Sistem Pemilahan Barang Berdasarkan Deteksi Label Menggunakan Vision Sensor

Authors

  • Carolus Henry Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Melisa Mulyadi Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Theresia Ghozali Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Linda Wijayanti Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Kumala Indriati Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia

DOI:

https://doi.org/10.25170/jurnalelektro.v17i1.5407

Keywords:

labels, pneumatics, programmable logic controllers, robot arms, stock keeping units, vision sensors

Abstract

Sorting goods based on the results of checking packaging labels is an important process in controlling production quality in industry. Many industries still carry out manual sorting and label checking processes, which results in low productivity levels and is susceptible to human error. This research develops an automation system for sorting goods based on label inspection using vision sensors, programmable logic controller (PLC), and robot arm. The system controlled by a PLC will read and detect damage to packaging labels by using VeriSens vision sensor and sort them using a robot arm according to predetermined stock keeping unit (SKU) categories, namely SKU 1, SKU 2, SKU 3, SKU 4, and rejected goods. The pneumatic system is used as an actuator to push goods onto the conveyor, moving the robot arm with three degrees of freedom and vacuum. Detection is carried out by applying the edge detection concept to read text, images and code that are reprocessed with the VeriSens Application Suite software. The success rate of the goods sorting system reached 90% with a reading speed of  0.389 seconds and a work process duration ranging from 21.54 seconds to 28.99 seconds.

References

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Published

2024-04-29
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