83 Citations (Scopus)

Abstract

Visual inspection process for weld defects still manually operated by human vision, so the result of the test still highly subjective. In this research, the visual inspection process will be done through image processing on the image sequence to make data accuracy more better. CNN as one of the image processing technique can determine the feature automatically which is suitable for this problem in order to classify the variation of each weld defect pattern. Classification using Convolution Neural Network (CNN) consist of two stages: extraction image using image convolution and image classification using neural network. Gaussian kernel used for blurring image, it helps the extraction of images without losing the main information from the original image, this filter also minimize the occurrence of interference or noise. Results of the classification used to get the category of weld defects with high accuracy as a variable of a weld inspection process whether the weld is pass the standard or not. The proposed system has obtained classification with validation accuracy of 95.83% for four different type of welding defect. The data input of this research is the result of images captured by a webcam.

Original languageEnglish
Title of host publication2017 International Seminar on Intelligent Technology and Its Application
Subtitle of host publicationStrengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-265
Number of pages5
ISBN (Electronic)9781538627068
DOIs
Publication statusPublished - 28 Nov 2017
Event18th International Seminar on Intelligent Technology and Its Application, ISITIA 2017 - Surabaya, Indonesia
Duration: 28 Aug 201729 Aug 2017

Publication series

Name2017 International Seminar on Intelligent Technology and Its Application: Strengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding
Volume2017-January

Conference

Conference18th International Seminar on Intelligent Technology and Its Application, ISITIA 2017
Country/TerritoryIndonesia
CitySurabaya
Period28/08/1729/08/17

Keywords

  • Convolution neural network
  • Visual Inspection
  • Welding defect

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