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Automated Silverware Sorting Using Vision Based Detection and Recognition Driving Pneumatically Actuated Mechanisms

[+] Author Affiliations
Ali Asmari, Lawrence L. Hoberock

Oklahoma State University, Stillwater, OK

Paper No. DETC2012-71283, pp. 1101-1108; 8 pages
doi:10.1115/DETC2012-71283
From:
  • ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 36th Mechanisms and Robotics Conference, Parts A and B
  • Chicago, Illinois, USA, August 12–15, 2012
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4503-5
  • Copyright © 2012 by ASME

abstract

Manual Sorting of silverware pieces after being washed by a high-volume commercial dishwasher is a costly and time consuming process which can be improved by automation. This paper describes the design, construction, and testing of an automated silverware sorting process. The process employs machine vision with simple, but effective, high-volume mechanisms to detect the type and orientation of different types of silverware pieces and place them into different bins. The project was conducted in two major phases:

1) Design and Construction of the Mechanism: a simple and effective mechanism was designed to sort the pieces into separate bins off of a conveyor belt. Pneumatic actuators provided the key mechanical sorting.

2) Design of the Control System: a computer program was developed that detects the entrance of a piece into the machine and recognizes the type and orientation of each silverware piece using computer vision techniques. The software then commands the proper mechanical component at the proper time to actuate, so that each piece ends up in the designated bin.

The machine was tested with different silverware input sequences The accuracy of the software in identifying the type and direction of the pieces, the accuracy of the mechanical system in sorting the pieces, and the accuracy of the overall system were found to be 100%, 90.63% and 88.75% respectively.

Copyright © 2012 by ASME

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