scholarly journals Deep-learning approach for predicting laser-beam absorptance in full-penetration laser keyhole welding

2021 ◽  
Author(s):  
Sehyeok Oh ◽  
Hyeongwon Kim ◽  
Kimoon Nam ◽  
Hyungson Ki
Keyword(s):  
Deep Learning ◽  
Laser Beam ◽  
Learning Approach ◽  
Keyhole Welding ◽  
Full Penetration ◽  
Laser Keyhole Welding

Surface tension generated defects in full penetration laser keyhole welding

2014 ◽  
Vol 26 (1) ◽  
pp. 012006 ◽  
Author(s):  
Ingemar Eriksson ◽  
John Powell ◽  
Alexander F. H. Kaplan
Keyword(s):  
Surface Tension ◽  
Keyhole Welding ◽  
Full Penetration ◽  
Laser Keyhole Welding

Power density effect on the laser beam-induced eruption of spatters in fiber laser keyhole welding

2022 ◽  
Vol 147 ◽  
pp. 107651
Author(s):  
Jianglin Zou ◽  
Baoqi Zhu ◽  
Gaolei Zhang ◽  
Shihui Guo ◽  
Rongshi Xiao
Keyword(s):  
Laser Beam ◽  
Fiber Laser ◽  
Power Density ◽  
Density Effect ◽  
Keyhole Welding ◽  
Laser Keyhole Welding

scholarly journals Interaction between the laser beam and keyhole wall during high power fiber laser keyhole welding

2017 ◽  
Vol 25 (15) ◽  
pp. 17650 ◽  
Author(s):  
Jianglin Zou ◽  
Na Ha ◽  
Rongshi Xiao ◽  
Qiang Wu ◽  
Qunli Zhang
Keyword(s):  
Laser Beam ◽  
Fiber Laser ◽  
High Power ◽  
Keyhole Welding ◽  
Laser Keyhole Welding

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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