2005p3ps055-welding research institute

8/8/2019 2005p3ps055-Welding Research Institute

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A REPORT

ON

LASER VISION BASED SEAM TRACKING SYSTEM FOR WELDING

AUTOMATION

BY

Name of the Student: 1) Adithya Reddy Gangidi I.D. No.: 1) 2005P3PS055

AT

BHARAT HEAVY ELECTRICALS LTD, TRICHY

A Practice SchoolI station of

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

JULY, 2007


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A REPORT

ON

LASER VISION BASED SEAM TRACKING SYSTEM FOR WELDINGAUTOMATION

BY

Names of the Student I.D. No. Discipline

1) Adithya Reddy Gangidi 2005P3PS055 Electrical & Electronics

Prepared in partial fulfillment of the

Practice SchoolI Course

AT

BHARAT HEAVY ELECTRICALS LTD., TRICHY

A Practice SchoolI station of

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

JULY 2007


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BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE

PILANI (RAJASTHAN)

Practice School Division

Station: Bharat Heavy Electrical Ltd. Centre: Trichy

Duration: 7 weeks Date of Start: 24th

May, 07

Date of Submission: 15th

July 2007

Title of the Project: Laser vision based seam tracking system for welding automation

ID No.: 2005P3PS055

Name: Adithya Reddy Gangidi

Discipline: B.E. (Hons.) Electrical and Electronics

Name of Guide: Dr. S. Manoharan

Designation of Guide: Senior DGM, WRI, BHEL

Name of the PS Faculty: Dr. Srinivasan

Key Words: Welding automation, Image processing, Machine Vision

Project Areas: Machine Vision

Abstract: PC based machine vision systems can be used for online control of welding

process. In this project, a seam tracking system which uses high intensity laser source,

camera and a PC has been developed. The system takes images at regular intervals of timeand estimates the edge gap and root gap of a groove. The position of edge centre is thus

calculated and feedback is given to torch so as to keep it at the edge centre. Measures to

further reduce the processing time are proposed.

Signature of Students Signature of PS faculty

Date: Date:


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ABSTRACT:

Visual checking of weld joints by operators is no longer sufficiently reliable or cost effective.

Seam tracking technologies that use machine vision are often cost effective and they enhance

the productivity of the welding process. The project deals with designing such a seam

tracking system.

PC based machine vision systems can be used for online control of welding process. In this

project, a seam tracking system which uses high intensity laser source, camera and a PC has

been developed. The system takes images at regular intervals of time and estimates the edge

gap and root gap of a groove. The position of edge centre is thus calculated and feedback is

given to torch so as to keep it at the edge centre. Measures to further reduce the processing

time are proposed.

The images are processed using MATLAB 7.0 software and the corresponding feedback is

given to stepper motor. Programming is done with image acquisition, image processing and

data acquisition toolbox of MATLAB. The motor is interfaced to the parallel or serial port

through an interface circuit. The system is made user friendly trough a graphical user

interface. Image acquisition and control is achieved in 0.41 seconds, (in a PC with 1.66 GHz

Core Duo processor) which is well in agreement with the need.


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CONTENTS:

Topic Page No.

A. Cover Page 1

B. Title page 2

C. Certificate 3

D. Acknowledgement 4

E. Abstract 5

F. Contents 6

1. Introduction 72. MATLAB-Overview 83. Apparatus Used 104. The Algorithm 125. Implementation of Algorithm in MATLAB 126. Software for stepper motor control 177. Hardware interface between stepper motor and PC 178. Conclusion 229. References 2310.Appendix 2411.Index 31


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1. INTRODUCTION:The use of robots in manufacturing industry has increased rapidly during the past decade. Arc

welding is an actively growing area and many new procedures have been developed for use

with new lightweight, high strength alloys. One of the basic requirements for such

applications is seam tracking. Seam tracking is required because of the inaccuracies in joint

fit-up and positioning, war page, and distortion of the work piece caused by thermal

expansion and stresses during the welding. These effects cannot be compensated for by most

robotic welding machines; especially those using open-loop control, and frequently lead to

poor welding quality. As a consequence, to maintain weld integrity, automated seam tracking

based on real-time feedback is required.

Robotic welding sensors, researched during the past few years, have used a variety of

different techniques for seam tracking. The most commonly used techniques include acoustic,

magnetic, electrical, and mechanical methods. The electrical through-the-arc sensor based on

the welding arc properties is the dominant method, where the current (voltage) through the

weld arc is used to control the position of the welding torch. However, the preferred systems

are based on optical or visual sensors.

