Prediction method, prediction device, prediction system, and prediction program

Abstract

To provide a prediction method, a prediction device, a prediction system, and a prediction program capable of reducing man-hours and ensuring satisfactory prediction precision.SOLUTION: In a prediction method for predicting characteristics of a vibration-proof device, the prediction method includes a parameter identification step of identifying a superelastic spring constant K0, a viscoelastic spring constant Kc, an attenuation coefficient C, a nonlinear coefficient (m), a Paine influence width (w), and a hysteresis load width (h) of the vibration-proof device each on the basis of a measurement result of measurement executed in advance to the vibration-proof device under a predetermined measurement condition, an element characteristic calculation step of calculating a superelastic characteristic FA, a viscoelastic characteristic FB, and an elastoplastic characteristic FC each under a predetermined excitation condition for prediction different from a predetermined measurement condition, and a first characteristic prediction step of predicting a first characteristic F of the vibration-proof device under a predetermined excitation condition for measurement by adding the superelastic characteristic FA, the viscoelastic characteristic FB, and the elastoplastic characteristic FC together.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a prediction method, a prediction device, a prediction system, and a prediction program used to predict the characteristics of a vibration isolation device. [Background technology]

[0002] BACKGROUND ART It is known that the characteristics of a vibration-damping device including a rubber elastic body can be predicted by creating a physical model of the device (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-52304 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the physical model used in Patent Document 1 has a large number of parameters, which may increase prediction accuracy, but may also complicate calculations and increase the number of steps.

[0005] Therefore, an object of the present invention is to provide a prediction method, a prediction device, a prediction system, and a prediction program that can ensure good prediction accuracy while reducing the number of steps. [Means for solving the problem]

[0006] [1] A prediction method for predicting the characteristics of a vibration isolation device, comprising: The vibration isolation device is a first attachment portion configured to be attached to the vibration generating portion side; A second mounting portion configured to be mounted on the vibration receiving portion side; a rubber elastic body that connects the first mounting portion and the second mounting portion to each other; Equipped with The prediction method includes: a parameter identification step of identifying a superelastic spring constant K0, a viscoelastic spring constant Kc, a damping coefficient C, a nonlinear coefficient m, a pain influence width w, and a hysteresis load width h of the vibration isolation device based on measurement results obtained by previously measuring the vibration isolation device under predetermined measurement conditions; an element property calculation step of calculating a hyperelastic property FA, ​​a viscoelastic property FB, and an elastic-plastic property FC of the vibration-damping device under predetermined vibration excitation conditions for prediction that are different from the predetermined measurement conditions; a first characteristic prediction step of predicting a first characteristic F of the vibration-damping device under the predetermined prediction excitation conditions by adding up the hyperelastic characteristic FA, the viscoelastic characteristic FB, and the elastic-plastic characteristic FC; Including, In the element characteristic calculation step, The superelastic characteristic F is calculated based on the superelastic spring constant K0, The viscoelastic characteristic F B is calculated based on the viscoelastic spring constant K c and the damping coefficient C, A prediction method, wherein the elastic-plastic characteristic FC is calculated based on the nonlinear coefficient m, the payload width w, and the hysteresis load width h. This makes it possible to reduce the number of steps while ensuring good prediction accuracy.

[0007] [2] The prediction method described in [1] further includes a second characteristic prediction step of predicting a second characteristic of the vibration isolation device under the specified prediction vibration conditions, which is different from the first characteristic F, based on the first characteristic F. This allows a wider variety of properties to be predicted, improving practicality.

[0008] [3] The prediction method according to [2], wherein the second characteristic is at least one selected from the dynamic spring constant Kd and the loss coefficient tanδ of the vibration isolation device. This makes it possible to provide prediction results for conventionally used types of characteristics, thereby improving practicality.

[0009] [4] The prediction method according to any one of [1] to [3], wherein the predetermined vibration condition for prediction is composed of a predetermined frequency for prediction and a predetermined amplitude for prediction. This can improve practicality.

[0010] [5] The prediction method according to any one of [1] to [4], wherein the measurement results are measurement results of the load deflection characteristics of the vibration isolation device. This allows each model parameter to be easily identified.

[0011] [6] The prediction method according to any one of [1] to [4], wherein the measurement results are measurement results of the dynamic vibration characteristics of the vibration isolation device. This makes it possible to easily identify each model parameter without the need to use any additional equipment other than a general dynamic spring testing machine.

[0012] [7] A prediction device configured to predict characteristics of an anti-vibration device, The vibration isolation device is a first attachment portion configured to be attached to the vibration generating portion side; A second mounting portion configured to be mounted on the vibration receiving portion side; a rubber elastic body that connects the first mounting portion and the second mounting portion to each other; Equipped with the prediction device includes a processing unit; The processing unit a parameter identification process for identifying the superelastic spring constant K0, viscoelastic spring constant Kc, damping coefficient C, nonlinear coefficient m, payne influence width w, and hysteresis load width h of the vibration isolation device based on measurement results obtained by performing measurements on the vibration isolation device under predetermined measurement conditions in advance; an element characteristic calculation process for calculating a hyperelastic characteristic FA, a viscoelastic characteristic FB, and an elastic-plastic characteristic FC of the vibration-damping device under predetermined vibration excitation conditions for prediction that are different from the predetermined measurement conditions; a first characteristic prediction process for predicting a first characteristic F of the vibration-damping device under the predetermined prediction excitation conditions by adding up the hyperelastic characteristic FA, the viscoelastic characteristic FB, and the elastic-plastic characteristic FC; and In the element characteristic calculation process, The superelastic characteristic F is calculated based on the superelastic spring constant K0, The viscoelastic characteristic F B is calculated based on the viscoelastic spring constant K c and the damping coefficient C, A prediction device, wherein the elastic-plastic characteristic FC is calculated based on the nonlinear coefficient m, the payload width w, and the hysteresis load width h. This makes it possible to reduce the number of steps while ensuring good prediction accuracy.

[0013] [8] The prediction device according to [7]; a measurement device configured to perform the measurements; and A prediction system with This makes it possible to reduce the number of steps while ensuring good prediction accuracy.

