Evidence map›Paper›PMID 42500740›Full record

ArticleNational science review2026

Self-evolving experimental platform for 3D sand printing.

Songtao Hu, Xiantong Zhang, Kaiming Tang, Haoran Li, Wenhui Lu, Zipeng Wang, Yinjun Deng, Bo Zhang, Xiaobao Cao, Xi Shi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Songtao HuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID https://orcid.org/0000-0002-8405-3788
Xiantong ZhangState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Kaiming TangState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID https://orcid.org/0009-0004-7121-8709
Haoran LiState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Wenhui LuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID https://orcid.org/0009-0002-3535-723X
Zipeng WangState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Yinjun DengState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Bo ZhangSchool of Mechanical Engineering, Ningxia University, Yinchuan 750021, China.
Xiaobao CaoGuangzhou National Laboratory, Guangzhou 510320, China.
Xi ShiState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Zhike PengState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Three-dimensional (3D) sand printing revolutionizes casting mold fabrication while confronting nonlinear high-dimensional optimization challenges stemming from variable combinations and objective conflicts. Here, we develop Ex3D sand printing (Ex3DSP), a self-evolving experimental platform combining robotic 'can-do' capabilities with artificial intelligence 'can-think' cognition to establish a'do-while-thinking' self-evolving experimentation paradigm. Addressing a three-objective, three-variable high-dimensional optimization problem, our Ex3DSP discovers the Pareto front with a 1148-fold reduction in experimental workload. The platform provides two solution types (objective-optimal and balanced) with interpretability and reveals underlying mechanisms (variable-objective interaction and sand mold fracture behavior). Moreover, these solutions are applied to guide real-world casting parameter selection, yielding castings with up to 385% improved physicochemical and mechanical performances. This work demonstrates a self-evolving experimentation paradigm for 3D sand printing, offering both an intelligent additive manufacturing roadmap and pre-industrial screening potential.

Indexed as

artificial intelligenceautomated experimentationcastingsand printing

Identifiers

PMID42500740
PMCPMC13397575

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.