Laser rapid scanning strategy for bitmaps via image segmentation utilizing grey wolf optimizer

Abstract

A novel laser scanning processing methodology is introduced for bitmap aimed at reducing time losses associated with multiple galvanometer scanning cycles. The Multi-threshold Otsu method, resolved via the Grey Wolf optimizer (GWO), effectively segments the target image into background and target regions. To handle the resulting threshold distribution, four mapping functions with distinct growth trends are to adjust pixel gray values within specified regions, thereby modulating scanning input powers accordingly. An experimental investigation was conducted utilizing a galvanometer scanner and a set of sample images. A comparative analysis of the image segmentation outcomes demonstrates that the GWO consistently delivers robust segmentation results and superior solution stability compared to other methodologies. Furthermore, when combined with the adaptive power mapping functions, this new approach shows remarkable adaptability to a range of graphical representations. Ultimately, the scanning process duration is reduced by as much as 89 % when contrasted with traditional point carving and layer-by-layer scanning. This discovery signifies that this approach holds significant promise for enhancing productivity in laser processing applications.