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AI-assisted reliability assessment for gravure offset printing system.

Anton Nailevich Gafurov^1,2, Thanh Huy Phung¹, Inyoung Kim^3,4

¹Department of Printed Electronics, Korea Institute of Machinery and Materials, 156, Gajeongbuk-ro, Yuseong-gu, Daejeon, 34103, Republic of Korea.

Scientific Reports

|February 23, 2022

Summary

This study introduces an artificial intelligence (AI) approach using deep convolutional neural networks for automated quality control in printed electronics. The AI models detect printing defects, enhancing electronic device fabrication reliability.

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Hybrid Printing for the Fabrication of Smart Sensors

Hybrid Printing for the Fabrication of Smart Sensors

Published on: January 31, 2019

Area of Science:

Materials Science and Engineering
Computer Science and Artificial Intelligence

Background:

Flawless printing quality is essential for reliable electronic device fabrication.
Conventional computer vision methods face challenges in recognizing printing defects, impacting device performance and longevity.

Purpose of the Study:

To develop an artificial intelligence (AI) based computer vision approach for automated printing defect recognition and quality evaluation in printed electronics.
To enhance the accuracy and efficiency of quality control in electronic device fabrication.

Main Methods:

Collected and labeled a dataset of printed line images.
Trained and evaluated an AI model for overall printing quality classification using Grad-CAM visualization.
Fine-tuned the YOLOv3 object detection model for local printing defect detection, utilizing k-means clustering for anchor box optimization.

Main Results:

Developed AI models for both overall printing quality classification and local defect detection.
Integrated these AI models with a roll-based gravure offset system for real-time quality control.
Demonstrated the potential for improved printing reliability analysis.

Conclusions:

The proposed AI-driven computer vision approach effectively addresses the challenges in printing defect recognition.
This AI integration offers a robust solution for enhancing quality control and reliability in printed electronics manufacturing.