1. 1ni.com Real-world Techniques in High-Speed Vision Inspection Wed 4:45 5:45 Room 17b

2. 2ni.com Presenters Kenneth Brey P.E. Technical Director, DMC Inc. Eric West Project Engineer, DMC Inc. 3. 3ni.com Agenda 1. DMC company overview 2. High Speed Vision - Definition 3. Customer Focus W.H. Leary 4. Hardware Architectures 5. Camera triggering with FPGA 6. Parallelism 7. Rapid algorithm tips 8. Product Training 9. Q&A Along the way: Case Studies Knowledge Nuggets 4. ni.com Company Overview 5. 5ni.com DMC Company Profile Industries Served: Automotive Chemical and Food Processing Electronics/Semiconductor Hydraulics Laboratory Testing Machine Tool Material Handling Metal Converting Packaging Pharmaceutical Printing & Textiles Established in 1996, offices in Chicago, Boston & Denver & customers throughout the world employees & growing 70+ 6. 6ni.com Certifications 7. ni.com High Speed Machine Vision Definition: Automatically processing many images per second and acting on the result in real time. Is different than High Speed Photography, which is about acquiring many images per second. 8. ni.com Customer Focus Daniel McCarty Sr. Software Development Mgr. W. H. Leary Co., Inc. Tinley Park, IL 9. 9ni.com Customer Scenario Global Consumer Packaging Solutions Challenges: 1. Factory lines running products over 200,000 pieces/hr 2. Microseconds to decide whether a product is good 3. Self-monitoring, self-compensating systems 4. Track and reject bad product 10. 10ni.com High Speed Packaging QA 11. 11ni.com Process Video 12. 12ni.com TraditionalApproaches: In-line Sensing, I/O Photoeye, Glue valve (gun), and Sensor 4, 8, 16, or 24 of these groups (stations) What about out-of-line defects? Glue sling, Carton scrap, Crossing glue lines Complex packaging requires 30+ stations 13. 13ni.com High Speed Complex Cartons 14. ni.com Hardware Architectures 15. 15ni.com HardwareArchitectures - Overview Compare: 1. Windows PC Based Solutions 2. Smart Cameras 3. LabVIEW RT Based Solutions More info from NI at: http://www.ni.com/webcast/1063/en/ 16. 16ni.com HardwareArchitectures Windows PC Based Separate Imagers, Windows PC Advantages: Buy Processor / Software Licenses Once Integrated HMI Works with standard cameras, plus odd-balls Easy image storage Leading edge processor performance Disadvantages: Not deterministic (subject to Windows naps) I/O and image acquisition loosely integrated* *Exceptions include NI FPGA-Enabled Vision RIO cards. C C C 17. 17ni.com HardwareArchitectures Smart Cameras Bundled Solution: Imager, Processor, Software, I/O Advantages: Great communication to PLCs Distributed Model: Scalable Performance Deterministic performance. (no naps) Disadvantages: Buy Processor/Software with Each Camera Multi-camera inspections are a challenge Slower processors than a typical PC Limited Imager Selection No Integrated HMI Limited on-board storage 18. 18ni.com HardwareArchitectures LabVIEW RealTime Single Processor Multi Imagers Advantages: Buy Processor / Software Licenses Once Multi-image inspections processed together Deterministic: No Naps Super fast processors available Broad Imager Selection Integrated I/O with FPGA! Disadvantages: Limited onboard storage No Integrated HMI* 19. 19ni.com HardwareArchitectures LabVIEW RealTime Single Processor Multi Imagers Advantages: Buy Processor / Software Licenses Once Multi-image inspections processed together Deterministic: No Naps Super fast processors available Broad Imager Selection Integrated I/O with FPGA! Integrated HMI with new NI Linux RT builds! Disadvantages: Limited onboard storage 20. ni.com Case Studies 21. 21ni.com Case Study 1: Packaging Glue Inspection Challenge: Cartons vary in size Frequent Job Changes Inspect glue location relative to carton 40 images per second 22. 22ni.com Case Study 1: Packaging Glue Inspection Solution: LabVIEW RT PC with FPGA card 4 Basler Racer GigE line-scan cameras o 2 sets - side-by-side Ultra-violet lighting and UV fluorescent glue Custom one-button Learn algorithm 23. 23ni.com Case Study 1: Packaging Glue Inspection Algorithm: Threshold image twice: o Carton Threshold edges define coordinate system o Glue Threshold (brightest) Inspect glue inside Regions of Interest (ROIs) Configure Ignore regions 24. 24ni.com Packaging Glue Inspection Passing Images 25. 25ni.com Packaging Glue Inspection Failing Images 26. 26ni.com Case Study 2: Packaging Braille Inspection Challenge: EU requires Braille on all OTC drug packaging Operators dont read Braille Confirm all Braille Dots Super fast: 50 cartons per second Handle carton rotation 27. 27ni.com Case Study 2: Packaging Braille Inspection Solution: LabVIEW RT PC and CameraLink card Basler Sprint Line-Scan Camera o Line-scan allows varying lengths of cartons o Uniformity of light along entire length of carton Automatic Learn and Translate Text 28. 