Evidence map›Paper›PMID 41156916›Full record

ArticleMaterials (Basel, Switzerland)2025

Research on the Influence of Temperature on the Stress-Electromagnetic Characterization of Radiation-Resistant Robotic Drive Steel Cables.

Tong Wu, Linlong Ding, Yingchun Chen, Jie Yang, Renjie Nie, Fengjuan Chen, Chuan Zhang, Jiahao Wu

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 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

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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. Review
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

8 authors.

Tong WuCNNC Hexin Information Technology (Beijing) Co., Ltd., Beijing 100091, China.
Linlong DingCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Yingchun ChenCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Jie YangCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Renjie NieCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Fengjuan ChenCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Chuan ZhangCNNC Hexin Information Technology (Beijing) Co., Ltd., Beijing 100091, China.
Jiahao WuCNNC Hexin Information Technology (Beijing) Co., Ltd., Beijing 100091, China.ORCID 0000-0002-5253-6961

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the operation of steel cable-driven radiation-resistant robots in nuclear industrial environments, the tensile force of a steel cable is influenced by temperature variations, which can cause significant detection errors. To address this problem, this study proposes a temperature-compensated axial force characterization method for steel cables based on the magnetoelastic effect, aiming to ensure the measurement accuracy of magnetoelastic sensors. The principle of the magnetoelastic measurement method involves magnetizing the steel cable. When subjected to tensile forces, the magnetization characteristics of the steel cable change, thereby altering the detection signal of the magnetoelastic sensor. By analyzing the relationship between steel cable tension and variations in the detection signal, effective force measurement can be achieved. First, experiments are conducted to investigate the influence of temperature on the detection signals of a magnetoelastic sensor under zero-load conditions. Then, additional tests are performed to examine the combined effects of a tensile force and temperature on the sensor's signals. Finally, based on the experimental data, axial force prediction models are constructed using both surface fitting and a backpropagation neural network (BPNN). The results demonstrate that, compared to the resistance values, inductance exhibits superior stability under temperature variations. In the temperature range of 20-50 °C, the inductance variation is approximately 0.15 μH, which indicates improved suitability for characterizing the axial force of steel cables. It is also shown that under isothermal conditions, the inductance increases linearly with the applied tensile force, exhibiting a slope of approximately 0.025 μH/kN. Both the surface fitting-based and BPNN-based axial force prediction models demonstrate high accuracy, with absolute prediction errors consistently below 5% compared to actual data.

Indexed as

backpropagation neural network modelmagnetoelastic effectsensorsteel cable stresstemperature

Identifiers

PMID41156916
PMCPMC12566012

What OpenQuestion holds

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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.