Combined contour error control method for five-axis machine tools based on digital twin.

Liuquan Wang, Ruijie Yang, Shisheng Lv, Zhiqi Yang, Shuwei Xin, Yanqiang Liu, Qiang Liu

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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.

A digital twin-assisted fault diagnosis method for stage lifts based on lightweight multi-scale convolution and broadcast self-attention mechanisms.Scientific reports · 2026
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

7 authors.

Liuquan WangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.

Ruijie YangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.

Shisheng LvSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.

Zhiqi YangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.

Shuwei XinSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.

Yanqiang LiuSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China. liuyanqiang@buaa.edu.cn.

Qiang LiuSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China. qliusmea@buaa.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Contour error is a critical factor influencing machining quality. This paper proposes a combined contour error control method for five-axis machine tools based on digital twin. The proposed method combines pre-compensation implemented in digital twin with feedback control in the real-time controller. After obtaining the tool path input, the digital twin performs interpolation and applies model predictive pre-compensation control to the interpolated commands to control modeled errors. The pre-compensated commands and interpolation data are sent to the real-time controller where contour error is estimated and controlled in each control cycle through feedback control to control unmodeled errors. Using the S-shaped curve as the test case, the maximum tool tip position contour error is reduced by 77.78%, with an average reduction of 83.90%. The maximum tool orientation contour error decreased by 79.05%, with an average reduction of 86.66%. The experimental results demonstrate that the proposed method significantly reduces tool contour error.

Indexed as

Contour error controlDigital twinFive-axis machine toolModel predictive pre-compensation control

Identifiers

PMID40404807

DOI10.1038/s41598-025-02047-2

PMCPMC12098878

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.