Prediction of Deformation-Induced Martensite Start Temperature by Convolutional Neural Network with Dual Mode Features.

Chenchong Wang, Da Ren, Yong Li, Xu Wang, Wei Xu

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing 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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chenchong WangState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China.

Da RenState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China.

Yong LiState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China.

Xu WangSchool of Mechanical Engineering, Liaoning Petrochemical University, Fushun 113001, China.

Wei XuState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China.

Funding

National Natural Science Foundation of China 52171109National Natural Science Foundation of China U1808208

6 · The paper itself

Abstract

Various models were established for deformation-induced martensite start temperature prediction over decades. However, most of them are empirical or considering limited factors. In this research, a dual mode database for medium Mn steels was established and a convolutional neural network model, which considered all composition, critical processing information and microstructure images as inputs, was built for Msσ prediction. By comprehensively considering composition, processing and microstructure factors, this model was more rational and much more accurate than traditional thermodynamic models. Also, by the full use of images information, this model has stronger ability to overcome overfitting compared with various traditional machine learning models. This framework provides inspiration for the similar data analysis issues with small sample datasets but different data modes in the field of materials science.

Indexed as

deep learningdeformation-induced martensite transformationdual mode datamicrostructuresteels

Identifiers

PMID35629523

DOI10.3390/ma15103495

PMCPMC9144313

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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.