CN111951908A - A strain-displacement construction method for flexible materials under external load - Google Patents
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Abstract
本发明公开了一种柔性材料在受外载荷作用时的应变‑位移关系构建方法，用于运用柔性材料制造的产品在进行应变‑位移测量时对系统误差进行修正。该方法基于基础的测量数据，选用一种神经网络拟合方法，对测量应变、测量位移进行单一拟合，进而再对拟合出来数据进行交叉拟合，最终得到修正应变‑修正位移关系。本发明用于对不确定性的系统误差进行修正，解决了由于测量工具系统误差特性造成的测量误差对后续产品分析与优化产生影响的问题。
The invention discloses a method for constructing a strain-displacement relationship of a flexible material under the action of an external load, which is used for correcting a system error when a product made of the flexible material is subjected to strain-displacement measurement. Based on the basic measurement data, this method selects a neural network fitting method to perform a single fitting on the measured strain and measured displacement, and then performs cross-fitting on the fitted data, and finally obtains the corrected strain-corrected displacement relationship. The invention is used for correcting the uncertain systematic error, and solves the problem that the measurement error caused by the systematic error characteristic of the measuring tool affects the analysis and optimization of subsequent products.
Description
技术领域technical field
本发明涉及一种应变-位移关系的构建方法，特别涉及一种有关柔性材料在受到外载荷作用时的应变-位移关系构建方法。The invention relates to a method for constructing a strain-displacement relation, in particular to a method for constructing a strain-displacement relation of a flexible material when subjected to an external load.
背景技术Background technique
柔性材料相较于传统的刚性材料，除了有优秀的结构性能，还能在导电性能、导热性能和膨胀性能等方面有良好的表现，渐渐的被更多地应用到了产品的设计中。如基于PI沉底的磁声表面波谐振器、在PMDS衬底上制备非晶硅薄膜、ITO透明导电膜等，柔性材料在其中都扮演着重要的角色。而随着越来越多的柔性材料产品面世，含柔性材料的机电产品结构的优化也是一个值得关注的问题。但柔性材料相对于刚性材料来说，在进行材料结构测试时存在应变-位移关系高度非线性化的问题，且由于当前测量方法中使用的测量器具自身的系统误差,从而导致的应变和位移检测一定存在误差，只有对误差进行修正了之后，产品才能在后期设计优化产品得到更为优良的结果。逆有限元法作为位移场构造辅助工具，只需少量测量数据，便可通过形函数的转换，构造出分析过程中所需的结构整体位移场。目前已有的应变-位移关系构造都是针对于飞机机翼受气动载荷后的刚性结构分析，或是如专利CN201910000649.6等对于一些刚性结构进行了应变-位移场重构，CN110470236A提出的一种嵌入光纤光栅的柔性结构形变重构方法，也是针对机翼等大型结构，由于光栅和结构材料间存在力传递作用，光纤在剪切力作用下易断，在专利CN109766617A提到了基于光纤光栅传感器的构件大变形大位移测量效果较差。而且由于尺度效应的影响，当柔性结构很小时，在结构表面布置的光栅及连接对结构的整体质量分布和其他性能会产生影响，无法对柔性材料进行应变-位移场重构。另外，由于柔性材料上分布有不同的电路、微传感和微执行器件等结构，不同部位的变形不呈现相同规律性，所以不能用常规拟合方式对应变-位移关系进行数学表达，对于目前柔性材料应用中关注的应变-位移关系构建并没有具体的解决方法。Compared with traditional rigid materials, flexible materials not only have excellent structural properties, but also have good performance in terms of electrical conductivity, thermal conductivity and expansion properties, and are gradually being used in product design. For example, PI-based surface magneto-acoustic wave resonators, amorphous silicon thin films prepared on PMDS substrates, ITO transparent conductive films, etc., flexible materials play an important role in them. As more and more flexible material products come out, the optimization of the structure of electromechanical products containing flexible materials is also a problem worthy of attention. However, compared with rigid materials, flexible materials have the problem that the strain-displacement relationship is highly nonlinear during material structure testing, and due to the systematic errors of the measuring instruments used in the current measurement methods, the strain and displacement detection caused by There must be errors. Only after the errors are corrected, the product can get better results in the later design and optimization of the product. As an auxiliary tool for displacement field construction, the inverse finite element method can construct the overall displacement field of the structure required in the analysis process by transforming the shape function with only a small amount of measurement data. The existing strain-displacement relationship structures are all aimed at the analysis of the rigid structure of the aircraft wing after being subjected to aerodynamic loads, or the strain-displacement field reconstruction of some rigid structures such as patent CN201910000649.6. A flexible structural deformation reconstruction method embedded in fiber grating is also aimed at large structures such as wings. Due to the force transmission between the grating and the structural material, the optical fiber is easily broken under the action of shearing force. In the patent CN109766617A, it is mentioned that the sensor based on fiber grating is mentioned. The measurement effect of the large deformation and large displacement of the components is poor. Moreover, due to the influence of the scale effect, when the flexible structure is very small, the gratings and connections arranged on the surface of the structure will affect the overall mass distribution and other properties of the structure, and the strain-displacement field reconstruction of the flexible material cannot be performed. In addition, since there are different structures such as circuits, micro-sensing and micro-actuating devices distributed on the flexible material, the deformation of different parts does not show the same regularity, so the strain-displacement relationship cannot be mathematically expressed by conventional fitting methods. There is no specific solution to the construction of the strain-displacement relationship of interest in flexible material applications.
