Track, Trace & Control Solutions © 2010 Microscan Systems, Inc. Introduction to Machine Vision for New Users Part 1 of a 3-part webinar series: Introduction.

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Presentation transcript:

Track, Trace & Control Solutions © 2010 Microscan Systems, Inc. Introduction to Machine Vision for New Users Part 1 of a 3-part webinar series: Introduction to Machine Vision

© 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:  What is Machine Vision  How Machine Vision can be used in different applications  What makes a Machine Vision system

© 2010 Microscan Systems, Inc. Today’s Topics Today we will discuss:  What is Machine Vision?  Why Use Machine Vision?  Parts of a Machine Vision system

© 2010 Microscan Systems, Inc. What is Machine Vision? Automatic extraction of information from digital images. The most popular Machine Vision applications include: 1.Measure: measure a part and check to tolerances. 2.Decode: 1D codes, 2D symbols, and OCR reading. 3.Count: find the number of parts or features on a part. 4.Locate: report the position and orientation of a part. Arthur C. Clarke’s Third Law: Any sufficiently advanced technology is indistinguishable from magic. It’s not magic!

© 2010 Microscan Systems, Inc. 1. Measure Automated measurement, and then check to specified tolerances. Measuring a spark plug for proper gap tolerance

© 2010 Microscan Systems, Inc. 2. Decode Decode 1D or 2D Symbologies, read OCR.  Record information for historical data  Use information for immediate action  Validate the data for correctness OCR on cans used to identify contents 1D and 2D symbols DPM Data Matrix

© 2010 Microscan Systems, Inc. 3. Count Find a number of parts or features on a part.  Missing parts/proper assembly  Presence: is it there or not?  Identify quantity Count the number of sodas in an 8-pack Counting the holes drilled in a machined block of aluminum

© 2010 Microscan Systems, Inc. 4. Locate Report the position and orientation of a part.  Locate position and orientation then check to specified tolerances  Report information to another device for guidance  Use for alignment of other Machine Vision tools  Find a unique pattern to identify the part Locate tools can align images for other tools Search for a pattern to identify a product

© 2010 Microscan Systems, Inc. Why Use Machine Vision?  Reduce Defects  Track WIP  Increase Yield  Comply with Regulations  Machines don’t get tired

© 2010 Microscan Systems, Inc. Parts of a Machine Vision System 1. Lighting The most critical piece, illuminates the part 3. Sensor Captures light and converts it to a digital image 4. Vision Processing Looks at the picture to extract features 5. Communicate Report the results to the world, can be data or discrete I/O Part The object to be measured or inspected 2. Lens Delivers image to the sensor Example component system Examples of integrated systems 2D imagers use the same parts as a Machine Vision system

© 2010 Microscan Systems, Inc. 1. Lighting and the part Proper lighting is essential to a successful Machine Vision application.  Why is lighting so important?  Select the right light and geometry: ˗ Reveal the part’s features ˗ Minimize everything else If the feature cannot be seen, it cannot be measured

© 2010 Microscan Systems, Inc. 2. Lens Delivers the image to the sensor.  Determines the Field of View, Depth of Focus, and Focal Point  Interchangeable: C-mount  Fixed: auto-focus 25mm: magnified image Depth of Focus Field of View Lens & extension tubes 12mm: larger Field of View

© 2010 Microscan Systems, Inc. 3. Sensor Captures light and converts it to a digital image. 2MP sensor.3MP sensor Sensor pixels collect light Sensor is inside the camera

© 2010 Microscan Systems, Inc. 4. Vision Processing Extracting useful information from images  Acquire image: Collect the image that was captured by the sensor/lens  Image Pre-processing: Modify the image to make features stand out  Image Analysis: Extract features from the image  Geometry & Tolerance: Measure features and compare to specification  Results: Communicate Pass/Fail Counting PCB pads using blobs Measure using edge tools Locate a part by searching for unique features

© 2010 Microscan Systems, Inc. 5. Communicate Report the inspection results.  Discrete I/O: Trigger inspection, drive indicator lights, PLCs, diverters  Data: send data over RS-232, Ethernet, Ethernet/IP… Electrical signals can be sent to a diverter to remove faulty items, and to a stack light to warn operators HMI screens can display Machine Vision inspection status A PLC can collect data and discrete I/O signals Integrated systems have I/O built in

© 2010 Microscan Systems, Inc. Parts of a Machine Vision System Putting it together: Check fill level and cap installation 1 1.Trigger the inspection (Communicate) 2.Illuminate the bottle (Lighting) 3.Capture the image (Lens and Sensor) 4.Run Machine Vision tools (Vision Processing) 5.Show results to an HMI (Communicate) 6.Send a signal to a reject device (Communicate)

© 2010 Microscan Systems, Inc. Introduction to Machine Vision for New Users Conclusion  Machine Vision can be used to automate manual tasks –Measure –Decode  There are five key components to a vision system: –Part/Lighting: the features to be extracted must stand out consistently –Lens: select the proper lens combination to deliver best Field of View and Depth of Focus to the sensor –Sensor: the sensor type and number of pixels is important to acquire a good image –Vision Processing: extract useful information from an image –Communicate: send the results in a way that is useful –Count –Locate

© 2010 Microscan Systems, Inc. Next session…. Choosing the Right Machine Vision Applications  Successful Machine Vision Applications  Questionable Machine Vision Applications  Machine Vision Hardware Platforms Introduction to Machine Vision Webinar series: Today: Introduction to Machine Vision for New Users December 10: Choosing the Right Machine Vision Applications December 17: Machine Vision Tools for Solving Auto ID Applications