Automated Optical Inspection (AOI) is among the most common and effective quality checks employed in production lines. This paper details the design of a Deep Learning solution that was developed for addressing a specific quality control in a Printed Circuit Board Assembly (PCBA) manufacturing process. The developed Deep Neural Network exploits transfer learning and a synthetic data generation process to be trained even if the quantity of the data samples available is low. The overall AOI system was designed to be deployed on low-cost hardware with limited computing capabilities to ease its deployment in industrial settings.

Automated Optical Inspection for Printed Circuit Board Assembly Manufacturing with Transfer Learning and Synthetic Data Generation / Saif, S. S.; Aras, K.; Giuseppi, A.. - (2022), pp. 318-323. (Intervento presentato al convegno 30th Mediterranean Conference on Control and Automation, MED 2022 tenutosi a Athens) [10.1109/MED54222.2022.9837280].

Automated Optical Inspection for Printed Circuit Board Assembly Manufacturing with Transfer Learning and Synthetic Data Generation

Giuseppi A.
2022

Abstract

Automated Optical Inspection (AOI) is among the most common and effective quality checks employed in production lines. This paper details the design of a Deep Learning solution that was developed for addressing a specific quality control in a Printed Circuit Board Assembly (PCBA) manufacturing process. The developed Deep Neural Network exploits transfer learning and a synthetic data generation process to be trained even if the quantity of the data samples available is low. The overall AOI system was designed to be deployed on low-cost hardware with limited computing capabilities to ease its deployment in industrial settings.
2022
30th Mediterranean Conference on Control and Automation, MED 2022
AOI; Automated Optical Inspection; deep learning; industrial assembly lines; neural networks; quality control
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Automated Optical Inspection for Printed Circuit Board Assembly Manufacturing with Transfer Learning and Synthetic Data Generation / Saif, S. S.; Aras, K.; Giuseppi, A.. - (2022), pp. 318-323. (Intervento presentato al convegno 30th Mediterranean Conference on Control and Automation, MED 2022 tenutosi a Athens) [10.1109/MED54222.2022.9837280].
File allegati a questo prodotto
File Dimensione Formato  
Said_postprint_Automated_2022.pdf

accesso aperto

Note: DOI 10.1109/MED54222.2022.9837280
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.1 MB
Formato Adobe PDF
4.1 MB Adobe PDF
Said_Automated_2022.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.81 MB
Formato Adobe PDF
4.81 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1654505
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact