Artificial Neural Network Based Improved Pelican Optimization Algorithm for 3D Printed Footwear Products Design in Artificial Intelligence

The usage of three-dimensional (3D) image printing for footwear designs has expanded recently as a result of improvements in production processes and materials. Throughout the manufacturing process, enormous volumes of multi-modal data including text and graphics are generated, effectively personalizing goods remains a challenge. Thus, in this research, an artificial intelligence technique known as the Artificial Neural Network based Improved Pelican Optimization approach (ANN – IPOA) is designed to produce user-friendly material for 3D printing. First, data about shoes is collected using the Common Objects in Context (COCO) database. ANN which improves the 3D printing by mimicking pelican foraging behavior to optimize printing parameters. Then IPOA is utilized which offers effecient basis for optimizing selection. According to the results it illustrates that the proposed model has achieved better performance in terms of accuracy of 98.95% and print time of 1hr 12 min which is comparatively higher than existing models namely Mask Region Convolutional Neural Network (Mask RCNN) and Deep Neural Network (DNN).