Optimized Placement Study of 3D Printing Models Based on Nelder-Mead Algorithm

The rapid development of 3D printing technology has driven a transformation in the manufacturing industry. However, the optimization of model placement remains a significant challenge in ensuring print quality and improving production efficiency. This key issue focuses on the rational addition of supports and the precise modeling of surface error volume, aiming to explore more effective model placement algorithms. The research findings reveal that appropriate support design can not only effectively reduce support defects during the printing process but also significantly enhance structural stability. Through the analysis of patch normal vectors, the study proposes two support configuration strategies and emphasizes the central role of accurate error assessment in model optimization. The main objective of this research is to deepen the understanding of the characteristics and printing behavior of 3D printing models through systematic technical analysis, thereby providing a reliable basis for theoretical research and industrial practice. Optimizing model placement is not only a technical challenge but also a comprehensive test for 3D printing designers and engineers, influencing the prospects for various innovative applications. As the research progresses, the constructed optimization model demonstrates its potential in improving print quality and reducing material waste, especially in addressing printing tasks that require complex shapes and high precision, showcasing its strong application value. This exploration of 3D printing embodies profound reflections and aspirations for the future of manufacturing, igniting anticipation and enthusiasm for new technologies both within and outside the industry.