BP neural network based flexural strength prediction of open-porous Cu-Sn-Ti composites

• 作 者: Biao Zhao Tianyu Yu Wenfeng Di
• 机 构: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics
• 出 处: 自然科学进展（英文版）
• 发表日期: 2018年3期
• 关键词: Flexural strength BP artificial neural network Training algorithms Metallic poro

Open-porous Cu-Sn-Ti composites are fabricated by the space holder sintering technique using carbamide par-ticles as space-holder material. Generally, the mechanical properties of open-porous sintered composites, especially the flexural strength affect the machine tools wear significantly. In this paper, a back-propagation (BP) artificial neural network with genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) was then employed to relate the composition parameters (pore size, porosity and concentration of molybdenum disulfide particles) to the flexural strength. Furthermore, a comparison of predicted and experimental results using GA-BP and PSOA-BP models was conducted and good prediction accuracy was obtained. The study showed that PSOA-BP models could achieve better prediction results in aspects of the higher convergence velocity, lower relative errors of the flexure strength utilizing GA-BP models. Finally, the high porosity and desired flexural strength were achieved by optimizing the input parameters of open-porous Cu-Sn-Ti composites.