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Kasdianto Kasdianto
Politeknik Negeri Batam
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Desain Sistem Pendeteksi untuk Citra Base Sub-assembly dengan Algoritma Backpropagation Kasdianto Kasdianto; Siti Aisyah
Jurnal Rekayasa Elektrika Vol 13, No 1 (2017)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (940.717 KB) | DOI: 10.17529/jre.v13i1.4368

Abstract

Object identification technique using machine vision has been implemented in industrial of electronic manufacturers for years. This technique is commonly used for reject detection (for disqualified product based on existing standard) or defect detection. This research aims to build a reject detector of sub-assembly condition which is differed by two conditions that are missing screw and wrong position screw using neural network backpropagation. The image taken using camera will be converted into grayscale before it is processed in backpropagation methods to generate a weight value. The experiment result shows that the network architecture with two layers has the most excellent accuracy level. Using learning rate of 0.5, target error 0.015%, and the number of node 1 of 100 and node 2 of 50, the successive rate for sub-assembly detection in right condition reached 99.02% while no error occurs in detecting the wrong condition of Sub-assembly (missing screw and wrong position screw).
Desain Sistem Pendeteksi untuk Citra Base Sub-assembly dengan Algoritma Backpropagation Kasdianto Kasdianto; Siti Aisyah
Jurnal Rekayasa Elektrika Vol 13, No 1 (2017)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v13i1.4368

Abstract

Object identification technique using machine vision has been implemented in industrial of electronic manufacturers for years. This technique is commonly used for reject detection (for disqualified product based on existing standard) or defect detection. This research aims to build a reject detector of sub-assembly condition which is differed by two conditions that are missing screw and wrong position screw using neural network backpropagation. The image taken using camera will be converted into grayscale before it is processed in backpropagation methods to generate a weight value. The experiment result shows that the network architecture with two layers has the most excellent accuracy level. Using learning rate of 0.5, target error 0.015%, and the number of node 1 of 100 and node 2 of 50, the successive rate for sub-assembly detection in right condition reached 99.02% while no error occurs in detecting the wrong condition of Sub-assembly (missing screw and wrong position screw).
Co-Authors Siti Aisyah