Choosing The Right Machine Vision Applications Session2Choosingthe

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Choosing the Right Machine
Vision Applications

Part 2 of a 3-part webinar series: Introduction to Machine Vision

Track, Trace & Control Solutions
© 2010 Microscan Systems, Inc.

About your Instructors

Dr. Jonathan Ludlow
Machine Vision Product Manager

Juan Worle
Technical Training Coordinator

© 2010 Microscan Systems, Inc.

Today’s Objectives

By the end of this webinar, you will know:
 How to identify a good Machine Vision application
and which applications to avoid
 Machine Vision hardware platforms and what to
consider when choosing one for an application

© 2010 Microscan Systems, Inc.

Today’s Topics

Today we will discuss:
 Successful Machine Vision Applications
 Challenging Machine Vision Applications
 Application Examples
 Machine Vision Hardware Platforms

© 2010 Microscan Systems, Inc.

Successful Machine Vision Applications
As a new user, you should choose an application that will not be overly complex.
Successful applications have the following attributes:
1.
2.
3.
4.
5.

Looking for a single decision point
Clear application requirements
Application is consistent
Parts are high value or critical
Some false rejects are OK

© 2010 Microscan Systems, Inc.

1. Looking For Single Decision Point
The inspection will look for good and bad, rather than sorting through
parts.




Good vs. bad applications have a single decision point
Failures should be clearly visible
Some sorting is OK –(but not much)

GOOD vs. BAD: Reject defective product

SORTING: Identifying each
battery type and vendor by
shape, color, and text
© 2010 Microscan Systems, Inc.

2. Clear Application Requirements
The application requirements have been clearly defined.




The list of requirements is small, such as fitting on a single page
Samples of good and bad parts are available
The part to be inspected has a beginning and end, rather than continuous

Samples of good and bad
parts should be available

Inspection requirements
should be one page

© 2010 Microscan Systems, Inc.

Avoid continuous web
inspection

3. Application is Consistent
The parts are always positioned the same way, at the same distance
from the camera, with consistent lighting.

Organic items are inconsistent in
size and shape; items randomly
placed are difficult to locate.

Good applications include consistent
part shape, positioning, and lighting.

Designing tight application parameters allows a simple Machine Vision system.

© 2010 Microscan Systems, Inc.

4. Parts Are High Value or Critical
The value of the part is high, or the consequence of a flawed part is
high.
High Value

Low Consequence

High Consequence

Low Value

© 2010 Microscan Systems, Inc.

5. Some False Rejects Are OK
The price for catching all the defects may be some false rejects. The
customer will need to make a decision on what will be the acceptable
level of rejects.

As good and bad parts appear to be similar,
some false rejects must be acceptable to
catch all the bad parts

Ideally, there is a large separation
between good and bad parts.

© 2010 Microscan Systems, Inc.

As good and bad parts become more
similar, the grey area becomes larger

Challenging Machine Vision Applications

Applications that are not impossible, but are specialized and time
consuming:
1. Non-industrial applications
2. Organic materials applications

ANYTHING IS PO$$IBLE!
 Time consuming projects cost money
 Consider the Return on Investment (ROI)

© 2010 Microscan Systems, Inc.

1. Non-Industrial Vision Applications
Machine Vision is a specific branch of Vision that is specialized for
industrial applications.
 Pursue these applications: Industrial, Manufacturing
These types of applications typically follow the 5 guidelines we
discussed earlier.
 Non-industrial applications include: Medical, Scientific, Security,
These applications have different requirements and use different
tools.

Security, Scientific and Medical applications have
specific challenges that take a lot of time to overcome.
© 2010 Microscan Systems, Inc.

2. Organic Materials Applications
Organic materials are inconsistent in size and shape - Difficult to
identify and inspect.


Avoid forest products, wood, vegetables, sorting trash

Watching vegetation grow
is not an ideal application

Sorting trash is
hard work!

© 2010 Microscan Systems, Inc.