[0014] [9] A prediction program configured to be executed by the processing unit in the prediction device according to [7], the parameter identification process; The element characteristic calculation process; the first characteristic prediction process; A prediction program configured to cause the processing unit to execute the above. This makes it possible to reduce the number of steps while ensuring good prediction accuracy. [Effects of the Invention]

[0015] According to the present invention, it is possible to provide a prediction method, a prediction device, a prediction system, and a prediction program that can ensure good prediction accuracy while reducing the number of steps. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a schematic diagram illustrating a prediction system according to an embodiment of the present invention, comprising a prediction device according to an embodiment of the present invention; [Figure 2] FIG. 1 is a schematic diagram illustrating an example of a physical model of an anti-vibration device that can be used as a basis for calculations performed in the prediction method, prediction device, prediction system, and prediction program according to each embodiment of the present invention. [Figure 3] 1 is a flowchart illustrating a prediction method according to an embodiment of the present invention. [Figure 4] 4(a) is an explanatory diagram for explaining the relationship between the Lissajous figure of the vibration-damping device characteristics (FIG. 4(a)) and the Lissajous figures of the superelastic characteristics, viscoelastic characteristics, and elasto-plastic characteristics (FIG. 4(b) to FIG. 4(d)). [Figure 5] FIG. 10 is an explanatory diagram for explaining the process of deriving the formula for calculating the elastic-plastic characteristic FC. [Figure 6] FIG. 10 is an explanatory diagram for explaining the process of deriving the formula for calculating the elastic-plastic characteristic FC. [Figure 7] 10 is a diagram schematically showing an example of a measurement result (load deflection characteristic) that can be obtained in the measurement step. [Figure 8] 10 is an explanatory diagram for explaining a specific example of a method for identifying a model parameter (hyperelastic spring constant K0) of the hyperelastic characteristic FA from the load deflection characteristic. FIG. [Figure 9] 10 is an explanatory diagram for explaining a specific example of a method for identifying model parameters (viscoelastic spring constant Kc and damping coefficient C) of the viscoelastic characteristic FB from the load deflection characteristic. FIG. [Figure 10] 10 is an explanatory diagram for explaining a specific example of a method for identifying model parameters (viscoelastic spring constant Kc and damping coefficient C) of the viscoelastic characteristic FB from the load deflection characteristic. FIG. [Figure 11] 1 is an explanatory diagram for explaining a specific example of a method for identifying model parameters (nonlinear coefficient m, payload width w, and hysteresis load width h) of an elastic-plastic characteristic FC from a load-deflection characteristic. [Figure 12]1 is an explanatory diagram for explaining a specific example of a method for identifying model parameters (nonlinear coefficient m, payload width w, and hysteresis load width h) of an elastic-plastic characteristic FC from a load-deflection characteristic. [Figure 13] 10 is a diagram schematically showing an example of a measurement result (dynamic vibration characteristic) that can be obtained in the measurement step. [Figure 14] 10 is an explanatory diagram for explaining a specific example of a method for identifying a model parameter (hyperelastic spring constant K0) of the hyperelastic characteristic FA from the dynamic vibration characteristic. FIG. [Figure 15] 10 is an explanatory diagram for explaining a specific example of a method for identifying model parameters (nonlinear coefficient m, pain influence width w, and hysteresis load width h) of the elastic-plastic characteristic FC from the dynamic vibration characteristic. FIG. [Figure 16] 10 is an explanatory diagram for explaining a specific example of a method for identifying model parameters (viscoelastic spring constant Kc and damping coefficient C) of the viscoelastic characteristic FB from the dynamic vibration characteristic. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0017] The prediction method, prediction device, prediction system, and prediction program of the present invention can be used to predict the characteristics of any type of vibration isolation device, and can be suitably used, for example, to predict the characteristics of vibration isolation devices (such as bushes) for vehicles.

[0018] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, with reference to the drawings, embodiments of a prediction method, a prediction device, a prediction system, and a prediction program according to the present invention will be described.

[0019] [Vibration isolation device VD] The prediction method, prediction device 3, prediction system 1, and prediction program 32P in each example described in this specification can be used to predict the characteristics of any type of vibration isolation device VD (FIG. 1). Examples of the vibration isolation device VD include vibration isolation devices for vehicles (such as bushes), but may also be devices used for other purposes. As shown in FIG. 1, the vibration isolation device VD includes a first mounting portion VDa, a second mounting portion VDb, and a rubber elastic body VDc. The first mounting portion VDa is configured to be attached to the vibration generating portion by fastening or the like. The second mounting portion VDb is configured to be attached to the vibration receiving portion by fastening or the like. The first mounting portion VDa and the second mounting portion VDb are preferably configured from rigid bodies. The rubber elastic body VDc is made of rubber and connects the first mounting portion VDa and the second mounting portion VDb to each other. The vibration generating portion is configured to input vibrations to the vibration isolation device VD, and the vibration receiving portion is configured to receive vibrations output from the vibration isolation device VD. For example, if the vibration isolation device VD is for a vehicle, the vibration generating portion may be the engine of the vehicle, and the vibration receiving portion may be the body of the vehicle. In the example of Figure 1, the vibration isolation device VD is configured as a bushing for a vehicle, and the first mounting portion VDa and the second mounting portion VDb are each cylindrical, but the shape and other configuration of the vibration isolation device VD may be different from the example of Figure 1.

[0020] [Prediction System 1] Here, with reference to FIG. 1, a basic configuration of a prediction system 1 according to one embodiment of the present invention will be described. As illustrated in FIG. 1, a prediction system 1 according to one embodiment of the present invention includes a measurement device 2 and a prediction device 3 according to one embodiment of the present invention.

[0021] (Measuring device 2) The measurement device 2 is configured to measure the vibration isolation device VD and output the measurement results. More specifically, the measurement device 2 is configured to measure one or more characteristics of the vibration isolation device VD under one or more predetermined measurement conditions that are arbitrarily set, and to output each of the measured characteristics as a measurement result. For example, the measurement device 2 may be configured to measure the load-deflection (static spring) characteristics of the vibration isolation device VD under predetermined measurement conditions and output the measured load-deflection characteristics as a measurement result. In this case, the measurement device 2 may have the functions of a known load-deflection testing machine. And / or, the measuring device 2 may be configured to measure the dynamic vibration (dynamic spring) characteristics of the vibration isolation device VD under predetermined measurement conditions and output the measured dynamic vibration characteristics as a measurement result. In this case, the measuring device 2 may have the functions of a known dynamic spring testing machine.

[0022] The measurement results output from the measurement device 2 are input to the prediction device 3. For example, the measurement device 2 may transmit the measurement results to the communication unit 33 of the prediction device 3 via communication (wired communication and / or wireless communication) during or after the measurement, thereby inputting the measurement results to the prediction device 3. Alternatively, the measurement device 2 may store the measurement results in an external storage device (USB, SD card, etc.) connected to the measurement device 2 during or after the measurement, and then the external storage device may be connected to the prediction device 3 by a person or the like, thereby inputting the measurement results to the prediction device 3.

[0023] (Prediction Device 3) As illustrated in FIG. 1, the prediction device 3 includes a processing unit 31 and a storage unit 32. The prediction device 3 may further include at least one of a communication unit 33, an input unit 34, and a display unit 35. The prediction device 3 may be configured as any computer, such as a personal computer, a tablet terminal, or a dedicated device. The prediction device 3 may be configured as a single computer or multiple computers. Furthermore, the measurement device 2 and the prediction device 3 may be configured as an integrated unit.

[0024] The processing unit 31 is configured to be composed of, for example, one or more processing devices (such as CPUs), and is configured to execute various programs such as the prediction program 32P stored in the storage unit 32, thereby executing various processes S20 to S50 (FIG. 3) described below while controlling the entire prediction device 3. However, the processing unit 31 does not necessarily have to be configured to execute the process of S50.

