Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding-Reference-Cited by-同舟云学术

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Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding

Published:2024-09 Issue: Volume:300 Page:112116
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Ma Shuai,Chen Zhuyun,Zhang Ding,Du Yixian,Zhang Xiaoji,Liu Qiang^ORCID

Publisher

Elsevier BV

Reference34 articles.

1. Cross-attention-based multi-sensing signals fusion for penetration state monitoring during laser welding of aluminum alloy;Cao;Knowl. Based Syst.,2023

2. A review on heat affected zone softening of dual-phase steels during laser welding;Khan;J. Manuf. Process.,2023

3. Machine learning and deep learning based predictive quality in manufacturing: a systematic review;Tercan;J. Intell. Manuf.,2022

4. Optimal design for dual laser beam butt welding process parameter using artificial neural networks and genetic algorithm for SUS316L austenitic stainless steel;Liu;Opt. Laser Technol.,2020

5. Multi-objective optimization of laser welding process parameters: the trade-offs between energy consumption and welding quality;Li;Opt. Laser Technol.,2022

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