28ni.com Case Study 2: Packaging Braille Inspection Algorithm: Normalize for uneven lighting across width of image Angled lighting, dimples comprise a dark & light spot Locate dots with dual-threshold particle-find Pattern Alignment with Principal Axis* *See Wikipedia for more information on Principle Axis 29. 29ni.com Case Study 2: Packaging Braille Inspection 30. ni.com Camera Triggering using FPGA 31. 31ni.com FPGA triggering Use NIs Vision-RIO Pre-programmed Common triggering and result buffering functions Learn more: http://www.ni.com/white-paper/14599/en/ Build your own with LabVIEW FPGA Module. Manage Encoders, Part Sensors, Lighting and Camera I/O all with microsecond precision Custom Synchronization and Buffering As easy as programming in LabVIEW 32. 32ni.com FPGAtriggering Example 1: Partial Closing Frame In a line-scan inspection, close and re-open the frame to begin a new image without missing a line. For webs that are continuous, but registered. Next Product Start Next Frame Time (200 us/division) Encoder A Encoder B Product Sensor Line trigger Frame Trigger 33. 33ni.com FPGA triggering Example 2: Result Tracking Monitor Encoders/Triggers Buffer and Output Pass/Fail results 34. 34ni.com FPGA triggering Example 2: Result Tracking 35. ni.com Parallelism inMachineVision Doing these things at the same time: Trigger Cameras Acquire Images Process Images Handle Results Display Images 36. 36ni.com Why Use Parallelism? Benefits: 1. Its faster! Costs: 1. More Data Tracking 2. More complex programming 3. Handshaking between distributed processes 4. System-level debugging 37. 37ni.com Parallelism Example Independent platforms help: FPGA / LabVIEW RT LabVIEW RT still does multiple things at once 38. 38ni.com Parallelism Strategies 1. Pass ID info with each image use to sync results IMAQdx Buffer # Camera Timestamp Encoder Position 2. Push the result position down the line use queues 39. 39ni.com Parallelism Strategies Use explicit processor selection for processing Leave a processor available for image acquisition. 40. ni.com Rapid Algorithms 41. 41ni.com RapidAlgorithms are key to high-speed processing Use Fast Algorithms Instead: Particle find and analysis Coordinate-based rotations and translations (Just Math) Subimage analysis Avoid Slow Algorithms: Pixel Rotations Morphology (erode, dilate) Edge find Object Find Pattern Match When possible do less. 42. 42ni.com Speed Tip: Avoid floating point math Math operations are generally fast (compared to most vision algorithms) To make math REALLY fast, be sure to perform only integer math. 43. 43ni.com Speed Tip: Avoid floating point math Average Execution Time (doubles): 142.6ms 44. 44ni.com Speed Tip: Avoid floating point math Average Execution Time (integers): 57.75ms 2.5x faster than doubles! 45. 45ni.com Speed Tip: Image References Creating and Disposing Takes Time! Declare a set of temp static image references use in sub-VIs as temporary images. 46. 46ni.com Speed Tip: Image References Bad Example IMAQ Count Objects 2.vi 47. 47ni.com Speed Tip: Image References DMCs Customized Count Objects VI: 48. 48ni.com Speed Tip: VI Profiler Use the profiler to find VIs that use the most Total Time Total Time: 1060ms 49. 49ni.com Speed Tip: VI Profiler Eliminated repeated IMAQ Create/Destroys Total Time: 860ms About 20% faster 50. 50ni.com Speed Tip: Sub-ImageAnalysis Where possible: Process a smaller Region of Interest Perform full-image operations on a down-sampled image. IMAQ Extract 2.vi: 51. ni.com Custom Product Training For high-speed vision 52. 52ni.com Product training Motivation: 10 or more product changes per shift Customers demand simplicity Setup engineers are not provided This is the one thing we can take our time on its only done once. 53. 53ni.com Product training Braille Example Challenge: Train and read with 1 button Inspect Fast 50 cartons/s EVERY DOT EVERY CARTON LEARY BRAILLE 54. 54ni.com Product training Braille Example 2. Capture multiple setup images 3. Align each pattern based on Centroid and Principle Axis 4. Create Golden Template as average of data from multiple images 5. Re-inspect all template images with the new golden template 1. Capture longest possible image, determine carton size 55. 55ni.com Product training Braille Example 56. 56ni.com Product training Glue Example Challenge: Train with 1 button Define coordinate system Define inspection regions Inspect fast 40 cartons/sec 57. 57ni.com Product training Glue Example Training Algorithm 1. Acquire multiple images 2. Align carton coordinate systems 3. Compile glue positions into one master template 4. Add constant offsets to create inspection regions 5. Reinspect all individual images Each inspection region is inspected as a subimage 58. 58ni.com Product training Glue Example 59. 59ni.com Product training Glue Example 60. 60ni.com In Conclusion 61. 61ni.com Conclusion Machines are going faster Zero defects are expected High-speed vision offers a competitive advantage as part of a high-tech product portfolio. 62. 62ni.com Questions? 63. 63ni.com