In this study, we developed a deep learning-based real-time fault diagnosis system to automate the weaving preparation process in textile manufacturing. By analyzing typical faults such as shaft eccentricity and rotational imbalance, we designed a data-driven fault diagnosis algorithm. We utilized tension data from both normal and faulty states to implement AI-based diagnostic models, including 1D CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM-AE (Long Short-Term Memory Autoencoder). These models enable real-time fault classification, followed by a comparative performance analysis. The LSTM-AE model achieved the best performance, with a classification accuracy of 99-100% for severe faults, such as 1.5 mm eccentricity and 100 or 150 g rotation imbalance, and 92.2% for minor faults like 1 mm eccentricity. This accuracy was optimized through threshold adjustments based on ROC curve analysis to select an optimal threshold. Building on these findings, we developed a GUI (Graphical User Interface) system capable of real- time fault diagnosis using TCP/IP (Transmission Control Protocol/Internet Protocol) communication between Python and LabVIEW. The results of this study are expected to accelerate the smartization of the weaving preparation process, contributing to improved textile quality and reduced defect rates, while also serving as a model for automation in other sectors.

Printed electronics is a manufacturing technology that fabricates electronic devices using printing techniques. Due to its characteristics of low cost and simple process, a roll-to-roll printing technique has been used to achieve the large area and mass production of flexible electronic devices such as a thin film transistor. In the roll-to-roll printing process, a fidelity of the engraved pattern position is one of the most important techniques to fabricate high resolution multi-layer electronic devices. In this study, an engraved register mark position measurement system was developed to numerically evaluate the position accuracy of engraved mark in printing roll. The proposed system is based on a high-precision encoder based position control system and a high-resolution machine vision system. The measurement error of the developed system is within the camera resolution ±2.1 μm, verifying the superiority of the system. Using the developed system, we measured the position errors of the engraved register marks for six industrial scale printing rolls. This study suggests that the position error of the engraved mark should be considered to achieve a high precision register control below ±10 μm and necessity of the developed system.