基于BP神经网络的形状记忆合金回复力预测研究
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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。
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.
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。
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.
引文
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[22]孙朝阳.非热弹性马氏体相变的细观本构模型[J].材料科学与工艺,2009,17(4):445-449.
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[31]洪雷,王林伟.形状记忆合金对混凝土梁驱动效应的有限元分析[J].山东建筑工程学院学报,2004,19(3):31-33.
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[34]李岩.机敏复合材料中TiNi形状记忆合金约束态相变研究[D].大连:大连理工大学,2001.
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[36]Liang C,Rogers C A.Design of shape memory alloys prings with applications in vibration control[J].J Vibration Acoustics,1993,115(1):135.
[37]Baz A,Iman K,Mccoy J.Active vibration control of flexible beams using shape memory actuators[J].J Sound Vibration,1990,140(3):437.
[38]Bidaux J E,Manson J A,Cotthardt R.Active stiffening of composite materials by embedded shape memory alloy fibers[J].Mater Res Soc Symp,1997,52:107.
[39]Dolce M.Cardone D.Mechanical behavior of shape memory alloys for seismic applications of austenite TiNi wires subjected to tension[J] .Inter J Mech Sci.2001,43:657.
[40]贺志荣.TiNi形状记忆合金的工程应用研究现状和展望[J].材料导报,2005,19(4):50-53.
[41]杨丰.TiNi形状记忆合金驱动特性研究[D].北京:中国石油大学,2002.
[42]胡守仁,等.神经网络导论[M].北京:国防科技大学出版社,1993.
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[44]袁曾任.人工神经网络及其应用[M].北京:清华大学出版社,2000.
[45]李允公.基于神经网络和主元分析的特征集生成方法[J].机械工程学报,2009,19(4):62-66.
[46]易荣庆,李文辉.基于自组织神经网络的特征识别[J].吉林大学学报.2009,39(1):148-153.
[47]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社,2002 .
[48]张文修,梁怡.遗传算法数学基础[M].西安:西安交通大学出版社,2000.
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[58]王正品,张路,要玉宏.金属功能材料[M].北京:化学工业出版社,2004.
[2]陶宝祺,熊克.智能材料结构的定义及应用前景[J].中国科学基金会,1995(2):40-46.
[3]薛标.铁磁形状记忆合金Ni-Mn-Ga微结构数值模拟[D].兰州:兰州大学,2008.
[4]付小华.嵌入形状记忆合金丝复合材料梁热屈曲分析[D].兰州:兰州理工大学,2003.
[5]王林伟.形状记忆合金对钢筋混凝土梁驱动效应的ANSYS分析[D].大连:大连理工大学,2004.
[6]余志刚.SMA环向预应力混凝土结构原理研究[D].重庆:重庆交通大学,2007.
[7]杨杰,吴月华.形状记忆合金及其应用[M].合肥:中国科学技术大学出版社,1993.
[8]卞铁荣,卢正欣,等.不同Cu含量的TiNiCu形状记忆合金薄膜的组织与相变[J].表面技术,2008,37(1):42-44.
[9]余华军,封向东,等.TiNi和TiNiCu记忆合金薄膜的温度记忆效应研究[J].电子科技大学学报,2008,37(1):128-130.
[10]Yunqing Ma , Shuiyuan Yang . Tensile characteristics and shape memory effect of Ni56Mn21Co4Ga19 high temperature shape memory alloy [J].Scripta Mater,2008,58:918-921.
[11]Li Y,Xin Y.Shape memory effect of grain refined Ni54Mn25Ga21 alloy with high transformation temperature[J].Scripta Materialia,2004,51:849-852.
[12]Y Li,L S Cui.Constrained phase transformation of a NiTi shape memory alloy[J].Metall,2003,34A:219.
[13]周星,朱明.人工食管用TiNi形状记忆合金的耐蚀性研究[J].上海金属,2009,31(1):10-12.
[14]龙继伟.谈混凝土构件裂纹的控制措施[J].科技资讯,2009,25:45.