[0025] The storage unit 32 is configured to store various information such as various programs such as a prediction program 32P to be executed by the processing unit 31, measurement results by the measuring device 2, and processing results by the processing unit 31 (calculation results, prediction results, etc.). The prediction program 32P is configured to cause the processing unit 31 to execute various processes S20 to S50 (FIG. 3) described below. However, the prediction program 32P does not necessarily have to be configured to cause the processing unit 31 to execute the process of S50.

[0026] The communication unit 33 is configured to communicate (wired communication and / or wireless communication) with the measurement device 2. When the communication unit 33 receives the measurement results from the measurement device 2, the processing unit 31 stores the measurement results in the storage unit 32. The prediction device 3 does not necessarily have to include the communication unit 33.

[0027] The input unit 34 is composed of, for example, a keyboard, a mouse, and / or push buttons, and receives input from a person. The display unit 35 is configured, for example, by a liquid crystal panel or the like, and is configured to be able to display various information such as the processing results (calculation results, etc.) by the processing unit 31. The input unit 34 and the display unit 35 may form a touch panel. Furthermore, the prediction device 3 does not necessarily have to include the input unit 34 and / or the display unit 35.

[0028] [Physical model] Next, referring to FIG. 2, an example of a physical model of the vibration isolation device VD that can be used as the basis for calculations performed in each of the prediction method, prediction device 3, prediction system 1, and prediction program 32P according to each embodiment of the present invention will be described. The physical model is composed of three element characteristic models connected in parallel: a model of hyperelastic characteristics FA, a model of viscoelastic characteristics FB, and a model of elastic-plastic characteristics FC. Therefore, the vibration isolation device characteristic F expressed by the physical model is the sum of the hyperelastic characteristics FA, the viscoelastic characteristics FB, and the elastic-plastic characteristics FC. The model of the hyperelastic characteristic FA has a spring term of the hyperelastic spring constant K0 (in the example of Fig. 2, only this spring term is included), and has the hyperelastic spring constant K0 as a parameter. Note that the hyperelastic characteristic FA may be identified as a higher-order equation for displacement to represent nonlinearity at large amplitudes due to shape elements, etc. The model of the viscoelastic characteristics FB is a Maxwell model, in which a spring term with a viscoelastic spring constant Kc and a damping term with a damping coefficient C are arranged in series, and the parameters are the viscoelastic spring constant Kc and the damping coefficient C. The damping term with the damping coefficient C has velocity dependency. The model of the elastic-plastic characteristic FC is composed of a spring term and a friction term arranged in series, and has parameters including a nonlinear coefficient m, a pain influence width w, and a hysteresis load width h. Therefore, the physical model has a total of six parameters: a hyperelastic spring constant K0, a viscoelastic spring constant Kc, a damping coefficient C, a nonlinear coefficient m, a pain influence width w, and a hysteresis load width h. For convenience, in this specification, the six parameters of the physical model are also referred to as "model parameters."

[0029] [Prediction method] Next, a prediction method according to one embodiment of the present invention will be described.

[0030] (Summary) First, an outline of a prediction method according to one embodiment of the present invention will be described with reference to Fig. 3. The prediction method of this embodiment includes a measurement step S10, a parameter identification step (parameter identification process) S20, an element characteristic calculation step (element characteristic calculation process) S30, a first characteristic prediction step (first characteristic prediction process) S40, and a second characteristic prediction step (second characteristic prediction process) S50. However, the second characteristic prediction step (second characteristic prediction process) S50 does not necessarily have to be performed.

[0031] Each of S20 to S50 may be performed by the processing unit 31 of the prediction device 3 of the prediction system 1 (Figure 1) described above in accordance with the prediction program 32P, or may be performed manually without using the prediction device 3 (and therefore the prediction system 1).

[0032] -Measurement step S10- First, in advance, in a measurement step S10, the measurement device 2 performs measurements on the vibration isolation device VD under one or more predetermined measurement conditions, and obtains the measurement results of the measurements. The predetermined measurement conditions are input into the measurement device 2 by a person or the like before measurement. The content of the measurement performed on the vibration isolation device VD may differ depending on the method used in the subsequent parameter identification step S20. The types of parameters (speed, frequency, amplitude, etc.) of the predetermined measurement conditions may differ depending on the content of the measurement performed on the vibration isolation device VD, but the values ​​of the parameters of the predetermined measurement conditions may be set arbitrarily.

[0033] - Parameter identification step (parameter identification processing) S20 - After the measurement step S10, in a parameter identification step (parameter identification process) S20, six model parameters of the vibration isolation device VD are identified based on the measurement results of the measurement step S10. As described above, the six model parameters are the hyperelastic spring constant K0, the viscoelastic spring constant Kc, the damping coefficient C, the nonlinear coefficient m, the pain influence width w, and the hysteresis load width h (FIG. 2). Specific techniques that can be used in the parameter identification step (parameter identification process) S20 include, for example, a technique (S20A) for identifying the six model parameters from the load-deflection (static spring) characteristics of the vibration isolation device VD, and a technique (S20B) for identifying the six model parameters from the dynamic vibration (dynamic spring) characteristics of the vibration isolation device VD, and either of these may be used. When the former technique (S20A) is used, the measurement step S10 obtains measurement results of the load-deflection characteristics of the vibration isolation device VD, and the types of parameters of the predetermined measurement conditions include velocity. When the latter technique (S20B) is used, the measurement step S10 obtains measurement results of the dynamic vibration characteristics of the vibration isolation device VD, and the types of parameters of the predetermined measurement conditions include frequency and amplitude. Either of the above methods (S20A and S20B) allows easy identification of each model parameter. The latter method (S20B) also has the advantage that it can be implemented without the need for additional equipment other than a general dynamic spring testing machine.

[0034] - Element property calculation step (element property calculation process) S30 - After the parameter identification step (parameter identification process) S20, in the element property calculation step (element property calculation process) S30, three element properties of the vibration isolation device VD under predetermined prediction excitation conditions are calculated based on the six model parameters identified in the parameter identification step (parameter identification process) S20. As described above, the three element properties are the hyperelastic property FA, ​​the viscoelastic property FB, and the elasto-plastic property FC (FIG. 2). The predetermined vibration conditions for prediction are vibration conditions set as vibration conditions under which a user or the like desires to obtain a prediction result. As the predetermined vibration conditions for prediction, any vibration conditions (types and values) different from the predetermined measurement conditions (types and values) used in the measurement in the measurement step S10 can be set. For example, the predetermined vibration condition for prediction may be composed of a frequency (predetermined frequency for prediction) and an amplitude (predetermined amplitude for prediction). In this case, the prediction result can be provided using the frequency and amplitude, which are generally in high demand as parameter types of the vibration condition for prediction, thereby improving practicality. When the prediction method is carried out using the prediction device 3, the predetermined vibration conditions for prediction are input to the prediction device 3, for example, by a person operating the input unit 34. Thereafter, an element characteristic calculation step (element characteristic calculation process) S30 is carried out using the input predetermined vibration conditions for prediction. More specifically, in the element property calculation step (element property calculation process) S30, the hyperelastic property FA is calculated based on the hyperelastic spring constant K0 identified in the parameter identification step (parameter identification process) S20, the viscoelastic property FB is calculated based on the viscoelastic spring constant Kc and damping coefficient C identified in the parameter identification step (parameter identification process) S20, and the elastic-plastic property FC is calculated based on the nonlinear coefficient m, Payne influence width w, and hysteresis load width h identified in the parameter identification step (parameter identification process) S20.

