Autors: Mitev, P. V.
Title: Development of a Training Station for the Orientation of Dice Parts with Machine Vision †
Keywords: automatic parts feeding, dice, machine vision, PLC, vibratory bowl feeder

Abstract: This paper reviews the process of research, development and production of a training station for the optical recognition of dice parts with machine vision. This approach is chosen due to the lack of mechanical features to allow for classical orientation approaches. The embossed dots are about 0.1–0.2 mm deep so it is impossible to design classical traps. The orientation occurs purely by visual comparison to a reference image, part of the current camera job. The sequence of parts is controlled by the programmable logic controller(PLC)program, which manages the camera job-changing process via I/O signals, thus ensuring the right face of the die is captured by the camera and achieving the right predefined order of the sequence. When the preset number of dice in the sequence is reached, they are released back to the vibratory bowl feeder by a pneumatic separator. This way, all dice parts circulate until they are recognized by the camera. There are jobs for each possible orientation of the dice and also a small HMI where the dice sequences could be adjusted by the operator(generally students). The main benefit for the students is the opportunity to program the PLC and to adjust the camera jobs for the detection of each possible orientation. This relies upon the fact that during the fall from the return conveyor to the bowl feeder, the parts flip and, thus, change their previous orientation to another side. Experiments are conducted regarding the probability of obtaining orientation “5” and all the other possible states in order to statistically express the probability.

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Issue

Engineering Proceedings, vol. 70, 2024, Greece, https://doi.org/10.3390/engproc2024070057

Full text of the publication

Цитирания (Citation/s):
1. Malakov I., Zaharinov V., Hasansabri H., Development of Optimal Size Range of Modules for Driving of Automatic Sliding Doors, 2025, Algorithms, issue 5, vol. 18, DOI 10.3390/a18050248, eissn 19994893 - 2025 - в издания, индексирани в Scopus
2. Naskinova I., Kolev M., Karova D., Milev M., Machine Learning-Based Blood Pressure Prediction Using Cardiovascular Disease Data: A Comprehensive Comparative Study, 2026, Electronics Switzerland, issue 2, vol. 15, DOI 10.3390/electronics15020312, eissn 20799292 - 2026 - в издания, индексирани в Scopus

Вид: статия в списание, публикация в реферирано издание, индексирана в Scopus