基于人工神经网络和遗传算法的复合材料涂层工艺优化
发布时间：2018-11-09 11:28
【摘要】：工艺-组织-性能之间的关系始终是材料科学研究的主题。长期以来,对材料研究采用的是依赖大量实验、或传统的试错法等经验或半经验的材料研究方法,这需要消耗大量的人力、物质资源和时间。随着计算机技术的发展,人们逐渐将人工智能技术应用到材料研究中,试图通过较少的实验获得较为理想的结果。人工神经网络和遗传算法是人工智能领域技术纯熟,且迄今为止应用较多的两种关键技术。将这两种算法融合,既可以发挥各自的优点,又可以避免两者单独使用的固有缺点,从而使对工艺的建模更精确有效。本文主要对人工智能领域的两个热点问题——人工神经网络和遗传算法进行分析和改进,尝试将这两种算法融合,并运用到羟基磷灰石-C/C复合材料的感应热沉积工艺和芳纶纤维化学镀镍工艺优化中。论文取得以下主要研究成果:1、设计了两种BP神经网络与遗传算法融合的方法:基于遗传算法的BP神经网络优化方法以及基于神经网络和遗传算法的非线性函数极值寻优方法。2、基于神经网络和遗传算法的非线性函数极值寻优方法,对C/C复合材料表面制备羟基磷灰石工艺进行了建模,获得最佳工艺条件,并对感应热沉积工艺的热力学和动力学的研究具有一定的指导意义。3、基于神经网络和遗传算法的两种融合算法,对芳纶纤维表面化学镀镍工艺进行了建模,获得最佳工艺条件:镀液中柠檬酸钠浓度为12.00 g/L,氯化铵浓度为24.00 g/L,次亚磷酸钠浓度为28.00 g/L,p H值为9.19,温度为50℃,化学镀镍的沉积速率为1.91 g/(g?min)。
[Abstract]:The relationship between process-structure and properties has always been the subject of material science research. For a long time, the material research is based on a large number of experiments, or the traditional trial and error methods and other empirical or semi-empirical material research methods, which need to consume a lot of manpower, material resources and time. With the development of computer technology, artificial intelligence technology has been gradually applied to material research, trying to obtain ideal results through fewer experiments. Artificial neural network (Ann) and genetic algorithm (GA) are two key technologies in the field of artificial intelligence. The fusion of the two algorithms can not only give full play to their respective advantages, but also avoid the inherent disadvantages of using the two algorithms separately, thus making the process modeling more accurate and effective. In this paper, two hot issues in artificial intelligence field, artificial neural network (Ann) and genetic algorithm (GA), are analyzed and improved. It was applied to the optimization of induction thermal deposition process of hydroxyapatite / C / C composite and electroless nickel plating of aramid fiber. The main research results are as follows: 1. Two methods of fusion of BP neural network and genetic algorithm are designed: BP neural network optimization method based on genetic algorithm and nonlinear function extremum optimization method based on neural network and genetic algorithm. Based on the nonlinear function extremum optimization method based on neural network and genetic algorithm, the preparation process of hydroxyapatite on the surface of C / C composite is modeled and the optimum process conditions are obtained. It has certain guiding significance for the study of thermodynamics and kinetics of inductive thermal deposition. 3. Based on two fusion algorithms of neural network and genetic algorithm, the electroless nickel plating process on the surface of aramid fiber is modeled. The optimum process conditions were obtained as follows: sodium citrate concentration was 12.00 g / L, ammonium chloride concentration was 24.00 g / L, sodium hypophosphite concentration was 28.00 g / L ~ (-1) H value was 9.19, and temperature was 50 鈩，
本文编号：2320274
[Abstract]:The relationship between process-structure and properties has always been the subject of material science research. For a long time, the material research is based on a large number of experiments, or the traditional trial and error methods and other empirical or semi-empirical material research methods, which need to consume a lot of manpower, material resources and time. With the development of computer technology, artificial intelligence technology has been gradually applied to material research, trying to obtain ideal results through fewer experiments. Artificial neural network (Ann) and genetic algorithm (GA) are two key technologies in the field of artificial intelligence. The fusion of the two algorithms can not only give full play to their respective advantages, but also avoid the inherent disadvantages of using the two algorithms separately, thus making the process modeling more accurate and effective. In this paper, two hot issues in artificial intelligence field, artificial neural network (Ann) and genetic algorithm (GA), are analyzed and improved. It was applied to the optimization of induction thermal deposition process of hydroxyapatite / C / C composite and electroless nickel plating of aramid fiber. The main research results are as follows: 1. Two methods of fusion of BP neural network and genetic algorithm are designed: BP neural network optimization method based on genetic algorithm and nonlinear function extremum optimization method based on neural network and genetic algorithm. Based on the nonlinear function extremum optimization method based on neural network and genetic algorithm, the preparation process of hydroxyapatite on the surface of C / C composite is modeled and the optimum process conditions are obtained. It has certain guiding significance for the study of thermodynamics and kinetics of inductive thermal deposition. 3. Based on two fusion algorithms of neural network and genetic algorithm, the electroless nickel plating process on the surface of aramid fiber is modeled. The optimum process conditions were obtained as follows: sodium citrate concentration was 12.00 g / L, ammonium chloride concentration was 24.00 g / L, sodium hypophosphite concentration was 28.00 g / L ~ (-1) H value was 9.19, and temperature was 50 鈩，
本文编号：2320274
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