[0035] -First characteristic prediction step (first characteristic prediction process) S40- After the element characteristic calculation step (element characteristic calculation process) S30, in the first characteristic prediction step (first characteristic prediction process) S40, the element characteristics (hyperelastic characteristic FA, viscoelastic characteristic FB, and elastic-plastic characteristic FC) calculated in the element characteristic calculation step (element characteristic calculation process) S30 are added together to predict (calculate) the first characteristic F (vibration isolation device characteristic F) of the vibration isolation device VD under specified prediction vibration conditions. That is, the first characteristic F (vibration isolator characteristic F) is calculated by the following formula (1). F = FA + FB + FC (1)

[0036] The specific types of each element characteristic FA, FB, FC and the first characteristic F (vibration isolation device characteristic F) are arbitrary, and examples include load deflection (static spring) characteristics (such as a Lissajous figure represented by a graph of horizontal axis: displacement (deflection) - vertical axis: load), dynamic vibration (dynamic spring) characteristics (such as a stress sine curve represented by a graph of horizontal axis: time - vertical axis: load), etc.

[0037] - Second characteristic prediction step (second characteristic prediction process) S50 - After the first characteristic prediction step (first characteristic prediction processing) S40, in the second characteristic prediction step (second characteristic prediction processing) S50, based on the first characteristic F obtained in the first characteristic prediction step (first characteristic prediction processing) S40, a second characteristic of the vibration isolation device VD under specified prediction vibration conditions, which is different from the first characteristic F, is predicted (calculated). By performing the second characteristic prediction step (second characteristic prediction process) S50, it is possible to predict more types of characteristics, thereby improving practicality. However, S50 may be omitted.

[0038] The second characteristic may be, for example, at least one selected from the dynamic spring constant Kd and the loss factor tanδ of the vibration isolation device VD. This makes it possible to provide prediction results for conventionally used types of characteristics, thereby improving practicality. For example, by performing calculations in accordance with JIS K 6394 based on the first characteristic F (vibration isolation device characteristic F), the values ​​of the second characteristic (dynamic spring constant Kd, loss coefficient tanδ, etc.) under specified prediction vibration conditions can be obtained.

[0039] When the prediction method is performed using the prediction device 3, the processing unit 31 may cause the display unit 35 to display at least one processing result of S20 to S50 at any predetermined timing and / or in response to an operation of the input unit 34 by a user or the like. For example, the processing unit 31 may cause the display unit 35 to display the first characteristic F (anti-vibration device characteristic F) predicted in the first characteristic prediction step (first characteristic prediction process) S40. The display unit 35 may also cause the display unit 35 to display the second characteristic predicted in the second characteristic prediction step (second characteristic prediction process) S50. This makes it easier to present the processing result (prediction result, etc.) to the user or the like, thereby improving convenience.

[0040] In the prediction method, prediction device 3, prediction system 1, and prediction program 32P according to each embodiment of the present invention, as described above, six model parameters of the vibration isolation device VD, namely, the hyperelastic spring constant K0, the viscoelastic spring constant Kc, the damping coefficient C, the nonlinear coefficient m, the Payne influence width w, and the hysteresis load width h, are identified (S20) based on the measurement results of a measurement (S10) previously performed under predetermined measurement conditions, and the first characteristic F (vibration isolation device characteristic F) is predicted based on each identified model parameter (S30-S40). Therefore, once the model parameters are identified through measurement, it is possible to obtain a prediction result of the first characteristic F (vibration isolation device characteristic F) under any excitation conditions (predetermined excitation conditions for prediction) different from the conditions used in the measurement (predetermined measurement conditions) without taking the trouble of measuring. This improves convenience. Furthermore, according to each embodiment of the present invention, the number of model parameters that need to be identified is kept to a necessary minimum from the viewpoint of the accuracy required at a practical level. This simplifies the calculations for identifying the model parameters, etc., and reduces the number of steps, while ensuring a prediction accuracy that is sufficiently good at a practical level. Furthermore, according to each embodiment of the present invention, the first characteristic F (vibration isolator characteristic F) is predicted using the six model parameters, so that the static spring characteristic and dynamic spring characteristic can be predicted together.

[0041] (Specific example of element property calculation step (element property calculation process) S30) Next, an example of specific calculation contents in the element characteristic calculation step (element characteristic calculation process) S30 will be described. As described above, in the element property calculation step (element property calculation process) S30, the three element properties (hyperelastic property FA, ​​viscoelastic property FB, and elastic-plastic property FC (Figure 2)) of the vibration isolation device VD under specified prediction vibration conditions are calculated based on the six model parameters identified in the parameter identification step (parameter identification process) S20.

[0042] As shown schematically in Figure 4, when the vibration-isolating device characteristic F of the vibration-isolating device VD is plotted as a Lissajous figure (Figure 4(a)), the hyperelastic characteristic FA, viscoelastic characteristic FB, and elasto-plastic characteristic FC, which are obtained by dividing the vibration-isolating device characteristic F into three elements, can be said to each become a Lissajous figure as shown in Figures 4(b) to 4(d). In the element characteristic calculation step (element characteristic calculation process) S30, calculation formulas based on such principles can be used to calculate each of the element characteristics FA, FB, and FC. Below, examples of calculation formulas that can be used in S30 for each of the element characteristics FA, FB, and FC will be explained, along with the calculation process.

[0043] --Example of calculation formula for hyperelastic properties FA (model parameter: hyperelastic spring constant K0)-- The superelastic property FA can be calculated by the following formula (2). FA=K0×X (2) In equation (2), K0: Superelastic spring constant X: Displacement is. or, FA=f(X) (3) The superelastic characteristic FA may be expressed as a polynomial of the displacement X, and may be an equation that represents a nonlinear load deflection curve due to the bushing shape.

[0044] As described above, in the element characteristic calculation step (element characteristic calculation process) S30, the hyperelastic characteristic FA can be calculated using equation (2) or equation (3) based on the hyperelastic spring constant K0 identified in the parameter identification step (parameter identification process) S20.

[0045] --Example of calculation formula for viscoelastic characteristics FB (model parameters: viscoelastic spring constant Kc and damping coefficient C)-- In this example, the viscoelastic characteristic FB is a general Maxwell model (Figure 2). Because the viscoelastic characteristic FB is velocity-dependent, it is practically easy to use if the viscoelastic characteristic FB can be calculated from frequency and amplitude, which are generally desired as parameters of the excitation conditions for prediction. From this perspective, in this example, a calculation formula is used that uses frequency (predetermined prediction frequency) freq and amplitude (excitation amplitude) A (predetermined prediction amplitude) as parameters of the predetermined excitation conditions for prediction. First, the following equations (4) and (5) can be formulated. X = A × sin(ωt) (4) ω=2×π×freq (5) In equations (4) and (5), X: Displacement (total displacement) A: Amplitude (excitation amplitude) ω: Angular velocity t: time freq: frequency is.

