Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics

Abstract

Gravure printing, which is a roll-to-roll printed electronics system suitable for high-speed patterning of functional layers have advantages of being applied to flexible webs in large areas. As each of the printing procedure from inking to doctoring followed by ink transferring and setting influences the quality of the pattern geometry, it is necessary to detect and diagnose factors causing the printing defects beforehand. Data acquisition with three triaxial acceleration sensors for fault diagnosis of four major defects such as doctor blade tilting fault was obtained. To improve the diagnosis performances, optimal sensor selection with Sensor Data Efficiency Evaluation, sensitivity evaluation for axis selection with Directional Nature of Fault and feature variable optimization with Feature Combination Matrix method was applied on the raw data to form a Smart Data. Each phase carried out on the raw data progressively enhanced the diagnosis results in contents of accuracy, positive predictive value, diagnosis processing time, and data capacity. In the case of doctor blade tilting fault, the diagnosis accuracy increased from 48% to 97% with decreasing processing time of 3640 s to 16 s and the data capacity of 100 Mb to 5 Mb depending on the input data between raw data and Smart Data.

Image Data-Centric Visual Feature Selection on Roll-to-Roll Slot-Die Coating Systems for Edge Wave Coating Defect Detection

“…Roll-to-roll (R2R) systems have emerged as promising technologies for high-speed, large-scale production across various areas, including flexible electronics, sensors, and electric vehicle batteries [ 1 , 2 , 3 , 4 ]. These systems provide continuous manufacturing processes, offering advantages such as increased production efficiency, reduced material waste, and enhanced product quality and consistency [ 5 , 6 ]. In an R2R setup, materials are continuously fed into the system as rolls, enabling rapid production as the manufacturing process occurs on the moving material web [ 7 , 8 , 9 ].…”

“…Recent studies have highlighted the significance of training time and data requirements in addition to accuracy [ 19 , 20 , 21 , 22 , 23 ]. Identifying relevant features that can enhance diagnostic accuracy, reduce processing time, and optimize data capacity for real-time monitoring is crucial [ 5 , 24 ]. However, current research primarily focuses on quantitative data, such as tension and vibration, when examining the features influencing multiple performance factors.…”

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

“…As a result, researchers have accelerated the development of data-driven intelligent health-diagnostic models using machine learning and deep learning [ 26 , 27 , 28 ]. Machine learning methods and techniques generally follow a sequence of sensor-data collection, data-quality assessment, feature extraction, feature selection, and model training [ 29 , 30 ]. Data are collected from sensors attached to the machine, and vibration sensors have proven to be effective in diagnosing faults in rotating components in several studies [ 31 , 32 , 33 , 34 ].…”

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

“…A fault diagnosis system aims to improve the productivity of a process and reduce the costs by monitoring the manufacturing process and notifying it of any failures in advance [1][2][3][4][5][6]. Machining is one of the processing methods used in product manufacturing.…”