Paper Info
Title
Intelligent Data-Driven Adaptive Method for Optimizing System Integration Scaling Factors for Touch Panel Lamination Machines
Abstract
This paper presents a new intelligent data-driven adaptive method (IDAM) of performing automatic online searches in real time. An online implementation of the proposed IDAM achieved rapid real-time optimization of system integration scaling factors for an automatic touch panel lamination machine. The proposed IDAM combines three-level orthogonal arrays (OAs), signal-to-noise ratios (SNRs), the best combined strategy, and a stepwise ratio. Three-level OA experiments with factor values are used to perform positional experiments, and SNRs are calculated for each experimental value. After the best combination of factor values (in terms of factor effect) is determined, new three-level factor values are derived by applying a stepwise ratio and used in further three-level OA experiments. These steps are repeated until the stopping criterion is met. Compared to conventional methods, the use of the IDAM in practical industrial applications, i.e., online real-time precision positioning for automatic touch panel lamination machines, reduces the number of experiments needed to obtain the system integration scaling factors that minimize the iteration count. For example, the IDAM required less than 40 online real-time experiments with a specific stepwise ratio for system integration scaling factors that met the minimum requirement of two iterations. In 50 independent experimental runs using the robust scaling factors obtained by the method, an average of 2.15 iterations was needed to achieve a positional accuracy within 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> . The main advantage of the proposed IDAM over conventional methods is its effectiveness for automatically finding robust parameters for online alignment systems in real time and with fewer experiments.
Year
DOI
Venue
2020
10.1109/TSMC.2017.2707441
IEEE Transactions on Systems, Man, and Cybernetics
Keywords
Field
DocType
Servomotors,Real-time systems,Lamination,Machine vision,System integration,Optimization,Feedback control
Lamination,Mathematical optimization,Data-driven,Adaptive method,Control engineering,Scaling,Mathematics,System integration
Journal
Volume
Issue
ISSN
50
4
2168-2216
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Jinn-Tsong Tsai191.55
Chorng-Tyan Lin252.57
jyhhorng3213.61