【作者】 曲冬；

【导师】 钟厉；

【作者基本信息】 重庆交通大学 ， 载运工具运用工程， 2010， 硕士

【摘要】 形状记忆合金(SMA, Shape Memory Alloy)作为一种集传感和驱动于一身的新型功能材料,近年来引起了工程界的广泛关注,利用SMA提高结构的控制性能,对结构进行智能控制已成为一个重要的研究方向。国内外众多学者开展了相关的研究工作,并取得了一定的成果。而对于SMA的力学特性、本构模型以及BP神经网络预测SMA回复力模型等问题的研究还处于探索阶段,仍需进一步的深入研究。本文利用过盈夹持原理自制夹具,在万能试验机上使NiTi SMA丝实现一定的预应变后,再在经改装的10kN的BZ2216数字式测力仪上测定合金丝的回复力的大小,研究其随着温度升高和降低的变化规律;同时讨论了温度、激励模式、激励次数及预应变量等因素对NiTi SMA丝回复力的影响。结果表明:NiTi SMA合金丝的回复力随温度的上升而增大,当温度高于合金的软化点时,合金发生软化,回复力开始下降;预应变越大,合金的回复力越大,但是当超过一定的预应变(8%)后,合金的回复力反而下降;与增大电流强度的激励模式比较,缓慢提高电流强度的激励模式,马氏体转变为奥氏体的量增多,合金的回复力增大;合金多次激励仍有回复力。同时本文利用神经网络的非线性映射能力,根据SMA丝在不同应变幅值条件下的试验结果,建立了基于神经网络的SMA回复力模型。由计算结果和试验数据的对比分析可知,该模型可以很好地映射SMA应力、应变、温度之间的非线性关系,且有良好的预测能力,能够在一定程度上正确预测出SMA在给定应变下的应力值,验证了这种BP神经网络模型的实用性。更多还原

【Abstract】 As a material of novel function, shape memory alloy(SMA) used as an integrated sensor and actuator has drawn much attention in engineering in recent years.The utilizaion of SMA to improve the intelligent control of structures has become an important research topic. Many scholars carry out the relevant research work and have achieved some results.However, some areas of the study are still in an exploratory or developing stage,such as the mechanical properties and constitutive modeling of SMAs and the BP neural network model of SMA recovery stress.Therefore,it is necessary to continue and further the knowledge base in this area.In this paper, the recovery stress of NiTi SMA wires’has been tested on the BZ2216 Digital Force Measurement Apparatus after refit, and its’change law with the increase and decrease of temperature has been studied, after being prestrained on the Hydraulic Universal Testing Machine, using the self-made fixture on the principle of interference coordinating. Meanwhile, the effect of temperature, incentive modes and incentive times, as well as prestrain level on NiTi SMA wires’have been discussed. It is shown that, the recovery stress of NiTi SMA increases with the increase of temperature, when over the softening point, the alloy becomes soft and the stress decreases; The higher the prestrain level, the greater the recovery stress, but when over 8%, it decreases instead; Compared with being incentived with high current intensity, under low current intensity, the amount of austenite became from martensite is much more, the stress is greater; After being incentived many times, there is still recovery stress.Meanwhile,utilizing the nonlinear mapping capability of artificial neural networks, the BP neural network model of SMA recovery stress is proposed according to the experimental results of SMA under different strains.The results show that the numerical results agree well with experimental observations.This model can describe the nonlinear relationship among stress,strain and temperature of SMA and has good forcasting capability.To a certain extent,this model can predict the correct stress of SMA under a given strain,and the results proved the practicality of BP neural network model.更多还原