发明内容：Invention content:
本发明的目的是针对现有的应变-位移构建方法存在误差，提出了一种有关柔性材料的应变-位移关系构建方法。The purpose of the present invention is to propose a method for constructing a strain-displacement relationship of a flexible material in view of the errors existing in the existing strain-displacement construction method.
为了实现上述目的，本方面的技术方案是：一种有关柔性材料的应变-位移关系构建方法，包括如下步骤：In order to achieve the above purpose, the technical solution of this aspect is: a method for constructing a strain-displacement relationship related to a flexible material, comprising the following steps:
步骤1：在柔性材料结构上相关节点安装应变计和位移传感器，获得每个节点的测量应变值和测量位移值。Step 1: Install strain gauges and displacement sensors on the relevant nodes of the flexible material structure, and obtain the measured strain value and measured displacement value of each node.
步骤2：对测量应变值做神经网络拟合，以得到柔性材料结构整体测量应变数据，具体步骤如下：Step 2: Perform neural network fitting on the measured strain values to obtain the overall measured strain data of the flexible material structure. The specific steps are as follows:
步骤21：设置神经网络的激活函数为ReLU。Step 21: Set the activation function of the neural network to ReLU.
步骤22：根据最后需求的结果精度以及硬件运行的条件，设置神经网络的最大和最小神经元个数和神经网络层数。Step 22: Set the maximum and minimum number of neurons and the number of layers of the neural network according to the final required result accuracy and the hardware operation conditions.
步骤23：根据产品的优化要求设置神经网络的停止条件。Step 23: Set the stopping condition of the neural network according to the optimization requirements of the product.
步骤24：神经网络初始化完成后，激活神经网络的向前传播，输入相关节点的测量应变值。Step 24: After the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measured strain values of the relevant nodes are input.
步骤25：对神经网络进行训练，当达到停止条件或神经元个数达到最大值时，固定权值和阈值，输出柔性材料结构整体测量应变数据；反之，则更新权值和阈值并增加神经元个数，直到满足输出条件。Step 25: Train the neural network. When the stopping condition is reached or the number of neurons reaches the maximum value, the weights and thresholds are fixed, and the overall measured strain data of the flexible material structure is output; otherwise, the weights and thresholds are updated and neurons are added. number until the output conditions are met.
步骤3：对测量位移值做神经网络拟合，以得到柔性材料结构整体位移数据，具体步骤如下：Step 3: Perform neural network fitting on the measured displacement values to obtain the overall displacement data of the flexible material structure. The specific steps are as follows:
步骤31：设置神经网络的激活函数为ReLU。Step 31: Set the activation function of the neural network to ReLU.
步骤32：根据最后需求的结果精度以及硬件运行的条件，设置神经网络的最大和最小神经元个数和神经网络层数。Step 32: Set the maximum and minimum number of neurons and the number of layers of the neural network according to the final required result accuracy and the hardware operation conditions.
步骤33：根据产品的优化要求设置神经网络的停止条件。Step 33: Set the stopping condition of the neural network according to the optimization requirements of the product.
步骤34：神经网络初始化完成后，激活神经网络的向前传播，输入相关节点的测量位移值。Step 34: After the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measured displacement values of the relevant nodes are input.
