NCDA: Pickle Sorter Concept Review Project 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.

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 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

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 Imaging Process 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 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 AlgorithmEx: Zero Crossings

23 Edge Detection Algorithms Ex: Gradient MagnitudeEx: Edge Tracking

Complete Model

Progress To Date

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.