66 So, the provided data can be used in gluing process with assisting of KUKA
arm robot. As in robotic automation system, the robot must be filled with 3D data configuration for robot to do the task.
3.2.3 Converting Robot Coordinate to Robot Coordinate
The information of robot coordinate is really important in providing the robot with coordinate that the robot understands. In order to achieve that, the
comparing between pixels coordinate and robot coordinate needs to be done in acquiring the new equation in converting pixels coordinate to robots coordinate.
Table 3.3 shows data recorded used in generate equation for converting pixels to robot coordinate.
Figure 3.17: Result of Image in x, y and z Data
67 Table 3.3: Data Recorded from Pixel and Robot Coordinate
Pixel Coordinate Robot Coordinate
P
p
1 = 319.52, 231.10, 202.16 P
p
2 = 447.74, 112.93, 0 P
p
3 = 454.27, 372.10, 0 P
p
4 = 188.54, 371.57, 0 P
p
5 = 191.33, 116.65, 0 P
r
1 = 365.87, 0.01, 383.76 P
r
2 = 458.05, 78.52, 263.54 P
r
3 = 456.74, -78.09, 264.66 P
r
4 = 287.42, -76.53, 263.08 P
r
5 = 288.19, 77.51, 263.96 A
p
= 259.17 B
p
= 265.73 C
p
= 254.92 D
p
= 256.41 E
p
= 202.16 A
r
= 156.61 B
r
= 169.32 C
r
= 154.04 D
r
= 169.86 E
r
= 119.95
By using the data available, ratio between pixels and robots coordinate can be calculated and used that data in generating new equation as shown below.
P
p
1 P
p
2
P
p
3 P
p
4 P
p
5
P
r
1 P
r
2
P
r
3 P
r
4 P
r
5 D
p
A
p
B
p
C
p
D
r
A
r
B
r
C
r
E
r
E
p
x z
y x
z y
68 From the information above, data ratio for each coordinate are determined in
findings the differences between pixels and robots coordinate. By applying that, the new generated equation in converting pixels to robot coordinate can be achieved. The equation
developed based on the same data used in pixels and robots coordinate as shown in Equation 3.4. The equation is shown as:
69 [
]
[ ]
[ ]
3.4
3.2.4 Defect Inspection Monitoring Application
Inspection system is one of the important aspects that will be required in industrial application. Every product produced must be through inspection system first to ensure the
quality of the product before it can be claimed as a quality product. In this research, defect inspection system known as Defect Inspection Monitoring DIM is introduced in order to
ensure that the gluing process done perfectly with quality is on hands. Defect can be occurring when the system is not well calibrated and the information send to the system
cannot be extracted efficiently according to the system required. This is one of the important aspects that need to be focus during the production of any product. Thus, DIM is
proposed to meet requirement of the task in developing complete system. According to the Figure 3.18, the suggested vision algorithm is applied based on the needs of the system that
required detecting two types of defect; 1 Gap defect and 2 Bumper defect in gluing line.
70 According to Figure 3.18, the proposed algorithm introduced two phase in Defect
Inspection Monitoring DIM are defect training phase and defect recognition phase. Defect training phase required the system to identify the usual error to be trained as the
defect template. In acquiring a better template, the source image needs to be emphasized in order to return the quality value of the image. The crucial part is involving the noise
reduction from source image that reflects mostly on recognition rate in defect recognition phase. Better quality for template creation, recognition rate results is higher. Less quality
image template provided, it turns the result low in recognition rate. In this research, two types of defect are trained according to the need of the system.
Each defect has its own special characteristic which is extracted that information and used as a medium of defect detection. Technique of ROI once again introduced to limit the
process region that used as a template defect image. Fill interlace and median filter as Figure 3.18: Suggested Algorithm for Defect Inspection System
71 important features in removing noise in the image defect. All information is saved into the
system and being group into two categories; gap defect and bumper defect. In gluing application, there are three major defects that are usually occur in gluing line, gap, bumper
and bubble defect. Gap and bumper are one of the major problem occur during gluing process because of the robot system is not well-calibrated. For gap defect, the problem
occurs when the gripper speed a little bit fast from usual that makes the empty region along the gluing line. As well as bumper defect, the gripper is far too slow that might cause at
certain region too much glue that looks like a bumper along gluing line. Bubble defect might occur when the gluing tool has inconsistent to distribute the glue along gluing line
but the defect is too small to determine that the product is rejected. So, bubble defect is assumed as a minor defect and the product is accepted. Figure 3.19 show the templates that
are used in this research for classification defects in gluing line.
By referring to Figure 3.19, both templates are created with less noise and better quality of the image. After the template had been created and stored in memory, the
recognition phase is takes placed. In order to accommodate the testing image with the template image, several techniques are introduced to free image testing from noise and its
Figure 3.19: Template Using in Defect Inspection
72 background those usually affecting results of recognition rate. Auto threshold is used
demanding of the system requirement that need to separate between image object and background automatically without user specified threshold value. Fill interlace also
introduced in order to make the testing image smooth by removing odd or even line number occur in the image. With this, the image is more soft compare to the original
image. 15 types of image testing had been used in this software development to configure
whether this system is capable in determining the defects in all possible direction. Each of tested images has different number of defects and various type of defect with each in
different location and direction. These tested images are developed to cope with the system development in order to meet objective of the research. Plain cardboard is used as object
specimen in order to do the classification of defects. According to Figure 3.19, by applying those templates each defects can be detected as long as the source image characteristic is
exactly the same with template image. In this software version, there is 4 types of classification to identify problem occur in the object after gluing process done that will
discussed thoroughly in next sub chapter of simulation results. Each classification needs to be repaired in Correction of Defect CoD application to ensure every rejected product can
be process again to produce a quality product.
73 Figure 3.20: Flow Chart of Defect Inspection Monitoring DIM and Correction of
Defect CoD
74
3.3 Simulation Results
