Evidence map›Paper›PMID 42000725›Full record

ArticleScientific reports2026

Bionic optimization design of bed for high-precision turn-milling compound machine tool based on the honeycomb structure.

Xu Bing, Chang Huijie, Li Haozhen, Wang Xuanyi, Deng Xiaolei, Wu Hongyi

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Xu BingTaizhou Vocational College of Science and Technology, HuangYan, 318020, China.
Chang HuijieKey Laboratory of Intelligent Manufacturing for Aerodynamic Equipment of Zhejiang Province, Quzhou University, Quzhou, 324000, China.
Li HaozhenKey Laboratory of Intelligent Manufacturing for Aerodynamic Equipment of Zhejiang Province, Quzhou University, Quzhou, 324000, China.
Wang XuanyiCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310032, China.
Deng XiaoleiCollege of Mechanical Engineering, Jiaxing University, Jiaxing, 314001, China. dxl@zju.edu.cn.
Wu HongyiKey Laboratory of Intelligent Manufacturing for Aerodynamic Equipment of Zhejiang Province, Quzhou University, Quzhou, 324000, China. 26014@qzc.edu.cn.

Funding

A Project Supported by Scientific Research Fund of Zhejiang Provincial Education Department Y202456018the National Natural Science Foundation of China 52175472
6 · The paper itself

Abstract

Aiming at improving the structural performance of the high-precision turning-milling combined machine tool bed, a multi-objective collaborative optimization framework that integrates bionic design principles and the Taguchi method is developed in this study. This framework first extracts the static and dynamic characteristics of the bed structure through finite element simulation. Then, inspired by the honeycomb configuration as a bionic prototype, it innovatively redesigns the internal rib layout. Subsequently, systematic optimization of key dimensional parameters is conducted via Taguchi orthogonal experiments, ultimately establishing a mathematical model with the comprehensive objectives of mass reduction, deformation suppression, and natural frequency enhancement. The results show that the quality of the optimized bed is reduced by 6.7%, the maximum deformation is decreased by 6.15%, and the fourth-order natural frequency is increased by 2.8%. It achieves lightweighting while significantly enhancing the static and dynamic performance of the structure, providing a feasible path for the green design and performance improvement of high-precision machine tools.

Indexed as

Bionic designHigh-precision turning and milling combined machine toolLightweight designMachine tool bedMulti-objective joint optimizationTaguchi method

Identifiers

PMID42000725
PMCPMC13249834

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.