Erick Barrionuevo Salazar
ABSTRACT
Classification and transport of material is a crucial stage in the manufacture of parts and represents a large portion of the lead time in a production line, which is why optimization is crucial. This work deals with an omnidirectional transport mechanism for classification and quality control of parts coming from rapid prototyping processes. Said mechanism is constituted by an artificial vision system, which will be responsible for taking the necessary information to perform the classification and quality control using a neural network; and, a matrix of omnidirectional wheels that allows the movement of the piece on the XY plane. The purpose of this investigation was to demonstrate that omnidirectional mechanisms can also be used to transport and classify parts within industrial processes, being another alternative of use to conventional systems. The system is able to classify three types of pieces of different forms, sizes and perspectives with high reliability and speed; it also allows a better human-machine interaction due to a graphical interface where the performed processes are detailed.
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Index Terms
- Omnidirectional Transport System for Classification and Quality Control using Artificial Vision
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