Transcript Network-based Production Quality Control Yongjin Kwon, Sweety

Network-based Production Quality Control
Principal Investigators: Dr. Yongjin Kwon, Dr. Richard Chiou
Research Assistants: Shreepud Rauniar, Sweety Agarwal
Applied Engineering Technology
Goodwin College of Professional Studies
Abstract
The current trends in industry include an integration of information and
knowledge base network with a manufacturing system, which coined a
new term, e-manufacturing. From the perspective of e-manufacturing,
any production equipment and its control functions do not exist alone,
instead becoming a part of the holistic operation system with distant
monitoring, remote quality control and fault diagnostic capabilities. The
key to this new paradigm is the accessibility to a remotely located
system and having the means of responding to a rapidly changing
environment. In this context, this paper presents innovative methods in
remote part tracking and quality control using the Ethernet SmartImage
Sensor and the Internet controllable Yamaha SCARA robot. Remote
control of an automation process using Internet can suffer from a time
lag, if the network is congested with heavy data traffic, which maybe
the greatest hurdle for using Internet for real time control. The
approach discussed in this paper overcomes the time lag and
mathematically calculates the part locations on the conveyor at various
instances and efficiently guides the robot to the product. The accuracy
of the proposed scheme has been verified and subsequent quality
control functions have been integrated, which vindicate the industrial
applicability of the setup. The Internet-based manufacturing operations
present many benefits, such as ubiquitous access, remote
control/programming/monitoring capabilities, and integration of
production equipment into information networks for improved efficiency
and enhanced product quality.
% Error of Robot Placement
3.00
2.00
% Error
1.00
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4
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No. of Trials
% Error (Robot Speed = 9.4 mm/s & Part Moving Speed = 2.9 mm/s)
% Error (Robot Speed = 16.04 mm/s & Part Moving Speed = 10.01 mm/s)
Fig. 5: Coordinate system for
machine vision (a, b) and
SCARA robot (x, y)
Fig. 6:Percentage of Robot error
Mathematical Relation
Fig. 1: The framework of Internet-based manufacturing systems
Fig. 2: Snapshot of the Application I developed for EQM.
The 3d space is broken down into two 2d space with the help of the DVT
machine vision cameras. With the color camera we divide the space into X
and Y co-ordinates .Considering the work area as a 2D surface, the scale
factors for each axis can be represented as:
START
Scale factor for z axis= Height of the sample / Pixels
Height of the object =Offset * Scale factor
The center point of moving object (centroid), which is a pick up location for
the robot, is defined as
Elements
1.YK250 –Robot used 4D axis
robot integrated with RCX-40
Controller
DVT Camera (X,Y axis)
Robot Arm YK 250
No
Yes
Start the Conveyor belt
Connect to DVT Cameras
Controller RCX 40 Application Program
C
DVT Camera (Z axis)
C
Connected?
2.DVT 540 C series (color vision
camera)
Object
3.DVT 540 series (black and white
camera)
No
Desired Object
present?
where K and G = the total number of pixel rows and columns in the object,
respectively, Xe = the x coordinate point for a left most pixel in row k, Xs =
the x coordinate point for a right most pixel in row k, Ye = the y coordinate
point for a bottom pixel in column g, and Ys = the y coordinate point for a top
pixel in column g.
Yes
Stop the Conveyor Belt
4.Conveyor belt
Conveyor Belt
Move the robot arms to
coordinates
Conclusion
5.Object to be inspected
Objective:
6.Remote Server
Pick up object and place in
desired location
Start conveyor belt
The main objective is to use the Internet as an
infrastructure to integrate manufacturing with quality
for industrial applications.
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LAN WEB
Enabled
• Divided in three modules
1. Development of Internet-Based Systems
2.Online Vision Guided Tracking
3.E-QUALITY for manufacturing
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Connect to Controller
E-manufacturing
• Enables manufacturing operations to successfully
integrate with the functional objectives of an enterprise
through the use of Internet, tether-free (wireless, web, etc.)
and predictive technologies
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Introduction
Current trend for manufacturing industry
• shorter product life cycle
• remote monitoring/control/diagnosis
• product miniaturization
• high precision
• zero-defect manufacturing and
• information-integrated distributed production systems
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Fig. 3: Work-area Set-up
Fig. 4: Flow Chart
The advancement in Internet technologies allows the remotely located
robots to be programmed ,controlled, and tested.
Robot and vision systems have been integrated in the network with
individual IP addresses for easy access and control.
Various image processing and analysis algorithms have been
integrated with the API for remote vision tracking.
These works have been tested over the Internet and
found to be reliable and less susceptible to process changes.