In-Process Ultrasonic Monitoring and Nugget Geometry Evaluation in Resistance Spot Welding of Stainless Steel.

Jianan Lv, Xiaopeng Gu, Juan Dong, Guocheng Xu, Xiaoyan Gu, Jinjiang Li, Jialiang Hao

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Article in Materials (Basel, Switzerland), 2026. 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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7 authors.

Jianan LvKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Xiaopeng GuKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Juan DongKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Guocheng XuKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Xiaoyan GuKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Jinjiang LiKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Jialiang HaoKey Laboratory of Automobile Materials of Ministry of Education, College of Materials Science and Engineering, Jilin University, Changchun 130025, China.

Funding

Changchun City Science and Technology Development Plan Project 2024GD05

6 · The paper itself

Abstract

Post-weld evaluation does not readily capture the dynamic evolution of nugget formation in resistance spot welding. In this study, an embedded ultrasonic probe was used to acquire A-scan signals during resistance spot welding of stainless steel. Consecutive signals were assembled into M-type ultrasonic images to analyze echo changes across the thermal cycle and under different heat inputs. Using 12 specimens with metallographic measurements of both nugget diameter D and thickness H, five- and three-dimensional acoustic features were extracted for D and H prediction, respectively. Random forest (RF) regression models were established and evaluated using leave-one-out cross-validation (LOOCV). The M-type images showed the streak evolution of interface echoes, back-wall echoes, and nugget-related echoes, reflecting differences in acoustic responses across welding stages and nugget states. The coefficient of determination (R

Indexed as

A-scan signalin-process ultrasonic monitoringM-type ultrasonic imagerandom forestresistance spot welding

Identifiers

PMID42795221

DOI10.3390/ma19184001

PMCPMC13608464

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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.