步骤35：对神经网络进行训练，当达到停止条件或神经元个数达到最大值时，固定权值和阈值，输出柔性材料结构整体位移数据；反之，则更新权值和阈值并增加神经元个数，直到满足输出条件。Step 35: Train the neural network. When the stopping condition is reached or the number of neurons reaches the maximum value, the weights and thresholds are fixed, and the overall displacement data of the flexible material structure is output; otherwise, the weights and thresholds are updated and the number of neurons is increased. count until the output conditions are met.
步骤4：运用逆有限元法，将神经网络输出的柔性材料结构整体位移数据，逆解得到柔性材料结构整体逆解应变数据。Step 4: Use the inverse finite element method to inversely solve the overall displacement data of the flexible material structure output by the neural network to obtain the overall inverse solution strain data of the flexible material structure.
步骤5：综合柔性材料结构整体测量应变数据与柔性材料结构整体逆解应变数据，运用神经网络拟合训练得到修正应变，步骤如下：Step 5: Integrate the overall measured strain data of the flexible material structure and the overall inverse solution strain data of the flexible material structure, and use the neural network fitting training to obtain the corrected strain. The steps are as follows:
步骤51：设置神经网络的激活函数为ReLU。Step 51: Set the activation function of the neural network to ReLU.
步骤52：根据最后需求的结果精度以及硬件运行的条件，设置神经网络的最大和最小神经元个数和神经网络层数。Step 52: Set the maximum and minimum number of neurons and the number of layers of the neural network according to the final required result accuracy and the hardware operation conditions.
步骤53：根据产品的优化要求设置神经网络的停止条件。Step 53: Set the stopping condition of the neural network according to the optimization requirements of the product.
步骤54：神经网络初始化完成后，激活神经网络的向前传播，输入相关节点的测量位移值。Step 54: After the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measured displacement values of the relevant nodes are input.
步骤55：对神经网络进行训练，当达到停止条件或神经元个数达到最大值时，固定权值和阈值，输出修正应变-测量应变-逆解应变之间的关系；反之，则更新权值和阈值并增加神经元个数，直到满足输出条件。Step 55: Train the neural network. When the stopping condition is reached or the number of neurons reaches the maximum value, the weights and thresholds are fixed, and the relationship between the corrected strain-measured strain-inverse solution strain is output; otherwise, the weights are updated. and threshold and increase the number of neurons until the output conditions are met.
步骤6：运用神经网络训练完成的修正应变-测量应变-逆解应变关系，求得修正应变。Step 6: Use the corrected strain-measured strain-inverse solution strain relationship completed by the neural network training to obtain the corrected strain.
步骤7：进而得到修正应变-修正位移关系。Step 7: Then obtain the corrected strain-corrected displacement relationship.
附图说明：Description of drawings:
图1为一种柔性材料在受到外载荷作用时的应变-位移关系构建方法流程图。Figure 1 is a flow chart of a method for constructing the strain-displacement relationship of a flexible material when subjected to an external load.
具体实施方式：Detailed ways:
现结合附图和具体实施例对本发明作进一步描述。The present invention will now be further described with reference to the accompanying drawings and specific embodiments.
本发明的目的是针对现有的应变-位移关系构建方法存在误差，提出了一种有关柔性材料的应变-位移关系构建方法。The purpose of the present invention is to propose a method for constructing a strain-displacement relation of a flexible material in view of the errors existing in the existing method for constructing the strain-displacement relation.
为了实现上述目的，本方面的技术方案是：一种柔性材料的应变-位移构建方法，包括如下步骤：In order to achieve the above purpose, the technical solution of the present aspect is: a strain-displacement construction method of a flexible material, comprising the following steps:
步骤1：在柔性材料结构上相关节点安装应变计和位移传感器，获得每个节点的测量应变值和测量位移值。Step 1: Install strain gauges and displacement sensors on the relevant nodes of the flexible material structure, and obtain the measured strain value and measured displacement value of each node.
步骤2：对测量应变值做神经网络拟合，以得到柔性材料结构整体测量应变数据，具体步骤如下：Step 2: Perform neural network fitting on the measured strain values to obtain the overall measured strain data of the flexible material structure. The specific steps are as follows:
步骤21：设置神经网络的激活函数为ReLU。Step 21: Set the activation function of the neural network to ReLU.
步骤22：根据最后需求的结果精度以及硬件运行的条件，设置神经网络的最大和最小神经元个数和神经网络层数。Step 22: Set the maximum and minimum number of neurons and the number of layers of the neural network according to the final required result accuracy and the hardware operation conditions.
