The document provides an overview of machine vision systems, detailing their components, such as image acquisition cameras and lighting techniques, as well as the steps involved in part inspection. It discusses various applications of machine vision in food and beverage industries, including quality control, bottling, labeling, and product safety. Additionally, it highlights the advantages of implementing machine vision systems, such as reduced costs, increased productivity, and improved customer satisfaction.

1. MACHINE VISION SYSTEM IN
By:
Mechatronics

2. Machine vision Block diagram:

3. What is Machine Vision?
Lighting
Techniques to make
part look its best
Components of a
MachineVision System
Field ofView
Communications
Send decisions to
other devices Part: Good
# Caps:
1
Image Acquisition
Camera taking a picture
VisionTools
Evaluate the picture

4. ImageAcquisition:
Imager
Microchip that converts light energy
into a digital information (pixels)
CCD & CMOS are most common imager
types
Pixels
The smallest piece of a digital
image
Camera Resolution is measured in
pixels
– 640 x 480 or 0.3 Megapixels
– 1600 x 1200 or 2.5 Megapixels
Field ofView
Parts

5. 480
1200
640 1600
Pixel Resolution:
0.3 Megapixel
640 X 480
2.5 Megapixel
1600 X 1200

6. Factors affecting Image:
 Light Intensity
 Direction
 Focal length
 Object distance
 Imager type (CCD or CMOS)
 Dark Image calibration
 Image filtering

7. Lighting sources:
Halogen
Fluorescent
LED

8. 1.Part arrives at inspection station
2.Proximity or Photo sensor detects
part & sends trigger to vision sensor
3.Part is illuminated
4.Image is acquired and
digitized
5.Vision software processes image
and analyzes part as Good or
Bad
6.Discrete output activates
reject diverter if part is Bad
7.Display shows operator rejected
parts and production statistics
GoodBad

9. Industry Layout:
Processing
Material Handling
Packaging
Bottling

10. Applications of MV in food and
beverages

11. MV
Bottling
&
Labeling
Product
Safety
Product
Trace-
ability
Material
Handling
Quality
Control

12. 1. Quality Control:
- Food
Problems:
- Wrong pie in wrapper
- Damaged pies
Solution:
- Color vision sensors
installed
- Quality inspection for
damaged pies
Good Pie Coated with gaps in
shell
Coated with gap in
shell
Uncoated with holes

13. Beverages:-
Problems:
- Liquid in bottles not
consistent in color
- Matched labels
Solution:
- DVT color vision sensor
- Color monitoring of
liquid color

14. 2. Bottling, labeling & thread
inspection:
Problem:
- Need to verify cap &
tightened correctly
- Check safety ring
Solution:
- Gauging tool in vision
sensor measures distance
from neck to top of the
cap
- Verifies safety ring

15.  Computes
dimensions of:
- Hole diameter
- Label length
- Distance and
angle

16. 3. Product Safety:
Label Quality check
Problem: - Label missed or torn
- Label might have ‘flag’, if not applied
fully
Solution: - Measure label to check for torn, missing
or skew labels
- Elimination of ‘flag’

17. PC Vision Sees the Entire
Product:
Verify
Product Size
Read
Bar Code
Inspect
Label Seam

18. 4. Product traceability:
-Can code verification
-Can Code verification
system installed.
- For no cans should be
mislabeled.

19.  BAR code reading and grading
 2D matrix code reading
 OCR/ OCV
- Optical Character recognition
- Optical character verification

20. 5. Material Handling:
- Locating the product conveyer and placing
into the boxes
- Vision sensor provides robotic guidance

21. Advantages:
 Lower capital costs
 Increased productivity
 Faster time to market
 Lower production costs
 Reduced scrap / rework
 Increased customer satisfaction
 Improved brand image

22. Animation of machine vision:

23. Thank You