【作者】 董杰；

【导师】 罗阳； 姚向东；

【作者基本信息】 四川大学 ， 机械工程， 2003， 硕士

【摘要】 铸造铝硅合金具有容重小、比强度高、铸造成形性和加工性能好等优点，已成为制造行业中最受重视的结构材料之一，该类合金中的ZL101A应用最为广泛。建立合金力学性能的模型预测，用于分析工艺参数、化学成分等对性能的影响，对提高其强度和韧性，稳定生产质量具有重要意义。 本文融合模糊推理和神经网络学习功能的自适应神经-模糊推理建模技术，不但可以逼近数据中存在的非线性关系，而且能够直接从中获得结构化知识描述，从而克服了传统反向传播等类型网络的“黑箱”问题。在预测和学习精度上，优于统计回归和单一的神经网络技术。 在此基础上，利用多年来积累的生产和实验数据，将自适应神经-模糊推理系统方法用于铸造铝硅合金性能建模，融合模糊推理系统与神经网络，把模糊系统处理不确定性方法与神经网络的连接结构和学习方法结合起来，从数据中自动提取模糊规则，实现了铸造铝硅合金强度、延伸率和硬度等的自适应模糊预报。 实验表明，本文所建立的自适应模糊预报模型，成功的实现了铸造铝硅合金力学性能以及其他材料物理性能的预报，可以用于分析工艺参数、化学成分等对性能的影响，为优化生产工艺、研制高性能合金提供依据。研究所形成的建模方法和技术具有一定的通用性和推广应用价值。更多还原

【Abstract】 With high strength, low density, good castability and machineability, Al-Si series cast alloy has become one of the most important structural materials in manufacturing industry, and ZL101A is the most widely used. In order to improve the strength and ductility, it is important to develop model for predicting material properties and analysis the role of compositions and process parameters.Besides the ability of mapping the complex non-linear relationships between compositions and properties, the proposed adaptive neuro-fuzzy inference system modeling method could extract "IF-THEN" rules directly from data, and get knowledge hidden in the data. This overcomes the "Black Box" shortcomings of BP artificial neural networks, which widely used in modeling and predicting. Further more, ANFIS has higher modeling and generation ability than regress analyses and artificial neural networks.In this paper, based upon the accumulated product records and experimental data, the adaptive neuro-fuzzy inference system (ANFIS) method has been used to build mechanical properties models of Al-Si serises cast alloy. By the coupled use of adaptive fuzzy inference modeling and artificial neural network learning ability, a set of rules, which could help us better understanding the basic principles of alloying, have been generated directly from the experimental data. The developed fuzzy inference systems could mapping the relationships between compositions and mechanical properties accurately and could used to predict material propertieswith the known compositions and/or process parameters. With the help of the developed models, one could analysis the influences of compositions and/or process on properties, and get information for the further improving of material property. By integrating in the genetic optimization procedure, the fuzzy inference systems could be used to determine the optimal compositions for the highest or desired ultimate properties.The above ANFIS modeling method has successfully used in the property prediction of Al-Si series cast alloy. The methodology present here could also be used for other data based material property modeling and optimization practices.更多还原