1. NCDA: Pickle Sorter Final Design Review Project 98.09 Sponsored by Ed Kee of Keeman Produce, Lincoln, DE

2. 2 Overview Introduction to the Problem Design Method –Wants  Metrics –System and Functional Benchmarking –Concept Generation & Selection –Fabrication & Testing Budget Demonstration

3. 3 Background Title: Pickle Sorter Sponsor: Ed Kee of Keeman Produce Problem: The cucumber pickling industry currently separates out undesirable pickles by hand. Mr. Kee would like a device to efficiently and reliably separate the usable cucumbers from the undesirable ones.

4. 4 Undesirable Cucumbers 3 Catagories: –Oversized (Diameter > 2 in.) –Relish (Broken) –Cul (Misshapen - Crooks and Nubs) Constitutes 18.4% (average) of Total Crop –Oversized: 3.8% –Relish: 7.8 % –Cul 6.8 %

5. Plant Schematic

6. 6 Mission To provide an integrated, automated system to sort out undesirable pickles on the processing line.

7. 7 Approach Collect customer wants and constraints and develop them into metrics Use metrics to evaluate benchmarks and concepts, leading to a final design solution Fabricate components and adapt existing system Test each component and the complete system to compare to target values

8. 8 Customers and Wants

9. 9 Wants  Metrics

10. 10 Benchmarking Patents, Internet and Trade Journals System: –Integrated production line identification and sorting Function: –Material handling equipment and identification –System consists of three main functions: alignment, identification and removal.

11. 11 System Benchmarks Machine Vision common to all System Benchmarks Typical Sorting Parameters - Color, Size(length), Surface Features Best Practices

12. 12 Functional Benchmarks Alignment Common Material Handling Task Best Practices: lane dividers, overhead rollers Removal Wide Range of Possible Methods Best Practices: air jet, piston, robotic arm, trapdoor Identification * Critical System Function Best Practice: Machine Vision was the only geometric identification system found in use

13. 13 Sorting

14. 14 Alignment

15. 15 Target Values

16. 16 Concept Generation Benchmarking Functions Which Satisfy Target Values Best Practices Produce Handling Applications Brainstorming Mechanical Solutions for Identification Use of Physical Properties for Self-Separation

17. 17 Concepts Alignment 1 Lane Dividers 2 Rollers 3 Chains 4 Compartments Identification 1 Imaging 2 Pins 3 Calipers 4 Rolling Removal 1 Air Jet 2 Piston 3 Trapdoor 4 Tilting Tray 5 Robot Arm

18. 18 Concepts (cont’d) Piezoelectric Pins – Displacement of pins in field creates 3-D surface image Calipers – Difference in caliper displacement provides degree of curvature

20. 20 Selected Concepts Alignment: Converging Lane Dividers Identification: Imaging Ejection: Air Propulsion

21. 21 Imaging Equipment Hardware: –Self-Contained Digital Camera –PC for Software Development –I/O Interface with Ejection System –External Relay Software: –Camera-Specific Compiler and Operating System –Communications Package for I/O with the Camera –Custom Program for the Camera to Run

22. 22 Imaging Process Algorithm I –Camera continually takes images until an entire pickle is in view –Image is binarized, objects labeled, bounding box determined –Edge detection algorithm returns pickle contour –Pickle’s worthiness is determined –Ejection signal sent at appropriate time Algorithm II –Camera continually takes pictures until an entire pickle is in view –Image is binarized, objects identified –Length of pickle determined –Ratio of dark areas to light, Length of pickle used to determine if pickle is good. –Ejection signal sent at appropriate time

23. 23 Ejection System Testing

24. 24 Test Plan and Results

25. Complete Model

27. 27 Expenditures Budget Engineering Development Time –Alignment 32 hrs –Ejection 86 hrs –Imaging140 hrs Fabrication Time –Alignment 20 hrs –Ejection 72 hrs –Imaging 54 hrs

29. 29 Closing Points Problem Statement Concept Development: –Alignment: Lane Dividers –Identification: Computer Controlled Imaging –Removal: Air Propulsion Budget Demonstration