SL-2002-34 Introduction to Optical Inspection1. Introduction to Optical Inspection Helge Jordfald Sales & Marketing Manager Tordivel AS – Norway.

SL-2002-34 Introduction to Optical Inspection 1

Introduction to Optical Inspection

Helge Jordfald

Sales & Marketing ManagerTordivel AS – Norway

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Content

• Why Automated Optical Inspection?

• How does it work?

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Why Automated Optical inspection?

To improve production and reduce cost through:

• Quality Control

• Flaw detection

• Yield Management

• Factory Automation

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Alternatives

• Manual Control

• Sample Control

• Mechanical Control

• Photo Sensor Control

• No Control

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Manual versus Automated

• Manual Inspection– Varies over time and with

different persons– Monotonous work

• Automated Optical Inspection– Fixed quality over time– High speed– Traceability and

documentation0

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Manual Inspection Automatic Inspection

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Quality Control

• Zero faults to customers– 100% control instead

of sample control

– Objective and constant quality control

• Use Vision Control as sales argument

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Flaw Detection

• Reduce added value to defective parts– Identify and remove

products with flaw early in the production line

– Use several inspection station

– Reduce materiel consumption

– Increase your production capacity

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Yield Management

• Monitor changes in production performance

• Do corrections before the yield is reduced

• Real time statistics and trends

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Flexible Automation

• Increase production efficiency and flexibility by:– Automatic guiding of

robots for picking and packing products

– Automatic product sorting and program selection

• Reduce inspection costs

• Improve Health, Environment and Safety– Automate heavy and

monotonous manual job functions

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How does it work?

• System components

• Resolution and tolerances

• Basic image processingtechniques

Magic?

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Basic system

• Standard PC• One or more cameras• Illumination• Mounting system and

protection for light and cameras

• Communication interface to Control System

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The Optical System

• Camera– CCD array, Line camera, Area camera– Analogue or Digital signal transmission

• Lenses– Adapted to the individual applications

• An Optical System– A picture of a physical area (FOV = Field Of View)– With a specified resolution– With a minimum of optical distortion (compromise)

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Illumination

• The key to a good and reliable Vision system

• An increasing number of specialised lighting sources for different applications– Fiber, LED, Laser– On-Axis, Cloudy Day, Darkfield

• Selection of lighting source – a job for experts

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Communication with the production line

• Logical– Trigger – Take image– Answer – Result

• Physical - Electrical– 24 volt isolated– RS-232, Ethernet – tcp/ip

• Protocols to robot, PLC and web• TDVCmdProtocol – RS-232 - TCP/IP• OPC – ip – profibus (PLC and PC systems)

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Graphical User Interface

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Process insight

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Documentation

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Camera - Field of View

Piksels in X direction

Pix

els

in Y

dire

ctio

n

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Grey scale pictures

Original

Greyscale

Pixel grid

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Pixel resolution

• VGA Camera– 640 x 480 pixels

• FOV: 5mm x 5mm– Resolution ca. 0,01mm

• FOV: 50mm x 50 mm– Resolution ca. 0,1mm

• FOV: 500mm x 500mm– Resolution ca. 1 mm

• High Res. Camera– 1024 x 768 pixels

• FOW: 5mm x 5mm– Resolution ca. 0,005mm

• FOW: 50mm x 50 mm– Resolution ca. 0,05mm

• FOW: 500mm x 500mm– Resolution ca. 0,5 mm

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Example - Pixel Resolution

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Gauging

• Intensity level variations along the line.

• Simple gauging ofdistance

• Measure the distance between two intensity levels

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Improving the result

• Filtering

• Using geometry

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Calibration

• Transforming pixel to a dimensional unit (mm)• Corrections for distortion in the optical system

(camera and lens)• Using GRID• Calibration only related to one measurement plane• Scorpion Vision Software can be calibrated in

many planes

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Other advantages using Calibration

• Large Objects • Perspective

50 cmCamera

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How to build an inspection

• Perform and validate a number of ”simple” measurements– Dimension– Lines– Greyscale values – colour

• A set of these measurements are combined to identify specific Conditions

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Image processing example1. Distance calculated with

NearestPointOnLine Tool

2. Based on Calibration toolresult directly presented in mm

3. Measurement resolution:better than 0,01 mm

4. Measurement tolerance:estimated to 0,05 mm

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