Neural Network Paradigm for Classification of Defects on PCB
DOI:
https://doi.org/10.11113/jt.v39.465Abstract
Satu teknik baru dicadangkan untuk mengkelaskan kerosakan yang boleh terjadi pada PCB menggunakan paradigma rangkaian neural. Algoritma untuk membahagi–bahagikan imej menjadi corak primitif, melingkupi corak primitif berkenaan, penandaan corak, normalisasi corak, dan pengkelasan telah dibangunkan berdasarkan pemprosesan imej morfologi penduaan dan rangkaian neural Learning Vector Quantization (LVQ). Ribuan corak rosak telah digunakan untuk tujuan latihan, dan rangkaian neural diuji untuk menilai prestasinya. Satu imej PCB yang rosak digunakan untuk memastikan teknik yang dicadangkan berfungsi. Kata kunci: PCB, pengkelasan kerosakan, pemprosesan imej morfologi, LVQ A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. Thousands of defective patterns have been used for training, and the neural network is tested for evaluating its performance. A defective PCB image is used to ensure the function of the proposed technique. Key words: PCB, defects classification, morphological image processing, LVQDownloads
Published
2012-01-20
Issue
Section
Science and Engineering
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How to Cite
Neural Network Paradigm for Classification of Defects on PCB. (2012). Jurnal Teknologi (Sciences & Engineering), 39(1), 87–104. https://doi.org/10.11113/jt.v39.465