These vision based seam trackers have many advantages. The sensor system is less sensitive

to electrical and magnetic interferences from the welding arc compared to the traditional

through-the-arc method. Also the vision sensor can provide more information about the joint

than merely the seam position. It is possible for the same sensor system to achieve seam

tracking and also obtain dimensional parameters about the seam, during a single pass.

With the sensor mounted on the welding torch, the seam tracking and control can be online.

The problems encountered by the early vision system applications are the speed of processing

and the cost of the hardware to implement such systems. This has since become a less

important factor as developments have taken place in parallel processing which have

decreased both the cost of the vision system hardware and the processing time.

PC based machine vision systems can be used for online control of welding process. In this

project, a seam tracking system which uses high intensity laser source, camera and a PC has

been developed. The system takes images at regular intervals of time and estimates the edge


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gap and root gap of a groove. The position of edge centre is thus calculated and feedback is

given to torch so as to keep it at the edge centre. MATLAB platform is used for coding.

2. MATLAB:2.1 Introduction:

MATLAB is a high-performance language for technical computing. It integrates

computation, visualization, and programming in an easy-to-use environment where problems

and solutions are expressed in familiar mathematical notation.

Typical uses include

Math and computation

Algorithm development

Data acquisition

Modeling, simulation, and prototyping

Data analysis

exploration, and visualization

Scientific and engineering graphics

Application development, including graphical user interface building

MATLAB is an interactive system whose basic data element is an array that does not require

dimensioning. This allows you to solve many technical computing problems, especially those

with matrix and vector formulations, in a fraction of the time it would take to write a program

in a scalar non interactive language such as C or FORTRAN.

The name MATLAB stands for matrix laboratory. MATLAB was originally written to

provide easy access to matrix software developed by the LINPACK and EISPACK projects.

MATLAB has evolved over a period of years with input from many users. In university

environments, it is the standard instructional tool for introductory and advanced courses in

mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-

productivity research, development, and analysis.

MATLAB features a family of add-on application-specific solutions called toolboxes. Very

important to most users of MATLAB, toolboxes allow you to learn and apply specialized


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technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that

extend the MATLAB environment to solve particular classes of problems. Areas in which

toolboxes are available include signal processing, image acquisition, image processing,

data acquisition, control systems, neural networks, fuzzy logic, wavelets, simulation, and

many others.

2.2 The MATLAB System:

The MATLAB system consists of five main parts:

1. Development Environment: This is the set of tools and facilities that help you use

MATLAB functions and files. Many of these tools are graphical user interfaces. It includes

the MATLAB desktop and Command Window, a command history, an editor and debugger,

and browsers for viewing help, the workspace, files, and the search path.

2. The MATLAB Mathematical Function Library: This is a vast collection of computational

algorithms ranging from elementary functions, like sum, sine, cosine, and complex

arithmetic, to more sophisticated functions like matrix inverse, matrix eigenvalues, Bessel

functions, and fast Fourier transforms.

3. The MATLAB Language: This is a high-level matrix/array language with control flow

statements, functions, data structures, input/output, and object-oriented programming

features.

4. Graphics: MATLAB has extensive facilities for displaying vectors and matrices as

graphs, as well as annotating and printing these graphs. It includes high-level functions for

two-dimensional and three-dimensional data visualization, image processing, animation, and

presentation graphics.

5. The MATLAB Application Program Interface (API): This is a library that allows you to

write C and FORTRAN programs that interact with MATLAB.


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3. APPARATUS:

3.1 Apparatus used:

Laser line source

Solid state camera

PC with MATLAB 7.0

Electronic components for interfacing the motor to PC

Stepper motor

1394 cable, parallel or serial port connector

Fig 1: Apparatus of the system with interconnections

3.2Description of apparatus arrangement:

A laser beam is projected on to a measurement surface where it is scattered from surface and

its image is detected by an optical detector, a camera. The images contain the detail of groove

measurements. PC processes the images, infers the details and gives the feedback to motor.


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3.3Laser Source:A monochromatic laser source is used so that the ambient and weld torch light will not

have much effect on source light .Thus information at a precise point can be obtained

correctly.

3.4Solid state camera:A Panasonic solid state camera is used for image acquisition. It has a maximum

resolution of 2048*1536. However we grab the images for our purpose at a resolution of

640*480. It is connected to the PC trough a frame grabber interface. Instead a 1394 cable

can also be used to interface the camera with PC.