[0046] Since the spring reaction force and damping force are equal, FB=Kc×x0 (6) FB=C×(V-v0) (7) Kc×x0=C×(V-v0) (8) Kc / C=(V-v0) / x0 (9) =V / x0-v0 / x0 (10) This becomes: In equations (6) to (10), FB: Viscoelastic properties Kc: viscoelastic spring constant C: damping coefficient V: Overall displacement velocity x0: spring displacement v0: spring displacement velocity A: Amplitude (total maximum amplitude) ω: Angular velocity (overall excitation angular velocity) is.

[0047] Here, the displacement of the spring x0 is x0=D×sin(ωt+β) (11) Let's say. In equation (11), D: Maximum spring amplitude β: spring amplitude phase angle is.

[0048] V is the change in X and v0 is the change in x0, so when we differentiate each of them, we get the following. V = A × ω × cos(ωt) (12) v0=D×ω×cos(ωt+β) ···(13) Substituting equations (11) to (13) into equation (10) gives the following: Kc / C=V / x0-v0 / x0 =(A×ω×cosωt) / {D×sin(ωt+β)}-{D×ω×cos(ωt+β)} / {D×sin(ωt+β)} ···(14)

[0049] Here, Kc / C is a constant value regardless of time, so if t=(π / 2) / ω, then equation (14) gives the following: Kc / C=-{D×ω×cos(π / 2+β)} / {D×sin(π / 2+β)} =-{ω×cos(π / 2+β)} / {sin(π / 2+β)} ···(15)

[0050] Here, by the addition theorem of trigonometric functions, sin(ωt+β)=sinωt×cosβ+cosωt×sinβ cos(ωt+β)=cosωt×cosβ-sinωt×sinβ that's why, sin(π / 2+β)=sinπ / 2×cosβ+cosπ / 2×sinβ =cosβ cos(π / 2+β)=cosπ / 2×cosβ-sinπ / 2×sinβ =-sinβ This becomes:

[0051] Therefore, from equation (15), we obtain the following: Kc / C=-{ω×(-sinβ)} / cosβ =ω×sinβ / cosβ (16) =ω×tanβ (17)

[0052] Furthermore, if t=0, then from equation (14), the following is obtained: Kc / C=(A×ω) / (D×sinβ)-(D×ω×cosβ) / (D×sinβ) =(A×ω) / (D×sinβ)-ω×cosβ / sinβ ···(18)

[0053] Substituting equation (16) into equation (18) gives the following: ω×sinβ / cosβ=(A×ω) / (D×sinβ)-ω×cosβ / sinβ (19) ω×sinβ / cosβ+ω×cosβ / sinβ=(A×ω) / (D×sinβ) ···(20) sinβ / cosβ+cosβ / sinβ=A / (D×sinβ) ···(21) D×sinβ=A / (sinβ / cosβ+cosβ / sinβ) ···(22) =A / {(sin 2 β+cos 2 β) / (cosβ×sinβ)} =A×(cosβ×sinβ) / {(sin 2 β+cos 2 β)} ···(twenty three)

[0054] where sin 2 β+cos 2Since β=1, from equation (23), we get: D=A×(cosβ×sinβ) / sinβ =A×cosβ (24)

[0055] Here, from equation (17), the following is obtained: tanβ=(Kc / C) / ω (25) β=arctan{(Kc / C) / ω} ···(26)

[0056] Furthermore, when equation (26) is substituted into equation (24), the following is obtained. D=A×cos[arctan{(Kc / C) / ω}] ···(27)

[0057] Therefore, from equations (6) and (11), the following is obtained: FB=Kc×D×sin(ωt+β) ···(28) Substituting equations (5), (26), and (27) into equation (28), we get the following: FB=Kc×A×cos[arctan{(Kc / C) / ω}]×sin(ωt+β) =Kc×A×cos[arctan{(Kc / C) / (2×π×freq)}]×sin[(2×π×freq)t+arctan{(Kc / C) / (2×π×freq)}] ···(29)

[0058] As described above, in the element characteristic calculation step (element characteristic calculation process) S30, the viscoelastic characteristic FB at the frequency (predetermined prediction frequency) freq and amplitude (excitation amplitude) (predetermined prediction amplitude) A as the predetermined prediction vibration condition can be calculated using equation (29) based on the viscoelastic spring constant Kc and damping coefficient C identified in the parameter identification step (parameter identification process) S20.

[0059] --Example of calculation formula for elastic-plastic properties FC (model parameters: nonlinear coefficient m, pane influence width w, and hysteresis load width h)-- The elastic-plastic characteristic FC represents the small displacement stiffness characteristic portion of rubber known as the Payne effect, and is determined by the rubber compounding and shape. The Payne effect is a phenomenon in which when rubber is subjected to strain, the elastic modulus decreases as the strain amount increases. The load deflection characteristics at minute displacements are nonlinear, and in this example, the characteristic is that it is set to a constant value in a region of relatively large strain. The curve of the elastic-plastic characteristic curve FC' (forward travel portion) can be expressed by the following formulas (30) to (31): Moreover, a schematic diagram of the curve of the elastic-plastic characteristic curve FC' is shown in FIG. 0≦x1 <wのとき、 FC'={x1 m -(m×w m-1 )×x1}×E (30) When x1 ≥ w, FC'=h (31) Here, in equations (30) to (31), m: nonlinear coefficient w: pane influence width (small displacement stiffness nonlinear region) h: Hysteresis load width x1: Displacement E: Hysteresis load width ratio is. The Pain effect width w is the displacement width at which the Pain effect appears.

[0060] The curve of the elastic-plastic characteristic FC' is the curve F1 obtained by multiplying the displacement x1 by the nonlinear coefficient m (Figure 5). F1=x1 m ···(32) A straight line with a slope when the displacement x1 becomes the pain influence width w is drawn from the above, and this is multiplied by the hysteresis load width ratio E, so that the maximum value becomes the hysteresis load width h. To calculate the slope when the displacement x1 becomes the pane influence width w, equation (32) is differentiated as follows: F1'=m×x1 m-1 ···(33) Therefore, the line F2 (Figure 5) with a slope when x1 = w is F2=m×w m-1 ×x1 ···(34) This becomes: If the load obtained by subtracting the straight line F2 with the slope of equation (34) from the curve F1 obtained by multiplying the displacement x1 by the nonlinear coefficient m is F3 (Figure 5), then the following is obtained: F3=x1 m -(m×w m-1 )×x1 ···(35)

[0061] Here, when x1=w, F3=h, so the hysteresis load width ratio (adjustment magnification) E is as follows: E=h / {w m -(m×w m-1 )×w} =h / {w m -(m×w m )} =h / {w m ×(1-m)} (36)

[0062] For strokes where the displacement is greater than the pane influence width w, F3 becomes constant, so 0≦x1 <wのとき、 FC' = F3 × E (37) When x1 ≥ w, FC'=h (38) (a straight line with zero slope) This becomes:

