Evidence map›Paper›PMID 36772313›Full record

ArticleSensors (Basel, Switzerland)2023

Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument.

Zhixiang Xing, Xianxian Wang, Mengshuai Ning, Cunfu He, Xiucheng Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Zhixiang XingFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Xianxian WangFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Mengshuai NingFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Cunfu HeFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Xiucheng LiuFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0002-1067-8734

Funding

National Key R&D Program of China 2018YFF01012300National Natural Science Foundation of China 11527801National Natural Science Foundation of China 11872081National Natural Science Foundation of China 12122201
6 · The paper itself

Abstract

The combination of multifunctional micromagnetic testing and neural network-based prediction models is a promising way of nondestructive and quantitative measurement of steel surface hardness. Current studies mainly focused on improving the prediction accuracy of intelligent models, but the unavoidable and random uncertainties related to instruments were seldom explored. The robustness of the prediction model considering the repeatability of instruments was seldom discussed. In this work, a self-developed multifunctional micromagnetic instrument was employed to perform the repeatability test with Cr12MoV steel. The repeatability of the instrument in measuring multiple magnetic features under both static and dynamic conditions was evaluated. The magnetic features for establishing the prediction model were selected based on the consideration of both the repeatability of the instrument and the ability of magnetic features in surface hardness evaluation. To improve the robustness of the model in surface hardness prediction, a modelling strategy considering the repeatability of the instrument was proposed. Through removing partial magnetic features with higher mean impact values from input nodes, robust evaluation of surface hardness in Cr12MoV steel was realized with the multifunctional micromagnetic instrument.

Indexed as

micromagnetic testingquantitative predictionrepeatabilityrobustnesssurface harness

Identifiers

PMID36772313
PMCPMC9921488

What OpenQuestion holds

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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.