NCDA: Pickle Sorter Concept Review

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

2. Overview • Introduction to the Problem • Method • Wants  Metrics • System and Functional Benchmarking • Concept Generation • Concept Selection • Schedule • Budget

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 unusable ones.

4. Plant Schematic

5. Strategy • Mission: To provide an integrated, automated system to sort out undesirable pickles on the processing line. • Approach: Collect customer wants and develop them into metrics which can be used to evaluate benchmarks and concepts, leading to a final design solution.

6. Customer Wants

7. Customer Wants (cont’d)

8. Wants  Metrics

9. 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.

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

11. 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

12. Alignment

13. Sorting

14. Target Values

15. 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

16. Concepts • Alignment • Lane Dividers • Rollers • Chains • Compartments • Identification • Imaging • Pins • Calipers • Rolling • Removal • Air Jet • Piston • Trapdoor • Tilting Tray • Robot Arm

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

19. Hardware: Digital Video Camera Frame-Grabber Data Acquisition Board Low Cost PC Software: Image processing utilities Specialized Grading software GUI for operator control over selection parameters Imaging Process • Input/Output controlled by microcomputer

20. Imaging Algorithms • Image as camera would receive it: • Processing includes: • Histogram analysis • Threshold selection • Application of an edge or range detection algorithm • Deterministic process

21. Image Flattening • Thresholded Image: • Proper threshold level is determined by Histogram analysis • A good threshold level may change slightly from batch to batch, but not often within a batch of pickles.

22. Edge Detection Algorithms • Ex: Canny Algorithm • Ex: Zero Crossings

23. Edge Detection Algorithms • Ex: Gradient Magnitude • Ex: Edge Tracking

24. Complete Model

25. Progress To Date

26. Critical Tasks in Spring

27. Estimated Hours

28. Estimated Costs

29. Closing Points • Problem Statement • Concept Selection Justification: • Alignment: Overhead Rollers • Identification: Computer Controlled Imaging • Removal: Air Propulsion • Physical Demonstration of Model.