2,312 publications from this institution
This study presents a novel variable arc-shaped beam processing technique based on Spatial Light Modulators (SLM) for high-precision ITO (Indium Tin Oxide) conductive glass surface stripping. Traditional laser processing techniques face limitations in precision and efficiency. However, by superimposing offset holograms, arc-shaped beams of varying shapes and sizes can be generated, thus enhancing the quality and flexibility of ITO glass surface stripping. In the experiments, key parameters such as laser power and scanning speed were optimized. The results demonstrate that the use of variable arc-shaped beams significantly improves processing precision, reduces the heat-affected zone, and effectively increases processing efficiency. Microscopic observation and analysis further validate the feasibility and superiority of this method. This research provides a new approach for laser surface stripping and holds significant application value in enhancing the quality and efficiency of ITO conductive glass surface stripping.
A robust Kalman filtering (KF) algorithm based on the evolutionary programming (EP) technique is proposed in this paper, for uncertain systems with unknown-but-bounded uncertain parameters which are described by interval systems. This algorithm takes advantage of the global optima-searching capability of EP to find the optimal KF results at every iteration, which include both the upper–lower boundaries and the nominal trajectory of the optimal estimates of the system state vectors. One prominent feature of this EP filtering algorithm is that it assumes the same statistical conditions and provides the same optimal estimates as the conventional KF scheme. Both linear and nonlinear systems are studied. Two typical computer simulation examples are given with comparison, which verify the merits of the new method – it yields more accurate estimation results and is less conservative as compared to the existing interval Kalman filtering (IKF).