3.5PC with MATLAB 7.0:The PC used is an independent computer (not connected to LAN) with a Pentium 4

processor and MATLAB 7.0 software installed. MATLAB is computational tool. Image

acquisition, image processing and data acquisition tool box of MATLAB are used for

programming in the current context.

3.6Interface between PC and stepper motor:The electronic components are soldered on PCB so as to make a secure interface between

the PCs port and the stepper motor. Interface is connected to the port trough a male

connector. Detailed description of circuit diagram is given in later part.

3.7 Stepper motor:Stepper motor used is of 6V and full step angle 1.8 degree. The four output lines of the

interface are taken in by the stepper motor. It corrects the torch position so as to align it

with the edge centre.


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4 ALGORITHM:Calibrate the image with help of set of blocks of known dimensions.

Get the image and store it into an array variable.

Convert the true color image into gray scale image.

Select a region of interest for further processing.

Filter the image by removing noise.

Detect the edge of the images using edge detection operator.

Once edge detection is done program, execute the MATLAB codes to find various

parameters required.

Calculate the pixel values of edge and root centers.

Estimate the amount of deviation in successive values of edge center using the

calibration measurements.

Give corresponding feedback to the motor so as to correct the deviation.

5 IMPLEMENTATION OF ALGORITHM USING MATLAB:5.1 Calibration:

The result of processing the acquired image is the deviation of edge centre value in terms of

number of pixels. But this cant be directly given as an input to the feedback mechanism. So

we need to know the length which each pixel corresponds to. For this purpose before starting

of the process we put a specimen of known length and measure the number of pixels it

corresponds to. This process is repeated for different lengths and the results are combined to

obtain a calibration curve.

The figure below (fig.2) shows the calibration process. After processing such an image of a

specimen of known length, it is converted to a binary image. From the profile of edge we get

the value of length in terms of pixels. The process is repeated for various lengths and thus a

calibration graph is made. The graphical user interface communicates between the user and

the MATLAB platform.


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Figure 2: MATLAB window showing the calibration process. The difference in pointsof sudden rise and fall in the graph gives the length of specimen in terms of pixels.

5.2 Image acquisition:

Image is taken with the camera and it is transferred on to the MATLAB platform with the

help of winvideo driver readily available in latest version of MATLAB. In order to get

images in to the platform we need to create a video input object in MATLAB.

Imaqhwinfo is the function used to know the adaptors available on the system.vid=videoinput('winvideo',1,'RGB24_352x288') creates a video input object taking device

adopter, ID and the format as inputs. Once the object is created we use i= getsnapshot(vid);

statement to get the snapshot and store it in an array variable i.


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5.3 Conversion to gray scale image:

RGB images are converted to grayscale by eliminating the hue and saturation information

while retaining the luminance. It is achieved with a command rgb2gray(RGB).

5.4 Selection of region of interest:

Processing of unnecessary regions of image will increase the processing time. Further as our

region of interest remains almost constant, we can crop the region of interest and processing

can be done on the selected region.

This can be done with the function imcrop( I, [region of interest]). Instead if we are enough

sure of the region of interest, then we can only acquire that specific region of image. This

action amazingly brings down the processing time and can be done by changing the

properties of video input object using the statement vid.roiposition=[ ].

5.5 Filtering the image:

It has to be taken care that the image is free from noise (due to surface reflections) before

performing edge detection. Thus filtering is a very important and sensitive step in the

algorithm.


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Fig 3: Difference between the unfiltered and filtered images after edge detection

5.6 Edge detection:

Now we need to carry out edge detection on the filtered image so that we get a clear profile

of the focused light. This helps us in getting the required parameters.

In MATLAB edge detection is carried out with help of statement s=edge(bw,'canny'); The

inputs to the edge detection function are input image array variable and the algorithm used.

The canny detection algorithm suits our purpose.


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Fig.4: MATLAB user window showing original image, image after filtering and image after

edge detection and then the measured values of edge and root gap.

5.7 Parameter evaluation:

The output of the edge detection is the binary image with the required edges detected. Now

we need to exactly graph of the edge for parameter evaluation. The parameter required

ultimately from each image is the location of the edge centre.

For graphing the edge we search for white pixel in each column of pixels and store the row

number of the white pixel in an array. In our case we begin the search from bottom of a

column and continue the search till we get the first white pixel in nthrow and it is this n we

store into an array variable. The array variable readily graphs the edge profile.

At the edges there is huge variation of slope of the graph. This fact helps to evaluate the pixel

values of two edges and take the mean of them to get the pixel value of edge center. This


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procedure is repeated for all the images. Looping commands like for ... endand conditional

statements likeif then are used for this purpose.