Application examples

 Ideal Machine Vision applications:
–
–
–
–
–

Ideal Machine Vision
applications have criteria
expressed in numbers

There is a part handling solution in place
Application can be replicated over 20 lines
You are inspecting/gauging expensive parts
There are good and bad parts available for evaluation
The inspection/gauging criteria can be expressed in numbers

 Challenging Machine Vision Applications:
–
–
–

The application is inspecting plastic knives and forks
The “spec” is a book with fuzzy pictures of bad parts
The current process is manual

© 2010 Microscan Systems, Inc.

Ideal Machine Vision Applications
Here are a few examples of successful applications for new users.
These applications have the traits of a successful application:
1.
2.
3.
4.

Looking for a single decision point
Clear application requirements
Application is consistent
Parts are high value or critical

Read barcode and check label position
© 2010 Microscan Systems, Inc.

Count that 3 bolts installed

Validate 2D and OCR data
Measure part to tolerance

Machine Vision Hardware Platforms

Hardware Platforms for Machine Vision Applications

Smart camera:
Integrated Machine Vision
solution

© 2010 Microscan Systems, Inc.

PC Based:
PC-based image processing
using cameras for acquisition

Smart Cameras: Integrated Solution
Smart cameras can vary in processor power and capabilities. Be sure to match
the smart camera with the application.






Sometimes includes integral lighting and lens
Vision processing is done in the camera
Good for a single or few tasks with moderate processing speeds
Single camera operation, no multiple camera operations
Fewer I/O points, less sensor options
A computer is only required for programming. When programming is complete,
the smart camera can run on its own.

Benefits of a Smart Camera:
• No PC on the floor
• Compact
• Low cost
• All-in-one

© 2010 Microscan Systems, Inc.

Smart Cameras:
Includes lighting, lens,
sensor, Image processor,
and I/O

PC-Based Solutions
GigE Machine Vision uses a camera transporting the image over Gigabit
Ethernet to a PC for vision processing.






Fewer system components than frame grabbers
More processor power than smart cameras
Multiple cameras in a single inspection
More I/O points than smart cameras
PC required for operation

I/O Expansion:
PC-based Machine Vision like
GigE and frame grabbers allow
for more I/O

GigE:
Ethernet transports an image into a PC

Similar configurations: USB, IEEE1394, 10/100 Ethernet, Frame Grabbers
© 2010 Microscan Systems, Inc.

LIGHTS!
The Machine Vision platforms discussed today did not include Machine
Vision lighting.
To learn more about Machine Vision lighting, visit www.microscan.com
and select Training/Lighting.
Learn about:
– Geometry
– Feature analysis

Geometry
© 2010 Microscan Systems, Inc.

Feature Analysis
– Lighting types
– Effect of lighting

Introduction to Machine Vision for New Users

Conclusion
 When entering the Machine Vision world, consider applications for:
Measuring, Decoding, Counting and Locating.
 By following the guidelines discussed here, your first Machine Vision
applications will result in success and low maintenance.
 Other types of applications are not impossible or unsuccessful, but they are
highly specialized and sometimes require specific equipment or other tools.
 Selecting the right platform for an application is a balance between cost,
performance and portability.
 Don’t forget to visit www.microscan.com to view training courses about
Machine Vision Lighting.
 Let us help you define an application and hardware platform!
© 2010 Microscan Systems, Inc.

© 2010 Microscan Systems, Inc.

Thank you!
For More information
Website: www.microscan.com
–
–
–
–

Online courses
Spec sheets
Technology Brochures
Support Self-help and support request form

Instructors:
Juan Worle, Technical Training Coordinator
Email: jworle@microscan.com
Dr. Jonathan Ludlow, Machine Vision Product Manager
Email: jludlow@microscan.com

Feedback on this webinar: www.microscan.com/feedback
Additional contacts:
Additional product information: info@microscan.com
Training: training@microscan.com
Support: helpdesk@microscan.com

© 2010 Microscan Systems, Inc.



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