Optimizing material and process parameters in laser engineered net shaping using liquid neural networks

Abstract This study introduces a physics-based Liquid Neural Network (LNN) framework for optimization of material and process parameters in Laser Engineered Net Shaping (LENS). The model combines neural differential equations with dynamic liquid weight mechanism, and enables adaptive learning under...

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主要な著者:	Haijiang Tian, Ismail Saad, Tianshu Chen, Teng Gao, Houpin Yoong, Bih Lii Chua
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2026-05-01
シリーズ:	Scientific Reports
主題:	LNN—Liquid Neural Network LENS—Laser Engineered Net Shaping CUI—Cladding Uniformity Index RL—Reinforcement Learning RSM—Response Surface Methodology PR—Polynomial Regression
オンライン･アクセス:	https://doi.org/10.1038/s41598-026-52507-6
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