5.8 Feedback:

For every image, edge centre value is compared to that of the previous image and the

difference in values with the help of calibration data is given as correction to the feedback

mechanism. The stepper motor by moving in either clockwise or anti-clockwise direction

corrects the torch position. Thus the torch position is always aligned with the edge centre.

6. SOFTWARE FOR STEPPER MOTOR CONTROL:For controlling the stepper motor trough the port an output object needs to be created in

MATLAB using parport=digitalio('parallel','LPT1');

The stepper motor moves clockwise when it outputs [1 1 0 0; 0 1 1 0; 0 0 1 1; 1 0 0 1]; that is

four sets of four bit data are sent out in a sequential order as shown in fig.4. If the order is

reversed, then the motor moves anti-clockwise.

For output and input through the port putvalue(parport,pval);andgval = getvalue(parport);

are the functions used. But between each output, the gap should be a minimum of one

millisecond. This can be achieved with tic .. toc command. Complete code is given in the

appendix.

7. HARDWARE INTERFACE BETWEEN STEPPER MOTOR AND PC:7.1. Stepper motor:

A Stepper Motor is a digital motor, because it is moved in discrete steps as it traverses

through 360 degrees. Thus stepper motor has an advantage of precise rotation. The stepper motor

which we are using has step angle of 1.8 degree. It has four input wires which need to be

energized in particular order so as to move the motor in desired fashion.


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Fig.5: Stepper motor inputs to the four wires (0011), (0110),(1001) and (0011) in this order

gives clockwise rotation. One output, for example (0011) makes the motor move by one step.

7.2. The interface circuit components and their purpose:

The following hardware required for the interface circuit:

1. Parallel or serial port connector: They are male connectors and the color configurationhelps us to identify the appropriate wire and connect it to the buffer.

2. RS 232 to TTL level converter: It is called the MAX-232 converter. We need it whenwe use serial port to give the feedback to the stepper motor. As all the electronic

components follow the TTL level logic, the RS232 output needs to be converted to

the TTL level.

Fig.6: The pin configuration of MAX232 converter


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\

3. Serial to parallel converter IC EDE1400: If in case, serial port is used, it is to be firstconverted to parallel data using the IC EDE1400. Rest of the circuit remains same for

series and parallel port connections.

Fig.7: Serial to parallel converter IC.

4. Four 1K resistors:R accounts for potential drop and acts like an extra resistor whichguarantees that transistor does not conduct when there is no signal fed.

5. Four TIP 120 transistors:When the input is on there is a base current in the transistorwhich gets amplified to cause the collector current.Thus when collector current reaches athreshold current level the stepper coil is in 1 state.

6. Four IN4001 diodes:If the coil is energized with DC, a diode is frequently installed acrossthe coil, to dissipate the energy from the collapsing magnetic field at deactivation, which

would otherwise generate a spike of voltage and might cause damage to circuitcomponents.


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Fig.8: Charging and discharging of the coil, before and after placing the diode

7. 5 V source for biasing: For biasing the transistors we need a constant voltage source.This source serves our purpose.

7.3 The circuit diagram:

Fig.9: The interface circuit between parallel port and the stepper motor.


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For the serial port interface the data has to be converted to parallel with EDE1400 after its

converted to TTL level logic using the MAX-232.

With the help of interface circuit, by controlling the state of parallel port pins we can obtain a

voltages of 0 and +V (6V) in the desired sequence and thus control the rotation of motor.


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8. CONCLUSCION:In this project, a seam tracking system which uses high intensity laser source, camera and a

PC has been developed. The system is made user friendly trough a graphical user interface.

Image acquisition, processing and control are achieved in 0.41 seconds, (in a PC with 1.66

GHz Core Duo processor) which is well in agreement with the need. Parallel programming

can be used to further reduce the processing time.


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9. REFERENCES:1. SMITH, J.S., and LUCAS, J.: A vision-based seam tracker for butt-plate TIG

welding, J. Phys. E; Sei. Instrum., 1989, 22, pp. 739 -744

2. UMEAGUKWU, C., and McCORMICK, J.: Investigation of an array technique forrobotic seam tracking of weld joints, IEEE Trans. Ind. Electron., 1991, 38, (3), pp.

