Research on Fatigue Life Prediction Method of Spot-Welded Joints Based on Machine Learning.

Shanshan Li, Zhenfei Zhan, Jie Zou, Zihan Wang

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. Not yet cited in PubMed.

0numbers the graph read from it

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

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

4 authors.

Shanshan LiSchool of Mechanical and Electrical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

Zhenfei ZhanSchool of Mechanical and Electrical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

Jie ZouSchool of Mechanical and Electrical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

Zihan WangSchool of Mechanical and Electrical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spot-welding joints are widely used in modern industries, and their fatigue life is crucial for the safety and reliability of structures. This paper proposes a method for predicting the fatigue life of spot-welding joints by integrating traditional structural stress methods and machine learning algorithms. Systematic fatigue tests were conducted on Q&P980 steel spot-welding joints to investigate the influence of the galvanized layer on fatigue life. It was found that the galvanized layer significantly reduces the fatigue life of spot-welding joints. Further predictions of fatigue life using machine learning algorithms, including Random Forest, Artificial Neural Networks, and Gaussian Process Regression, demonstrated superior prediction accuracy and generalization ability compared to traditional structural stress methods. The Random Forest algorithm achieved an R

Indexed as

fatigue life predictionmachine learningrandom forestspot-welding jointsstructural stress method

Identifiers

PMID40805420

DOI10.3390/ma18153542

PMCPMC12348867

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.