Optimizing System Integration Scaling Factors for Auto-alignment Machines by Using Intelligent Computing Methods

• Main Authors: Chorng-Tyan Lin, 林崇田
• Other Authors: Jyh-Horng Chou
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/xptgtz

博士 === 國立高雄應用科技大學 === 電機工程系博碩士班 === 106 === This thesis mainly studies how to quickly and accurately obtain the "system integration scaling factors" between the physical space, vision system, and the alignment system by the intelligent computing methods for the high precision equipment. First of all, to quickly get the best initial system integration scaling factors, a soft computing technology is proposed for optimizing the system integration scaling factors for an exposure machine for flexible printed circuit boards. The proposed technology integrates a full factorial experimental design, a multilayer perceptron (MLP) artificial neural network, and the Taguchi-based-genetic algorithm (TBGA). First, a full factorial experimental design is used to conduct experiments and to accumulate data that represent the system integration scaling factors of an exposure machine. The MLP is then used to build a positioning model of an exposure machine by minimizing the performance criterion of mean squared error. Finally, the TBGA optimize the system integration scaling factors for the exposure machine. The experimental results demonstrate the excellent performance of the MLP-TBGA approach in obtaining system integration scaling factors for decreasing the number of iterations and the alignment time. Secondly, how to quickly get a new set of system integration scaling factors when the system integration scaling factors are no longer optimal after a machine had been used for a while. We developed 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. The main advantage of the proposed IDAM over conventional methods is its effectiveness for automatically finding robust system integration scaling factors system integration scaling factors for online alignment systems in real time and with fewer experiments.