Extract the aluminum alloy MIG welding pool edge with Otsu threshold selection based on the genetic

algorithmic

Abstract—It is one of the important methods to detect the image edge of the molten bath for gain the characteristic information including the maximum width of molten bath, the half length of the molten bath and the rear angle of the molten bath etc It is one of the important methods to achieve automatic control for welding process that the visual sensing technology is used to gain the pictorial information of molten bath and make processing.This paper introduced the structure of experimental system and algorithm, uses the Otsu to realize the detection of aluminum alloy MIG welding molten bath edge ,and compared with the Sobel operator and the Canny operator molten bath edge detection result , the experimental result indicated that it is completely feasible using genetic algorithmic to realize the detection of welding pool edge based on threshold value selection, and the molten bath edge information obtained is smooth, continual, without the break point and the noise.

Keywords- Otsu ; threshold value selection ; aluminum alloy MIG welding ; genetic algorithm ;

I. INTRODUCTION It is one of the important methods to achieve automatic

control for welding process that the visual sensing technology is used to gain the pictorial information of molten bath and make processing.At the same time ,the visual sensing technology has been applied preliminarily in welding of many materials such as carbon steel, stainless steel, aluminum and so on[1 2] . The purpose of processing the molten bath imagery is to gain the characteristic information including the maximum width of molten bath, the half length of the molten bath and the rear angle of the molten bath etc, and standardize welding and realize the welding process control through establishment of the molten bath characteristic and the mapping models of the goal objects including depth of fusion or melt width.Therefore it is one of the important methods to detect the image edge of the molten bath for obtaining the above characteristic information. During the welding process of the aluminum alloy MIG, the bigger welding background current is needed to guarantee the stave function of the negative pole and realize the normal welding of the aluminum alloy. Due to the power source interference of the

contravariant welding, the transient interference of the dissolving drop during MIG welding process, poor contrast ratio between the molten bath and the emarcation line of parent metal gradient difference and the electromagnetic interference of welding electric arc, the background noise of the image of the welding molten bath is very clear[3].

Threshold value division is one of the basic issues during the image processing, which plays the vital role in the image analysis and the image recognition.Among them, threshold value selection is the key of the threshold value division. The common methods include enumerate method, stochastic search method, gradient descent method, simulated annealing method and so on,.There are some problems concerning the optimal solution in these methods such as low efficiency , easy to fall into part and so on[4]. In contrast, GA can carry out the parallel search from the overall situation, which have the characteristics including simple, fast, strong stability and so on[5] .According to the analysis of the image characteristic of the molten bath, this article proposed the method of detection the welding pool edge of aluminum alloy MIG welding based on the threshold value selection among the genetic algorithmic.

II. COMPOSITION OF THE EXPERIMENT SYSTEM The vision sensing system of aluminum alloy MIG

welding molten bath is mainly composed of a camera of Panasonic CP2230 CCD (charge coupled device) and a compound light filter head with optics components including neutral cut-rays sheet, narrow band filter, endothermic film, , an image gathering card of TianMin SDK22000, an industry control computer of ASUSTeK 610 and so on.. The electric welding machine uses German DELEXVRD MIG2400L digital control electric welding machine, which can carry out the adjustment of many kinds of pulse current. For the Cloos welding robot and the welding work table, when the work table moves, the relative position between CCD and the welding torch keeps steady.

Ming-liang WU, Li-li WEI and Lin-hai XIAO Key Laboratory of Digital Manufacturing Technology

and Application,The Ministry of Education Lanzhou University of Technology Lanzhou , Gansu

Province,730050, China kaixin310178093@163.com;

Shu-rong YU , Jian-kang HUANG State Key Laboratory of Gansu Advanced Non-ferrous

Materials Lanzhou University of Technology Lanzhou , Gansu

Province,730050, China

978-1-4244- 7618-3 /10/$26.00 ©2010 IEEE

III. PRINCIPLE OF THE GENETIC ALGORITHM The genetic algorithm is the computation model to

simulate the biological evolution process in nature. It is based on the principle of survival of the fittest, to carry on the operations repeatedly which based on the genetics to the communities which are needed to be optimized, generating new optimized community continuously..And at the same time the most superior individuals are searched in the optimized community by the way of overall parallel search so as to obtain the optimal solution which meets the requirements[6 7].

The genetic algorithm generally contains 4 main operations: Coding, selection, crossover and variation.

1) Coding. Every individual of the initial community is indicated with a binary string. Because in the digital image processing, the threshold value selection is usually the integers between 0 and 255, therefore it is convenient to use binary codes of 8 bits.

2) Selection. The fittest individuals, namely the best individuals in the population are unconditionally duplicated into the next new generation (use the optimal preservation strategy), then the roller mass method is used as the selection method. the gene string which has big adaptability will be inherited to the next generation due to its big chance of being selection; On the contrary, the gene string which has small adaptability will be eliminated due to its small, chance of being selected.