步骤23：根据产品的优化要求设置神经网络的停止条件。Step 23: Set the stopping condition of the neural network according to the optimization requirements of the product.
步骤24：神经网络初始化完成后，激活神经网络的向前传播，输入相关节点的测量应变值。Step 24: After the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measured strain values of the relevant nodes are input.
步骤25：对神经网络进行训练，当达到停止条件或神经元个数达到最大值时，固定权值和阈值，输出柔性材料结构整体测量应变数据；反之，则更新权值和阈值并增加神经元个数，直到满足输出条件。Step 25: Train the neural network. When the stopping condition is reached or the number of neurons reaches the maximum value, the weights and thresholds are fixed, and the overall measured strain data of the flexible material structure is output; otherwise, the weights and thresholds are updated and neurons are added. number until the output conditions are met.
步骤3：对测量位移值做神经网络拟合，以得到柔性材料结构整体位移数据，具体步骤如下：Step 3: Perform neural network fitting on the measured displacement values to obtain the overall displacement data of the flexible material structure. The specific steps are as follows:
步骤31：设置神经网络的激活函数为ReLU。Step 31: Set the activation function of the neural network to ReLU.
步骤32：根据最后需求的结果精度以及硬件运行的条件，设置神经网络的最大和最小神经元个数和神经网络层数。Step 32: Set the maximum and minimum number of neurons and the number of layers of the neural network according to the final required result accuracy and the hardware operation conditions.
步骤33：根据产品的优化要求设置神经网络的停止条件。Step 33: Set the stopping condition of the neural network according to the optimization requirements of the product.
步骤34：神经网络初始化完成后，激活神经网络的向前传播，输入相关节点的测量位移值。Step 34: After the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measured displacement values of the relevant nodes are input.
步骤35：对神经网络进行训练，当达到停止条件或神经元个数达到最大值时，固定权值和阈值，输出柔性材料结构整体位移数据；反之，则更新权值和阈值并增加神经元个数，直到满足输出条件。Step 35: Train the neural network. When the stopping condition is reached or the number of neurons reaches the maximum value, the weights and thresholds are fixed, and the overall displacement data of the flexible material structure is output; otherwise, the weights and thresholds are updated and the number of neurons is increased. count until the output conditions are met.
步骤4：运用逆有限元法，将神经网络输出的柔性材料结构整体位移数据，逆解得到柔性材料结构整体逆解应变数据。Step 4: Use the inverse finite element method to inversely solve the overall displacement data of the flexible material structure output by the neural network to obtain the overall inverse solution strain data of the flexible material structure.
步骤5：综合柔性材料结构整体测量应变数据与柔性材料结构整体逆解应变数据，运用神经网络拟合训练得到修正应变，步骤如下：Step 5: Integrate the overall measured strain data of the flexible material structure and the overall inverse solution strain data of the flexible material structure, and use the neural network fitting training to obtain the corrected strain. The steps are as follows:
步骤51：设置神经网络的激活函数为ReLU。Step 51: Set the activation function of the neural network to ReLU.
步骤52：根据最后需求的结果精度以及硬件运行的条件，设置神经网络的最大和最小神经元个数和神经网络层数。Step 52: Set the maximum and minimum number of neurons and the number of layers of the neural network according to the final required result accuracy and the hardware operation conditions.
步骤53：根据产品的优化要求设置神经网络的停止条件。Step 53: Set the stopping condition of the neural network according to the optimization requirements of the product.
步骤54：神经网络初始化完成后，激活神经网络的向前传播，输入相关节点的测量位移值。Step 54: After the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measured displacement values of the relevant nodes are input.
步骤55：对神经网络进行训练，当达到停止条件或神经元个数达到最大值时，固定权值和阈值，输出修正应变-测量应变-逆解应变之间的关系；反之，则更新权值和阈值并增加神经元个数，直到满足输出条件。Step 55: Train the neural network. When the stopping condition is reached or the number of neurons reaches the maximum value, the weights and thresholds are fixed, and the relationship between the corrected strain-measured strain-inverse solution strain is output; otherwise, the weights are updated. and threshold and increase the number of neurons until the output conditions are met.
步骤6：运用神经网络训练完成的修正应变-测量应变-逆解应变关系，求得修正应变。Step 6: Use the corrected strain-measured strain-inverse solution strain relationship completed by the neural network training to obtain the corrected strain.
步骤7：进而得到修正应变-修正位移关系。Step 7: Then obtain the corrected strain-corrected displacement relationship.
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