[0063] FC' represents only the forward path during excitation, and in order to convert it into FC, which is a Lissajous figure (round trip path), it is necessary to offset the origin (Fig. 6). Since x1 represents the displacement from the turning point, the total displacement is x1=x1+A (39) This becomes: Similarly, the hysteresis load width h can also be divided into ±, so the origin is offset so that the load center is zero (Figure 6). Then, When the applied amplitude A×2≧w, the offset width Foffset of the origin offset is Foffset=h / 2 (40) And When the added amplitude A×2 < w, FC’ will turn back before reaching h. Therefore, the offset width Foffset of the origin offset is Foffset = {(A×2) m - (m×w m-1 )×(A×2)}×E / 2 ···(41) That is

[0064] Therefore When 0 ≦ x1 < w FC = {(x1 + A) m - (m×w m-1 )×(x1 + A)}×E - Foffset ···(42) When x1 ≧ w FC = h - Foffset ···(43) (a straight line with zero slope) That is From equations (40) to (43), we get the following When the added amplitude A×2 ≧ w and 0 ≦ x1 < w FC = {(x1 + A) m - (m×w m-1 )×(x1 + A)}×[h / {w m ×(1 - m)}] - h / 2 ···(44) When the added amplitude A×2 ≧ w and x1 ≧ w FC = h / 2 ···(45)<00​​​​​​​​​​​​​​​​​​​​​​​​​​​​As described above, in the element characteristic calculation step (element characteristic calculation process) S30, the elastic-plastic characteristic FC at the frequency freq (predetermined prediction frequency) and amplitude (excitation amplitude) A (predetermined prediction amplitude) as the predetermined prediction vibration condition can be calculated using equations (44) to (47) based on the nonlinear coefficient m, pain influence width w, and hysteresis load width h identified in the parameter identification step (parameter identification process) S20.

[0066] (Specific example of measurement step S10 and parameter identification step (parameter identification process) S20) Next, an example of specific calculation contents in the measurement step S10 and the parameter identification step (parameter identification process) S20 will be described. In this example, in the subsequent element characteristic calculation step (element characteristic calculation process) S30, the calculation formula of the specific example described above will be used with reference to FIGS. As described above, in the measurement step S10, the measurement device 2 measures the vibration isolation device VD under one or more predetermined measurement conditions to obtain the measurement results. Then, in the subsequent parameter identification step (parameter identification process) S20, six model parameters of the vibration isolation device VD (hyperelastic spring constant K0, viscoelastic spring constant Kc, damping coefficient C, nonlinear coefficient m, Payne influence width w, and hysteresis load width h) are identified based on the measurement results of the measurement step S10. Specific methods that can be used in the parameter identification step (parameter identification process) S20 include, for example, a method (S20A) of identifying the six model parameters from the load deflection (static spring) characteristics of the vibration isolation device VD, and a method (S20B) of identifying the six model parameters from the dynamic vibration (dynamic spring) characteristics of the vibration isolation device VD, and either of these may be used. Below, the above two methods S20A and S20B will be explained in order, along with the specific contents of the measurement step S10 when using each method.

[0067] - When using the method (S20A) to identify six model parameters from load deflection (static spring) characteristics - When the parameter identification step (parameter identification process) S20 uses a method (S20A) for identifying six model parameters from the load-deflection (static spring) characteristics of the vibration isolation device VD, the preceding measurement step S10 uses the measurement device 2 to measure the load-deflection characteristics of the vibration isolation device VD under two predetermined measurement conditions with different speeds, and the two measured load-deflection characteristics are obtained as measurement results. The speeds under these two predetermined measurement conditions may be set to any speed as long as they are different from each other. Hereinafter, for convenience of explanation, the two predetermined measurement conditions with different speeds will also be referred to as the "predetermined high-speed measurement condition" and the "predetermined low-speed measurement condition," respectively. In the following explanation, as an example, it is assumed that the load deflection characteristics (horizontal axis: displacement (deflection)-vertical axis: load) obtained by measurements under predetermined high-speed measurement conditions and predetermined low-speed measurement conditions in measurement step S10 are as shown by waveforms ga and gb in FIG. 7, respectively. A specific example of a method for identifying model parameters in S20 for each of the element characteristics FA, FB, and FC will be described below.

[0068] <Specific example of how to identify the model parameters (hyperelastic spring constant K0) of hyperelastic FA> The rates of change gc and gd (FIG. 8) of the load-deflection characteristics (load-deflection curve) (FIG. 7) are calculated under the predetermined high-speed measurement conditions and the predetermined low-speed measurement conditions, and the average value of the rates of change extracted within the range where the rate of change is approximately constant (the portion on the thick line in FIG. 8) is calculated. Furthermore, the average value of the above average values ​​under the predetermined high-speed measurement conditions and the predetermined low-speed measurement conditions is taken as the superelastic spring constant K0. The "range in which the rate of change is approximately constant" refers to the range that includes the minimum value of the rate of change, and extends from the point where the rate of change has sufficiently converged (below a predetermined range) from a displacement of 0 to the point where the rate of change begins to increase. Here, the load deflection displaces at a constant speed, and the speed-dependent reaction force (viscoelastic damping force) is also constant, so it does not affect the rate of change (Fig. 8).

[0069] <Specific example of how to identify the model parameters of the viscoelastic characteristics FB (viscoelastic spring constant Kc and damping coefficient C)> Under the specified high-speed measurement conditions and the specified low-speed measurement conditions, the superelastic load is subtracted from the measured value of the load-deflection characteristics (load-deflection curve) (Fig. 7) (waveforms ge and gf in Fig. 9). The difference in load is the damping force for the speed difference between the specified high-speed measurement conditions and the specified low-speed measurement conditions. The load difference between the waveform ge under the predetermined high-speed measurement condition and the waveform gf under the predetermined low-speed measurement condition is, for example, as shown by the waveform gg in FIG. When displacement is at a constant speed, the viscoelastic characteristic FB becomes a nearly constant value after a certain delay time. In the waveform gg, the range where the load becomes a nearly constant value (the part on the thick line in Figure 9) is extracted and set as Fdiff (Figure 9). The "range in which the load is approximately constant" includes the minimum value of the load after a certain displacement (delay time), and is the range from when the load value has sufficiently converged (below a predetermined range) from the displacement to when the load value begins to increase. At this time, the dashpod speed and the overall displacement speed are the same, so the damping coefficient C can be calculated as follows: C=Fdiff / Vdiff (50) Here, in equation (50), Vdiff: Difference in displacement speed between the specified high-speed measurement condition and the specified low-speed measurement condition is.

[0070] Here, as also described in the above formula (6), FB=Kc×x0 (51) From this, the viscoelastic spring constant Kc is calculated. In addition, in equation (51), x0: spring displacement is.

