Hypothesis Open Access

Real-Time Automatic Inspection System for the Classification of PCB Flux Defects

Ang Teoh Ong1, Aouache Mustapha2, Zulkifilie Bin Ibrahim3, Suzaimah Ramli4 and Boo Chai Eong5
  • 1 Control Easy Technology Sdn. Bhd, Malaysia
  • 2 Universiti Kebangsaan Malaysia, Malaysia
  • 3 Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
  • 4 Universiti Pertahanan National Malaysia, Malaysia
  • 5 Technical Development Manager, Malaysia
American Journal of Engineering and Applied Sciences
Volume 8 No. 4, 2015, 504-518

DOI: https://doi.org/10.3844/ajeassp.2015.504.518

Submitted On: 25 June 2015 Published On: 25 August 2015

How to Cite: Ong, A. T., Mustapha, A., Ibrahim, Z. B., Ramli, S. & Eong, B. C. (2015). Real-Time Automatic Inspection System for the Classification of PCB Flux Defects. American Journal of Engineering and Applied Sciences, 8(4), 504-518. https://doi.org/10.3844/ajeassp.2015.504.518

Abstract

Automatic inspection based on a real-time machine vision system may serve as substitute for the manual human visual inspection of flux defects in Printed Circuit Boards (PCBs), which often cause damage on the board in the form of corrosions that harm the assembly. The concept of automatic inspection contributes to the improvement of the manufacturing quality of PCBs and facilitates their approval or rejection. The Automatic Inspection System for Printed Circuit Boards (AIS-PCB) is developed with the capability to identify the defects and the quality of PCBs. It is based on a real time system machine vision. The developed AIS-PCB is capable of detecting, indexing and classifying by measuring the flux defects in PCBs during the re-flow of the real-time process. The AIS-PCB is The total automation control system is the core of the AIS-PCB. This system consist of vision inspection station, mechanical loader and unloader, final decision station and the pneumatic system handler. To detect and classify the quality of PCBs, segmentation in conjunction with Radon transform approaches are used for feature indexing and line detection based on the gradient field of PCB images. The Feed-Forward Back-Propagation (FFBP) model is used to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed to learn and match the targets. The images of each PCB classes are used as inputs to the classification module. The obtained results from the classification and rule decision are used to establish the receiver operating characteristic curve. The classifier, which is based on the proposed approach and is tested on the PCBs from a factory’s production line, achieves a sorting Coefficient Of Efficiency (COE >95%). The developed AIS-PCB system shows promising results in successfully segmenting and classifying flux defects in PCBs through computerized visual information and facilitates their automatic inspection, thereby aiding humans in conducting rapid inspections.

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Keywords

  • PCB Flux Image
  • Segmentation Approach
  • Classification Approach
  • Automatic Machine Vision System