of Robotics, Ritsumeikan University, Shiga, 525-8577, Japan.
Zhongkui WangDepartment of Robotics, Ritsumeikan University, Shiga, 525-8577, Japan.ORCID https://orcid.org/0000-0003-4037-3306
Ren XuSchool of Medicine, Xiamen University, Xiamen, Fujian, 361100, China.
Lin MengDepartment of Electronic and Computer Engineering, Ritsumeikan University, Shiga, 525-8577, Japan.ORCID https://orcid.org/0000-0003-4351-6923

Funding

New Energy and Industrial Technology Development Organization JPNP14004
6 · The paper itself

Abstract

Designing voxelized composite structures via 4D printing involves creating voxel units with distinct material properties that transform in response to stimuli; however, optimally distributing these properties to achieve specific target shapes remains a significant challenge. This study introduces an optimization method combining deep learning (DL) and an evolutionary algorithm, focusing on a solvent-responsive hydrogel as the target material. A sequence-enhanced parallel convolutional neural network is developed and generated a dataset through finite element simulations. This DL model enables high-precision prediction of hydrogel deformation. Furthermore, a progressive evolutionary algorithm (PEA) is proposed by integrating the DL model to construct a DL-PEA framework. This framework supports rapid reverse engineering of the desired shape, and the average design time for specified target shapes is reduced to ≈3.04 s. The present findings illustrate how 4D printing of optimized hydrogel designs can effectively transform in response to environmental stimuli. This work provides a new perspective on the application of hydrogels in 4D printing and presents an efficient tool for optimizing 4D-printed voxelized composite structures.

Indexed as

4D printingdeep learningevolutionary algorithmhydrogelvoxel assembly

Identifiers

PMID39893044
PMCPMC11948029

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.