3) Crossover. Regarding the load bearing 2 variable the single-spot crossover only can change one of the parent value each time and the search route is only horizontal or vertical, while the multi-spot crossover can change 2 parent values and the search route is random oblique line. The the search speed can be increased obviously. However, the consistent crossover (it is also called uniform overlapping) can form any new patterns in the principle, which is helpful for searching the new region of solution space. But it also has the possibility to destroy the pattern with low-order, short distance, high adaptability. Therefore, this article uses two-spot crossover..

4) Variation. The genetic algorithm based on big mutation is used here, and its basic approach is: When all individuals in one generation are assembled together, carry out a variation operation with a probability which is far bigger than that of the usual variation.The variation with the big mutation probability is able to generate many new individuals randomly and independently, thus the entire population can be separated from early-maturing.

IV. EXTRACT THE IMAGE EDGE OF MOLTEN BATH WITH THE GENETIC ALGORITHMION

The purpose of processing the molten bath is to examine the molten bath edge so as to obtain the information concerning the maximum width , half length and rear angle of the molten bath related with the welding penetration ,melt width, and realize the welding process sensing.

Figure 1 is the CCD grey image and three-dimensional contour chart of grayscale, chart 1a is the grey image (it is noted G (x, y)), chart chart1b is the three dimensional contour

of chart 1a. In chart 1b, the hathpace is the welding molten bath with the big grey level, the wide surface is the area with small,grey level,which is represented by the dark area in chart 1a .From chart 1b we can discover that the key is to choose a precise threshold value.to extract the molten bath from the image and get the molten bath edge.

(a) original grey image

(b) three-dimensional contour

Fig. 1 CCD grey image and three-dimensional contour The selection of threshold value is a basic question in

image processing. At present ,the methods commonly used include Otsu method, co-occurrence matrix, histogram and probability relaxation and so on . The Otsu has been applied in reality widely because of its simple,computation stable and effective, Its basic approach is to divide the picture elements into two categories of C1 and C2 with threshold value according to the grey level. C1 is the target image and C2 is the background image and Otsu is f (M) = W1 (M) W2 (M) [U1 (M) - U2 (M)] 2(1)

Among them, M is a candidate threshold value between 0 and 255, W1 (M) is the picture element number in C1’s; W2 (M) is the picture element number in C2’; U1 (M) is the average grey level of all picture element number in C1; U2 (M) is the average grey level of all element number in C2.This article uses the Otsu method to realize the detection of aluminum alloy MIG welding molten bath edge. Figure 2 shows the flow process of extracting molten bath edge with threshold value selection in genetic algorithm . First coding is carried out. For gray level image, there are 256 gray levels from 0 to 255. Therefore division threshold value of the gray level is coded as 0, 1 binary code string of 8 bit; According to the Otsu method the fitness function of the genetic algorithm is determined, like formula (1). The termination condition is determined by the iterative times or the fitness ratio. The entire detection process of molten bath edge shall

be completed in software platform with CPU of AMD 64 3200+, the memory of 512M.

Fig. 2 Edge detection flow chart with genetic algorithmic

image Chart 3a is the result of the edge examination based on

the Sobel operator function in the Matlab software[8] ,the computing time is 32ms and the examination result of the molten bath edge is not very ideal, bring into a lot of useless information..Chart 3b shows the result of the edge examination based on the Canny operator function brought in Matlab software[8], and the computing time is 203 ms.The examination result of the edge is good, but this algorithm examination result cannot eliminate the image edge of welding blow lamp. The image edge is not continual and there are other information points mixed in the image..Chart 3c shows the result of the edge examination with the threshold value selection in genetic algorithm, and the individual number is 20. The division threshold value of the last image M = 135, and the computing time is 3. 98s. The maximum heredity algebra is 40, the generation gap is 0. 9, overlapping rate is 0. 7. From the analysis of the formula (1) ,we know the classification is carried out on the entire image. From chart 1b, we can find the classification of the entire picture results in calculating the majority of irrelevant regions, which increased the weight of the non-molten bath area. Therefore in the image region, select G (40, 75), G (150, 155), G (150, 75) and G (150, 155) as the rectangular regions of the apex .Chart 3d is the partial genetic algorithm threshold values with the individual number of 15, the maximum heredity algebra of 30, , the generation gap of 0. 95, overlapping rate of 0. 8, image division valve value M = 155, the computing time of 219 ms.From chart 3c, we can see, continual molten bath edge with high quality can be obtained through threshold value selection in the genetic algorithm and the the influence of noise and negative pole atomizes area can be eliminated, specially ,the partial threshold value has quick operating speed, which can satisfy the basic requirements of real-time examination and control.

(a)Sobel operater (b)Canny operater

(c)whole genetic algorithm (d)part genetic algorithm

threshold value threshold value Fig. 3 Result of edge detection of welding pool with

different algorithm

V. CONCLUSION 1) It is completely feasible using genetic algorithmic to

realize the detection of welding pool edge based on threshold value selection, and the molten bath edge information is smooth, continual, without the break point and the noise.

2) The threshold value division in partial genetic algorithm method, has superiority of small computation load, quick computation speed etc, and it has practical value to the real-time examination andcontrol during the aluminum alloy MIG welding process

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