[0071] If the displacement speed under the specified high-speed measurement conditions is Vhi, then the following holds: FB=C×Vhi (52) Furthermore, if the displacement speed under the predetermined low-speed measurement conditions is Vlow, then the following holds: FB=C×Vlow (53) By dividing this FB by the calculated C, the dashpod speed v2 can be calculated. Additionally, by integrating this dashpod velocity, we can obtain dashpod displacement x2.

[0072] The spring displacement x1 is the total displacement minus x2, so we get: x1=X-x2 (54) This x1 becomes an approximately constant value after a certain delay time has elapsed (Fig. 10). In Fig. 10, the range on the waveform where the spring displacement x1 becomes an approximately constant value (the part on the thick line in Fig. 10) is extracted and set as x1 (Fig. 10). The above-mentioned "range in which the spring displacement x1 is approximately constant" refers to the range that includes the maximum value of the spring displacement x1, and extends from the point where the increase in the value of the spring displacement x1 from displacement 0 has sufficiently converged (to a predetermined value or less) to the point where the value of the spring displacement x1 begins to decrease.

[0073] Therefore, the viscoelastic spring constant Kc can be calculated as follows: Kc=FB / x1 (55) It should be noted that Kc can be calculated under both the predetermined high-speed measurement condition and the predetermined low-speed measurement condition (FIG. 10 shows the waveform under the predetermined high-speed measurement condition), so it may be taken as the average value of these.

[0074] <Example of how to identify the model parameters of the elastic-plastic properties FC (nonlinear coefficient m, pain influence width w, and hysteresis load width h)> The reaction force of the elastic-plastic characteristic FC is obtained by subtracting the reaction force of the superelastic characteristic FA and the reaction force of the viscoelastic characteristic FB from the reaction force of the load deflection characteristic measurement result (F in FIG. 11) in the measurement step S10. FC=F-FA-FB (56) As the load deflection characteristic F (F in FIG. 11) in equation (56), one of the load deflection characteristics ga and gb (FIG. 7) obtained by measuring under the predetermined high-speed measurement condition and the predetermined low-speed measurement condition in the measurement step S10 is used. From this FC waveform (Fig. 11), each parameter that satisfies the following equation (57) for the elastic-plastic characteristic FC is extracted. FC={(X+A) m -(m×w m-1 )×(X+A)}×Eh / 2 (57) As mentioned above, E=h / {w m ×(1-m)} (36) is. The nonlinear coefficient m corresponds to the curvature of the curve portion where FC rises due to the Payne effect from the origin to point G (Fig. 12). After point G, FC becomes constant at the value h. In the origin-offset Lissajous figure (FC in Figure 6) described above, at point G where FC begins to become a constant value, the displacement is w / 2 and the load is h / 2 (which occur in the ± directions. This is because the measurement (S10) in this example only measures the load deflection in the forward direction. If the measurement results (S10) are used in both directions, these values ​​can be used as is). Therefore, the curve obtained by doubling these values ​​is taken as the curve of the elastic-plastic characteristics FC, and the displacement and load at point G on this curve are taken as the Payne influence width w and hysteresis load width h, respectively, and the value of the nonlinear coefficient m is identified. The nonlinear coefficient m is calculated from equation (57) for FC, which passes through the three points of the origin, midpoint J, and point G. In other words, the nonlinear coefficient m is found so that equation (57) for FC satisfies (passes through) each of the three points. Here, the midpoint J can be any point between zero displacement and displacement w on the curve of FC obtained from equation (56). It is more practical for the midpoint J to have high accuracy in characteristics, especially at small amplitudes, and it is preferable to locate it at, for example, about displacement w / 10 (FIG. 12). 12, waveform gn shows an example of FC as a measured value, and waveform FC' shows an example of FC when passing through three points in equation (57): the origin, midpoint J, and point G. As shown in the example of Fig. 12, there is a slight difference between the two, but this difference is at a level that does not cause any practical problems, and by approximating it as waveform FC', the number of parameters can be reduced, thereby reducing the amount of calculation.

[0075] - When using the method (S20B) to identify six model parameters from dynamic vibration (dynamic spring) characteristics - When the method (S20B) of identifying six model parameters from the dynamic vibration (dynamic spring) characteristics of the vibration isolation device VD is used in the parameter identification step (parameter identification process) S20, the previous measurement step S10 measures the dynamic vibration characteristics (vibration isolation device characteristics F) of the vibration isolation device VD under each of a total of nine predetermined measurement conditions of three frequency levels x three amplitude levels using the measurement device 2, and obtains each of the nine measured dynamic vibration characteristics as measurement results. However, instead of a total of nine predetermined measurement conditions of three frequency levels x three amplitude levels, predetermined measurement conditions of any multiple levels of frequency x any multiple levels of amplitude may be used. In the following explanation, for convenience of explanation, of the nine predetermined measurement conditions, the three with the smallest amplitudes will be collectively referred to as "condition i," the three with intermediate amplitudes will be collectively referred to as "condition ii," and the three with the largest amplitudes will be collectively referred to as "condition iii." The intermediate amplitude of the three amplitude levels will be referred to as the payne influence width w. Since the payne influence width w cannot be determined by measuring the dynamic vibration characteristics, it can be obtained, for example, by measuring the values ​​of a similar rubber compound or the load deflection characteristics. In the following explanation, as an example, it is assumed that the dynamic vibration characteristics (horizontal axis: vibration frequency - vertical axis: load amplitude) obtained by measuring under each of conditions i, ii, and iii in measurement step S10 are waveforms gj, gi, and gh in Figure 13, respectively.

[0076] If the measuring device 2 has the function of a known dynamic spring tester, the measuring device 2 can measure the following parameters in the measuring step S10. freq: frequency (excitation condition) A: Vibration amplitude (vibration conditions) F: Load reaction force α: Phase difference (load and displacement) By using these parameters as they are for identification, it becomes possible to easily obtain the parameters without using any additional equipment.

[0077] A specific example of a method for identifying model parameters in S20 for each of the element characteristics FA, FB, and FC will be described below.

[0078] <Specific example of how to identify the model parameters (hyperelastic spring constant K0) of hyperelastic FA> For each amplitude (condition ii, condition iii) greater than the pane influence width w, find the intercepts (load amplitudes) se2 and se3 at the intersection of the extension lines (dashed double-dashed lines in Figure 13) of the waveforms gi and gh of the dynamic excitation characteristics (horizontal axis: excitation frequency - vertical axis: load amplitude) and the zero axis of the excitation frequency (Figure 13). When converted into a graph with the horizontal axis being the displacement amplitude and the vertical axis being the load amplitude, the slope of the straight line gk connecting the two points corresponding to the intercepts se2 and se3 above becomes the hyperelastic spring constant K0 (Figure 14).

