Continuous online flaws detection with photodiode signal and melt pool temperature based on deep learning in laser powder bed fusion

Author:

Mao Zhuangzhuang,Feng Wei,Ma Heng,Yang Yang,Zhou Jiangfan,Liu Sheng,Liu Yang,Hu Ping,Zhao Kai,Xie Huimin,Guo Guangping,Liu Zhanwei

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference46 articles.

1. Development of a defect-detection platform using photodiode signals collected from the melt pool of laser powder-bed fusion;Taherkhani;Addit. Manuf.,2021

2. Online in-situ monitoring of melt pool characteristic based on a single high-speed camera in laser powder bed fusion process;Ma;Appl. Therm. Eng.,2022

3. Energy and emissions saving potential of additive manufacturing: the case of lightweight aircraft components;Huang;J. Cleaner Prod.,2016

4. Research needs and recommendations on environmental implications of additive manufacturing;Rejeski;Addit. Manuf.,2018

5. The impact of metal additive manufacturing on the aerospace industry;Mohd Yusuf;Metals,2019

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