223-229

3. CHAMBERS, S.P., SMITH, J.S., and LUCAS, J.: A real time vision system forindustrial control using transporters, Electro-techno[. (IEEIE) February/March 1991,

pp. 32-37

4. WLESE, D.R.: Laser triangulation sensors: A good choice forhigh speedinspection, I&CS Control Technol. Eng. Mngmt., 1989, 62, (9), pp. 21-29

5. MATLAB-Help file


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10.APPENDIX :MATLAB CODE:

1. Initialization of video: contents of file initial.m%---------------------initialization of video for calibration---------

vid=videoinput('winvideo',1,'RGB24_352x288')

%creation of video input objects

preview(vid)

%previews the information shot

2. Calibration: contents of file calibration.m%---------------------once the arrangement ready, through GUI, execute this code

p= getsnapshot(vid);

% gets the snap

figure,imview(p);

% for viewing pixel numbers, this will help in cropping

pe=imcrop(p)

% manual cropping by user

p1=rgb2gray(pe);

%converting normal image into gray scale


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background=imopen(p1,strel('square',95));

%SE = strel('square',W) creates a square structuring element whose width is W pixels.

%IM2 = imopen(IM,SE)----------->

%performs morphological opening on the grayscale IM with the structuring element SE

p4=imsubtract(p1,background);

%This command gives the difference of respective co-ordinates of both images

p5=imadjust(p4,stretchlim(p4),[0 1]);

h=ones(5,5)/25;

p6=imfilter(p5,h);

% filters an image with a 5-by-5 filter with equal weights, called an averaging filter

level=graythresh(p6);

%level = graythresh(I) computes a global threshold (level) that can be used to convert an

intensity image to a binary image

bw=im2bw(p6,.48);

%converts normal image to binary image

s=edge(bw,'canny');

%carries out edge detection using the specified algorithm

imview(s)

%----------------------code to plot the edge into array

t=0

trow=0;

trow1=0;

[r1,c1]=size(s);


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for j=1:c1

n=0;

for i=1:r1

if s(i,j)==1

c=c+1;

if c==1

trow=i;

tcol=j;

elseif c==2

trow1=i;

tcol1=j;

end

c=1;

end

end

distance(j)=r1-trow1;

t(1,j)=[distance(j)];

c=0;

end

%---------------------close the preview window

closepreview;

3. Image processing and measurement: Contents of measurement.m file:

%initilise the video with initial.m file, thus you now have image as array i

Old= ( p1+p4)/2

i1=rgb2gray(i);

i2=imcrop(i1);

background=imopen(i2,strel('square',95));


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i4=imsubtract(i2,background);

i5=imadjust(i4,stretchlim(i4),[0 1]);

h=ones(5,5)/25;

i6=imfilter(i5,h);

level=graythresh(i6);

bw=im2bw(i6,.48);

s=edge(bw,'canny');

imview(s)

t=0

trow=0;

trow1=0;

[r1,c1]=size(s);

for j=1:c1

n=0;

for i=1:r1

if s(i,j)==1

c=c+1;

if c==1

trow=i;

tcol=j;

elseif c==2

trow1=i;

tcol1=j;

end

c=1;

end


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end

distance(j)=r1-trow1;

t(1,j)=[distance(j)];

c=0;

end

k=0;

l=0;

for p=6:(c1-6)

if (t(p)-t(p+5))>3

k=k+1;

if k==1

p1=p;

end

p2=p+5;

end

if (t(p)-t(p-5))>3

l=l+1;

if l==1

p3=p-5;

end

p4=p;

end

end

new=(p1+p4)/2

4. Stepper motor control: contents of stepper.msteps=[1 1 0 0; 0 1 1 0; 0 0 1 1; 1 0 0 1];


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i=1;

k=1;

parport=digitalio('parallel','LPT1');

% creates a port object on

line=addline(parport,0:3,'out');

%declares the output lines of parallel port

deviation=new-old

direction=sign(deviation)

%depending upon the sign of deviation we give the input to motor, if it is to move clockwise

or anti-clockwise.

if direction=1

for j=1:deviation

pval=steps(i,:);

i=i+1;

if i>4

i=1;

end

putvalue(parport,pval);

%puts one row specified by pval ofmatrix on parallel port

gval = getvalue(parport);

tic

pause(0.001);

toc

end

end

if direction=-1


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for j=1:(-deviation)

pval=steps(i,:)

i=i-1

if i


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11.INDEX:A

Acquisition 9

B

Biasing 20

C

Calibration 13, 14

Canny 17, 22

E

Edge detection 13, 15

Edge center 13, 17

G

Gray-scale image 13, 15, 24

L

Laser source 8, 11

M

MATLAB 6, 9

P

Parallel port 6

Parallel processing 6

S

Serial port 11

T

Tool-box 9


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2005p3ps055-welding research institute

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