Machine Learning-Assisted Square-Spot Laser Surface Reshaping for Sidewall Roughness Control of LDED Ti-6Al-4V Thin-Walled Structures.

Wenjun Yu, Fei Li, Yanze Wang, Pengpeng Xiong, Xiaohu Guan, Feiyue Lyu, Jicheng Chen

Abstract read

In one paragraph

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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5 · Who and what money

Authors and funding

7 authors.

Wenjun YuChengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610091, China.

Fei LiChengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610091, China.

Yanze WangCollege of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Pengpeng XiongCollege of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Xiaohu GuanCollege of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Feiyue LyuChengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610091, China.

Jicheng ChenCollege of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.ORCID 0000-0001-5513-0310

Funding

Chengdu Aircraft Industrial (Group) Co., Ltd. JY-25-A33-0488

6 · The paper itself

Abstract

Laser directed energy deposition (LDED) can fabricate Ti-6Al-4V thin-walled structures efficiently, but the deposited sidewalls usually contain adhered particles, layer steps, and waviness that limit surface quality. This study combined square-spot laser surface reshaping with machine learning-assisted parameter design to control sidewall roughness. Sixteen single-factor experiments were first conducted to clarify the effects of laser power, scanning speed, spot overlap ratio, and scan number. An 80-sample dataset was then established to train and compare random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGBoost) models, and SHapley Additive exPlanations (SHAP) were used to interpret feature contributions. RF showed the best predictive performance, with R

Indexed as

laser directed energy depositionmachine learningsquare-spot laser reshapingsurface qualitythin-walled structures

Identifiers

PMID42654560

DOI10.3390/ma19163406

PMCPMC13514719

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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.