scholarly journals Conduction mode laser welding with beam shaping using a deformable mirror

2022 ◽  
Vol 148 ◽  
pp. 107718
Author(s):  
Yongcui Mi ◽  
Satyapal Mahade ◽  
Fredrik Sikström ◽  
Isabelle Choquet ◽  
Shrikant Joshi ◽  
...  
Keyword(s):  
Laser Welding ◽  
Beam Shaping ◽  
Deformable Mirror ◽  
Mode Laser ◽  
Conduction Mode

Modeling of Conduction Mode Laser Welding Process For Feedback Control

1999 ◽  
Vol 122 (3) ◽  
pp. 420-428 ◽  
Author(s):  
Fuu-Ren Tsai ◽  
Elijah Kannatey-Asibu,
Keyword(s):  
Feedback Control ◽  
Laser Welding ◽  
Weld Pool ◽  
Vision System ◽  
Plate Thickness ◽  
Welding Process ◽  
Laser Machine ◽  
Mode Laser ◽  
Conduction Mode ◽  
Laser Welding Process

The response of conduction mode laser weld pool dimensions, specifically weld width, to a step change in power input has been modeled using two-dimensional heat flow analysis. The goal is to develop a simplified model suitable for feedback control. The weld pool geometry was approximated by a tear-drop shape. The workpiece thermal properties were assumed to be lumped and temperature-independent. The result was a first-order weld pool thermal model. A series of experiments was performed using different welding conditions (plate thickness, step power changes, and welding speeds) to validate the model. The weld pool image was recorded using a vision system and digitized. The process time constant as calculated by the model was of the order of 10−4 seconds. The response of the laser machine, estimated by the least squares method, was found to be about 10−2 seconds, which is much slower than that of the weld pool. Thus, within the constraints of the assumptions on which the model is based, the entire laser welding process is considered to be dominated by the laser machine dynamics. [S1087-1357(00)00502-5]

Two-dimensional modeling of conduction-mode laser welding

1984 ◽  
Author(s):  
Anthony J. Russo ◽  
David A. Benson ◽  
G. Ronald Hadley ◽  
Henry C. Peebles
Keyword(s):  
Laser Welding ◽  
Two Dimensional ◽  
Mode Laser ◽  
Dimensional Modeling ◽  
Conduction Mode

scholarly journals Beam Shaping by a Magnetic Fluid Deformable Mirror With Curved Trajectory for Optical Tweezer System

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 121645-121653
Author(s):  
Xiang Wei ◽  
Yongjun Yang ◽  
Dziki Mbemba ◽  
Feng Li ◽  
Zhizheng Wu ◽  
...  
Keyword(s):  
Magnetic Fluid ◽  
Optical Tweezer ◽  
Beam Shaping ◽  
Deformable Mirror

scholarly journals L-Statistical Analysis of Sound Signal Acquired from Pulse Mode Laser Welding for Characterising Weld Geometry

2019 ◽  
Vol 16 (3) ◽  
pp. 6987-7006
Author(s):  
M. F. M. Yusof ◽  
M. Ishak ◽  
M. F. Ghazali
Keyword(s):  
Laser Welding ◽  
Focal Length ◽  
Welding Process ◽  
Pulse Mode ◽  
Sound Signal ◽  
Boron Steel ◽  
Statistical Features ◽  
Absolute Deviation ◽  
Mode Laser ◽  
Weld Geometry

Many ongoing studies have proven that statistical features extracted from the acquired sound during a laser welding process significantly yield some pictures on the weld condition, including weld geometry. However, a considerable amount of studies has underlined the use of common statistical features in which they are restricted to some limitations when dealing with non-stationary random sound signal. In the present study, the main aim is to study the correlation between the L-statistical features trend of the sound amplitude distribution with respect to the change in weld geometry during pulse mode laser welding compared to common statistical features. In an attempt to achieve this goal, a pulse mode laser welding was subjected to 22MnB5 boron steel with variations in the peak power, pulse width, and focal length. Meanwhile, the sound signal was acquired during the process, with standard deviation, interdecile range, mean absolute deviation, L-Cv (scale), and L-kurtosis extracted from the analysis. The degree of correlation between these statistical features and weld geometry was compared from the R-square value. According to the reported results, L-kurtosis yielded the strongest correlation with both weld penetration depth and bead width compared to the remaining five statistical features. This showed that the use of L-statistical features was significant to improve the correlation between sound signals and weld geometry.

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