Скачать презентацию NCDA Pickle Sorter Final Design Review Project 98 Скачать презентацию NCDA Pickle Sorter Final Design Review Project 98

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NCDA: Pickle Sorter Final Design Review Project 98. 09 Sponsored by Ed Kee of NCDA: Pickle Sorter Final Design Review Project 98. 09 Sponsored by Ed Kee of Keeman Produce, Lincoln, DE

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

3 Background • Title: Pickle Sorter • Sponsor: Ed Kee of Keeman Produce • 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 Undesirable Cucumbers • 3 Catagories: – Oversized (Diameter > 2 in. ) – 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 %

Plant Schematic 5 Plant Schematic 5

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

7 Approach • Collect customer wants and constraints and develop them into metrics • 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 Customers and Wants 8 Customers and Wants

9 Wants Metrics 9 Wants Metrics

10 Benchmarking • Patents, Internet and Trade Journals • System: – Integrated production line 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 System Benchmarks • Machine Vision common to all System Benchmarks • Typical Sorting 11 System Benchmarks • Machine Vision common to all System Benchmarks • Typical Sorting Parameters - Color, Size(length), Surface Features • Best Practices

12 Functional Benchmarks Alignment • Common Material Handling Task • Best Practices: lane dividers, 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 Sorting 13 Sorting

14 Alignment 14 Alignment

15 Target Values 15 Target Values

16 Concept Generation Benchmarking • Functions Which Satisfy Target Values • Best Practices • 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 Concepts Alignment Identification Removal 1 Lane Dividers 1 Imaging 1 Air Jet 2 17 Concepts Alignment Identification Removal 1 Lane Dividers 1 Imaging 1 Air Jet 2 Rollers 2 Pins 2 Piston 3 Chains 3 Calipers 3 Trapdoor 4 Compartments 4 Rolling 4 Tilting Tray 5 Robot Arm

18 Concepts (cont’d) Piezoelectric Pins – Displacement of pins in field creates 3 -D 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

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

21 Imaging Equipment • Hardware: – Self-Contained Digital Camera – PC for Software Development 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 Imaging Process • Algorithm I – Camera continually takes images until an entire 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 Ejection System Testing 23 Ejection System Testing

24 Test Plan and Results 24 Test Plan and Results

Complete Model 25 Complete Model 25

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27 Budget Engineering Development Time – Alignment 32 hrs – Ejection 86 hrs – 27 Budget Engineering Development Time – Alignment 32 hrs – Ejection 86 hrs – Imaging 140 hrs Fabrication Time – Alignment 20 hrs – Ejection 72 hrs – Imaging 54 hrs Expenditures

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