[0079] <Example of how to identify the model parameters of the elastic-plastic properties FC (nonlinear coefficient m, pain influence width w, and hysteresis load width h)> As mentioned above, the pane influence width w cannot be determined by measuring the dynamic vibration characteristics, so it can be obtained, for example, by measuring the values ​​of similar rubber compounds or the load deflection characteristics. In a graph with the horizontal axis being the displacement amplitude and the vertical axis being the load amplitude, the hysteresis load width h is calculated by doubling the intercept of the straight line gk (Fig. 14). Here, since the intercept is the load amplitude value, it is converted into a ± width and doubled. The nonlinear coefficient m is calculated in the same manner as described above, with reference to the above equations (56) to (57) and Figure 12, etc., from equation (57) for FC, which passes through three points: the origin, midpoint J, and point G on a graph of horizontal axis: displacement vs. vertical axis: load (Figure 15). Here, the midpoint J is the excitation amplitude (condition i) smaller than the payne influence width w and the intercept load minus the hyperelastic load component (Fig. 15). 15, waveform gm indicates an example of FC as a measured value, and waveform gl indicates an example of FC when passing through three points in equation (57): the origin, midpoint J, and point G. The waveform gm in FIG. 15 can be obtained from the measurement results of the dynamic vibration characteristics in measurement step S10.

[0080] <Specific example of how to identify the model parameters of the viscoelastic characteristics FB (viscoelastic spring constant Kc and damping coefficient C)> From the measurement results (load amplitude and displacement-load phase difference) in measurement step S10, a stress sine curve (vibration isolation device characteristic F in Figure 16) is generated, and from this, the hyperelastic characteristic FA and elastoplastic characteristic FC already identified in the above manner are subtracted to extract the viscoelastic characteristic FB (Figure 16), as expressed in the following equation (60). FB=F-FA-FC (60) The curve of this FB is approximated to a sine curve as expressed by the following equation (61). FB=Y×sin(ωt+β) (61) In this way, by approximating the FB curve to a sine curve, it is possible to reduce the amount of calculation while ensuring the accuracy required for practical use.

[0081] By reading the load amplitude Y and the phase difference β (excitation amplitude and load amplitude), each parameter is calculated from the time series characteristics of the viscoelasticity characteristic FB of the above-mentioned equation (28). That is, FB=Kc×D×sin(ωt+β) ···(28) From equation (61), we obtain the following: Y×sin(ωt+β)=Kc×D×sin(ωt+β) ···(62) Y = Kc × D (63) Kc=Y / D (64) Here, the above equation (24), i.e., D = A × cosβ (24) Therefore, from equation (64), we get the following: Kc=Y / (A×cosβ) (65) Furthermore, the above equation (25), i.e., tanβ=(Kc / C) / ω (25) Therefore, from equation (65), we get the following: C = Kc / (ω × tanβ) (66) It should be noted that Kc and C can be calculated under each vibration condition, and therefore the average of each Kc and C may be used as the value. [Industrial Applicability]

[0082] The prediction method, prediction device, prediction system, and prediction program of the present invention can be used to predict the characteristics of any type of vibration isolation device, and can be suitably used, for example, to predict the characteristics of vibration isolation devices (such as bushes) for vehicles. [Explanation of symbols]

[0083] 1. Prediction System 2. Measuring equipment 3 Prediction Device 31 Processing section 32 Storage section 32P Prediction Program 33 Communications Department 34 Input section 35 Display section VD vibration isolation device VDa 1st mounting part VDb 2nd mounting part VDc rubber elastic body

Claims

1. A prediction method for predicting a characteristic of an anti-vibration device, comprising: The vibration isolation device is a first attachment portion configured to be attached to the vibration generating portion side; a second mounting portion configured to be mounted on the vibration receiving portion side; a rubber elastic body that connects the first mounting portion and the second mounting portion to each other; Equipped with The prediction method includes: a parameter identification step of identifying a superelastic spring constant K0, a viscoelastic spring constant Kc, a damping coefficient C, a nonlinear coefficient m, a Payne influence width w, and a hysteresis load width h of the vibration isolation device based on measurement results obtained by performing measurements on the vibration isolation device under predetermined measurement conditions in advance; an element characteristic calculation step of calculating a hyperelastic characteristic FA, a viscoelastic characteristic FB, and an elastic-plastic characteristic FC of the vibration-damping device under predetermined vibration excitation conditions for prediction that are different from the predetermined measurement conditions; a first characteristic prediction step of predicting a first characteristic F of the vibration-damping device under the predetermined prediction excitation condition by adding up the hyperelastic characteristic FA, the viscoelastic characteristic FB, and the elastic-plastic characteristic FC; Including, In the element characteristic calculation step, The superelastic characteristic F is calculated based on the superelastic spring constant K, the viscoelastic characteristic F B is calculated based on the viscoelastic spring constant K c and the damping coefficient C; A prediction method in which the elastic-plastic characteristic FC is calculated based on the nonlinear coefficient m, the Payne influence width w, and the hysteresis load width h.

2. The prediction method according to claim 1, further comprising a second characteristic prediction step of predicting a second characteristic of the vibration isolation device under the specified prediction vibration condition, which is different from the first characteristic F, based on the first characteristic F.

3. The prediction method according to claim 2 , wherein the second characteristic is at least one selected from a dynamic spring constant Kd and a loss factor tanδ of the vibration isolation device.

4. The prediction method according to claim 1 , wherein the predetermined vibration condition for prediction comprises a predetermined frequency for prediction and a predetermined amplitude for prediction.

5. The prediction method according to claim 1 , wherein the measurement result is a measurement result of a load deflection characteristic of the vibration isolation device.

6. The prediction method according to claim 1 , wherein the measurement results are measurements of dynamic vibration characteristics of the vibration isolation device.

7. 1. A prediction device configured to predict a characteristic of an isolator, comprising: The vibration isolation device is a first attachment portion configured to be attached to the vibration generating portion side; a second mounting portion configured to be mounted on the vibration receiving portion side; a rubber elastic body that connects the first mounting portion and the second mounting portion to each other; Equipped with the prediction device includes a processing unit; The processing unit a parameter identification process for identifying the superelastic spring constant K0, viscoelastic spring constant Kc, damping coefficient C, nonlinear coefficient m, Payne influence width w, and hysteresis load width h of the vibration isolation device based on measurement results obtained by performing measurements on the vibration isolation device under predetermined measurement conditions in advance; an element characteristic calculation process for calculating a hyperelastic characteristic FA, a viscoelastic characteristic FB, and an elastic-plastic characteristic FC of the vibration-damping device under predetermined vibration excitation conditions for prediction that are different from the predetermined measurement conditions; a first characteristic prediction process for predicting a first characteristic F of the vibration-damping device under the predetermined prediction excitation condition by adding up the hyperelastic characteristic FA, the viscoelastic characteristic FB, and the elastic-plastic characteristic FC; and In the element characteristic calculation process, The superelastic characteristic F is calculated based on the superelastic spring constant K, the viscoelastic characteristic F B is calculated based on the viscoelastic spring constant K c and the damping coefficient C; A prediction device in which the elastic-plastic characteristic FC is calculated based on the nonlinear coefficient m, the Payne influence width w, and the hysteresis load width h.

8. The prediction device according to claim 7 ; a measurement device configured to perform the measurements; and A prediction system with

9. A prediction program configured to be executed by the processing unit in the prediction device according to claim 7, the parameter identification process; The element characteristic calculation process; the first characteristic prediction process; A prediction program configured to cause the processing unit to execute the above.

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