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All Journal International Conference on Maritime Technology and Its Application
Ivan Nur Rahman
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Detection of Screen Printing Result Using U-Net Convolutional Neural Network Method to Improve Quality Control Ivan Nur Rahman; Edy Setiawan; Adianto
International Conference on Maritime Technology and Its Application Vol. 3 No. 1 (2025): ICOMTA : International Conference on Maritime Technology and Its Application
Publisher : Surabaya State Polytechnic of Shipbuilding

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35991/icomta.v1i1.3

Abstract

The increasingly competitive convection industry has compelled all enterprises to enhance production quality. One sector affected is the screen printing industry. According to information from one of the companies, customer complaints often arise from suboptimal screen printing results (defects). To achieve satisfactory outcomes, it is crucial to address this issue. Currently, the classification of result eligibility or quality control in screen printing relies on human observation. Pattern recognition technology is significantly transforming the convection industry, particularly within screen printing. This technology enhances quality control, shifting from manual processes to automated quality detection of results. Real-time pattern recognition employs image processing techniques. In this implementation, we utilize image processing with Convolutional Neural Networks for object classification., successfully identifying screen printing defects with an accuracy rate of 97%.
Co-Authors Adianto Edy Setiawan