Research on Optimization of 3D Printing Model Based on Deep Learning

Abstract

3D printing is a process of creating three-dimensional physical objects through a series of layering techniques. It is a relatively new technology, but it has already brought a lot of innovation and change to manufacturing and other fields. This paper introduces the basic mechanism of 3D printer and summarizes several advanced 3D printing model optimization methods based on deep learning and their application scenarios. The proposed approach involves training a deep neural network on a large dataset of 3D models and their corresponding printing parameters. The trained model is then used to detect printing parameters of different 3D models, such as lattice defect detection, laser temperature detection, and fault detection. This method improves printing quality and reduces printing time and material consumption. The experimental results show that these methods are superior to traditional methods and achieve a good balance between print quality and efficiency. The proposed method has great application potential in various fields such as manufacturing, prototyping, and art design.