Intelligent Data-Driven Adaptive Method for Optimizing System Integration Scaling Factors for Touch Panel Lamination Machines
提出一种智能数据驱动自适应方法，用于自动触摸面板贴合机的在线实时优化，通过结合正交阵列和信噪比，在少于40次实验中找到最小化迭代次数的系统集成缩放因子，实现5微米内的定位精度。
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 μm. 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.