[15]陈海全.应用形状记忆合金的大跨桥梁结构振动控制理论研究与振动台试验[D].天津:天津大学,2001.
[16]邓宗才.形状记忆合金对混凝土梁驱动效应分析[J].土木工程报,2002,35(2):41-47.
[17]邓宗才.形状记忆合金对纤维增强聚合物梁驱动效应的分析[J].公路交通科技,2005,22(10):71-74.
[18]王治国.形状记忆合金双向记忆效应及驱动特性研究[D].四川:四川大学,2001.
[19]姚康德,成国祥.智能材料[M].北京:化学工业出版社,2002.
[20]邓文英.金属工艺学[M].北京:高等教育出版社,2004.
[21]王正品,张路,要玉宏.金属功能材料[M].北京:化学工业出版社,2004.
[22]孙朝阳.非热弹性马氏体相变的细观本构模型[J].材料科学与工艺,2009,17(4):445-449.
[23]戴鹏,吴明在.热弹性马氏体相变中的低温内耗峰[J].安徽大学学报,2008,32(6):56-59.
[24]宫秀敏.相变理论基础及应用[D].武汉:武汉理工大学出版社,2004.
[25]石德珂.材料科学基础[D].西安:西安交通大学出版社,1995.
[26]Q Z Li,Z J Jin.Experimental study on influences of organic alkali on material removal rate of Cu CMP[J].ICSFT,2006:317-322.
[27]郭世海,张羊换,王新林.磁性形状记忆合金的研究及发展[J].稀有金属,2005,29(3):431-439.
[28]黄克智,黄永刚.固体本构关系[M].北京:清华大学出版社,1999.
[29]吴承建,陈国良,强文江.金属材料学[M].北京:冶金工业出版社,2005.
[30]邓兰兰.加工方式对NiTi形状记忆合金回复力影响的研究[D].重庆:重庆交通大学,2008.
[31]洪雷,王林伟.形状记忆合金对混凝土梁驱动效应的有限元分析[J].山东建筑工程学院学报,2004,19(3):31-33.
[32]张亚欧.基于形状记忆合金驱动的自适应曲壳结构研究[D].南京:南京航空航天大学,2003.
[33]李岩,崔立山,郑雁军,等.TiNi形状记忆合金在定应变约束下的马氏体相变[J].材料研究学报,2001,15(6):649.
[34]李岩.机敏复合材料中TiNi形状记忆合金约束态相变研究[D].大连:大连理工大学,2001.
[35]Moorleghem W V.Otte D.The use of shape memory alloys for fire protection[J].Engineering Aspect of Shape Memory Alloys,1990,110:295.
[36]Liang C,Rogers C A.Design of shape memory alloys prings with applications in vibration control[J].J Vibration Acoustics,1993,115(1):135.
[37]Baz A,Iman K,Mccoy J.Active vibration control of flexible beams using shape memory actuators[J].J Sound Vibration,1990,140(3):437.
[38]Bidaux J E,Manson J A,Cotthardt R.Active stiffening of composite materials by embedded shape memory alloy fibers[J].Mater Res Soc Symp,1997,52:107.
[39]Dolce M.Cardone D.Mechanical behavior of shape memory alloys for seismic applications of austenite TiNi wires subjected to tension[J] .Inter J Mech Sci.2001,43:657.
[40]贺志荣.TiNi形状记忆合金的工程应用研究现状和展望[J].材料导报,2005,19(4):50-53.
[41]杨丰.TiNi形状记忆合金驱动特性研究[D].北京:中国石油大学,2002.
[42]胡守仁,等.神经网络导论[M].北京:国防科技大学出版社,1993.
[43]李学桥,马莉.神经网络工程应用[M].重庆:重庆大学出版社,1996.
[44]袁曾任.人工神经网络及其应用[M].北京:清华大学出版社,2000.
[45]李允公.基于神经网络和主元分析的特征集生成方法[J].机械工程学报,2009,19(4):62-66.
[46]易荣庆,李文辉.基于自组织神经网络的特征识别[J].吉林大学学报.2009,39(1):148-153.
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