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基于机器学习的高强高导Cu基复合材料力-电性能 统一预测模型研究
Unified Prediction Model for the Mechanical and Electrical Properties of High Strength and High Conductivity Cu Matrix Composite Materials Based on Machine Learning Algorithms
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- DOI:
- 作者:
- 刘 楠1，2，3，郭青成1，2，3，马麟趾1，2，3，王嘉琦1，2，3
LIU Nan1,2,3, GUO Qingcheng1,2,3, MA Linzhi1,2,3, WANG Jiaqi1,2,3
- 作者单位:
- 1. 西安理工大学 材料科学与工程学院，陕西 西安 710048； 2.陕西省电工材料与熔渗技术重点实验室，陕西 西安 710048； 3. 导电材料与复合技术教育部工程研究中心，陕西 西安 710048
1. School of Materials Science and Engineering, Xi'an University of Technology, Xi'an 710048, China; 2. Shaanxi Provincial Key Laboratory of Electrical Materials and Infiltration Technology, Xi'an 710048, China; 3. Conductive Materials and Composite Technology Engineering Research Center of the Ministry of Education, Xi'an 710048, China
- 关键词:
- 铜基复合材料；BP神经网络；蚁群算法；机器学习；导电率
Cu matrix composite; back propagation neural network; ant colony optimization; machine learning; conductivity
- 摘要:
颗粒增强铜基复合材料具有良好的力学、电学性能，但增强体特征参量与材料性能之间的定量关系难以量化确定。为建立TiB和TiB2陶瓷增强相与铜基复合材料力学与电学综合性能之间的映射关系，以求大幅提高铜基复合材料强度的同时，将其导电率降低在可接受范围内，提出了一种基于蚁群算法优化的BP神经网络铜基复合材料力-电性能统一预测模型(ACO-BP-Cu)。通过BP神经网络建立铜基复合材料性能与特征参数间关系，通过蚁群算法全局寻优确定BP神经网络模型结构。实验表明，ACO-BP-Cu模型能够根据TiB和TiB2陶瓷增强相特征参数有效预测铜基复合材料各项性能，且相对决策树、线性回归、K邻近法等9种回归算法准确率更高，稳定性更强。
Particle-reinforced copper matrix composites exhibit good force-electric performance, but the quantitative relationship between the characteristic parameters of the reinforcement and the force-electric performance is difficult to quantify. To establish the relationship between the TiB and TiB2 reinforcement and the force-electric performance of copper matrix composites, greatly improve the strength of the copper matrix and control the change in conductivity within an acceptable range, a back propagation(BP) neural network and ant colony optimization based copper matrix composite performance prediction model (ACO-BP-Cu) was proposed. The relationship between the performance of the copper matrix composites and characteristic parameters was determined via a back propagation neural network, and the model structure was determined via global optimization of ant colony algorithms. The results show that the ACO-BP-Cu model can effectively predict the performance of copper matrix composites according to the characteristic parameters of TiB and TiB2, and have higher accuracy and stability compared with 9 regression algorithms including decision tree, linear regression, and K-nearest neighbor algorithms.