A Study of tow-Power Density Laser Welding Process with Evolution of me Surface

2004 ◽  
Vol 28 (10) ◽  
pp. 1202-1209
Author(s):  
Eung-Ji Ha ◽  
Woo-Seung Kim
Author(s):  
T. Suthakar ◽  
K. R. Balasubramanian ◽  
K. Sankaranarayanasamy

Laser welding process is the high energy beam welding process which is very much used for thin and thick section industrial applications. The weld bead profile relies on the selection of process parameter. Due to its high power density optimal selection of process parameters is vital. In this research the optimization of the input process parameters namely power density (PD), welding speed (WS), beam angle (BA) and gas flow rate (GFR) on the response bead width (BW), depth of penetration (DOP) and depth to width aspect ratio (D/W) is analyzed. As the process parameters are highly non-linear, quadratic equations are generated for determining the desired response. The experimental trials are performed on an AISI 304 austenitic stainless steel using the four-factor-five-level central composite experimental design (CCED). Optimization of process parameters is performed using the desirability approach and the results obtained from the mathematical model is compared with the experimental results and found to be in agreement. The target fixed for the weld is to determine the optimal process parameters for the minimization of bead width and the maximization of depth of penetration and depth to width aspect ratio.


2012 ◽  
Vol 201-202 ◽  
pp. 91-94
Author(s):  
Yan Xi Zhang ◽  
Xiang Dong Gao

Configuration of a molten pool is related to the laser welding quality. Analyzing the configuration of a molten pool is important to monitor the laser welding process. This paper proposes a method of segmentation of a molten pool and its shadow during high power disk laser welding, consequently provides the groundwork for reconstruction of the molten pool and analysis of welding quality. Subsection linear stretching histogram equalization was applied to enhance the contrast of the original images firstly, and then edge detection was used to highlight the edges. After that we used the morphology filtering method to produce the segmentation mask, and then combined the mask with the original images to get the final segmentation results. Also, the proposed method was compared with other traditional methods. The experimental results showed that our method not only could give better segmentation results and process large quantities images automatically, but also overcame the less-segmentation problems of traditional methods.


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