Machine-learning-guided cross-material process optimization of laser-assisted robotic roller forming

Junying Min

doi:10.1016/j.cirpj.2026.05.003

Machine-learning-guided cross-material process optimization of laser-assisted robotic roller forming

2026-05-20

0

PRE

AI

闵

闵峻英 (Junying Min)

Y

Yujie Gu

J

Junpeng Qiu

Z

Zeran Hou *

DOI：10.1016/j.cirpj.2026.05.003

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Abstract

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• A machine learning-based framework (MIMOF) is established, showing robust cross-material generalization. • Challenges in LRRF parameter determination due to poor empirical data generalizability are addressed. • A multi-source dataset is compiled with detailed feature selection to enhance model generalizability. • Effects of key process parameters are systematically evaluated via an SVR-based mechanistic analysis.

Keywords:

machine learning

cross-material generalization

laser-assisted robotic roller forming

process optimization

support vector regression

Machine-learning-guided cross-material process optimization of laser-assisted robotic roller forming

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