Defect Detection in Printed Board Circuit using Image Processing
N. Munisankar1, S. Nagarajan2, B. Narendra Kumar Rao3
1N. Munisankar, Department of Computer Science and Engineering, Annamalai University, Chidambaram, India.
2Dr. S. Nagarajan, Department of Computer Science and Engineering, Government college of Engineering, Trichy, India.
3B. Narendra Kumar Rao, Department of Computer Science and Engineering, Sree Vidyanekethan Engineering College, Autonomous, Tirupati, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 3005-3010 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6649129219/2019©BEIESP | DOI: 10.35940/ijitee.B6649.129219
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Abstract: A printed circuit board without connecting with any components called as a bare PCB. Consider a PCB as a basic part which has been settled with more electronic units. In order to display the manufacturing process, the drawbacks have been taken by PCB individually. The reflection of this separation process impacts the performance of the circuits. Also, we have examined about classification methodologies as well as referential based PCB detection. From the input images, the needed and related information has been pulled out using image processing methodologies by the referential based PCB detection. Comparing with the un-defected PCB images, this was used to find out the defects. To meet the goal of the PCB defect detection, several feature extraction and pre-processing methods are derived in this article. The PCB defects have been classified by those features using the machine learning algorithms. Moreover, several types of machine learning algorithms are derived in this article. This paper helps the researchers for achieving a better solution for image processing and machine learning-based printed circuit board the defect classification.
Keywords: Image Processing, Printed Circuit board, Machine learning, Defect Essing, Printed Circuit Board, Machine learning, Pefect Classification, feature Extraction
Scope of the Article: Classification