JP2009133788A - Strength prediction evaluation method, device, program and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To quickly and precisely evaluate at an early design stage having no trial component in consideration of an influence, after baking hardening of a steel material, to predict and readily and accurately evaluate the practical strength of a steel product, to shorten the development period of the steel product, to reduce cost, and to provide the steel product that has high reliability. <P>SOLUTION: A strength prediction evaluation device has a constitution, having a material parameter estimation part 1 for estimating a material parameter of the steel material, after being baking-hardened, relative to a product (a component or a final product) manufactured by using the steel material; and a strength prediction part 2 for predicting and evaluating the practical strength of the steel product. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a strength prediction evaluation method and apparatus, a program, and a recording medium for predicting a practical strength of a part or a final product manufactured using a steel material.

Conventionally, for example, performance prediction evaluation by a finite element method (FEM) has been used for development of an automobile body. Conventionally, generally used strength simulation software inputs the material thickness and work hardening characteristics. Of these, the Swift equation in which the parameters K, ε ₀ , and n are identified by fitting to the true stress σ _eq −true strain ε _eq curve obtained by a tensile test is often used for work hardening characteristics.
σ _eq = K (ε ₀ + ε _eq ) ⁿ

  Alternatively, some FEM software inputs a polygonal line that approximates a true stress-true strain curve. A strength simulation using the material characteristics input in this way is performed, and a material that satisfies a predetermined strength requirement during actual product use is selected, and the product shape, structure, and the like are optimized.

Nakanishi et al. SAE980953 (1998) 31-37) Ueno et al., Automotive Engineering Society 217 (1997) 259-262) Masa et al., Heisei 12 Spring Plasticity Lecture (2000) 17-18)

  In the conventional strength analysis, the material thickness and mechanical property values are used as material data of the member, but in reality, since it has undergone the press forming process and painting process, work hardening, thickness change, and bake hardening (BH: Bake Hardening: Strengthening obtained by diffusing carbon in steel by aging treatment at paint baking temperature and fixing movable dislocations introduced by cold working in the previous process) The material properties and plate thickness differ depending on the part. Also, bake-hardened steel sheets (BH steel sheets) that use these manufacturing histories to increase the strength as parts are also used.

  Up to now, the importance of machining history has been pointed out, and as a strength prediction method considering the machining history of members, (1) the work hardening amount is estimated from the sheet thickness reduction rate and reflected in the input conditions of the strength simulation. Method (refer to Non-Patent Document 1), (2) Method of estimating work hardening amount and thickness change during molding from Vickers hardness distribution and reflecting it in strength simulation (refer to Non-Patent Document 2), (3) Molding There has been proposed a method (see Non-Patent Document 3) in which element information after simulation is taken over by an element of strength analysis. Among these, (2) can also consider the influence of bake hardening by measuring hardness after performing a coating baking process on a member.

  However, none of the methods (1), (2), and (3) is suitable for evaluation at the initial design stage where there are no prototype parts, leaving room for shortening the development period of steel products and reducing costs. There is a problem that.

  The present invention has been made in view of the above-mentioned problems, and allows for quick and accurate evaluation at the initial stage of design in which there is no prototype part in consideration of the effects after bake hardening of steel materials. A strength prediction evaluation method and apparatus, a program, and a recording medium capable of predicting and evaluating strength easily and accurately, reducing the development period of steel products and reducing costs, and supplying reliable steel products The purpose is to provide.

As a result of intensive studies, the present inventors have conceived various aspects of the invention described below.
(1) A strength prediction evaluation method for predicting and evaluating the practical strength of a product manufactured using a steel material, the first step of estimating the material parameters of the steel material after bake hardening, and a forming simulation A second simulation for predicting and evaluating the practical strength of the steel product by performing a strength simulation using at least the plate thickness and strain tensor for each element of the obtained material and the material parameters estimated in the first step. An intensity prediction evaluation method comprising: steps.
(2) In the first step, based on a new stress-strain curve obtained by uniformly adding the bake hardening amount measured by the method specified by the industry standard to the stress-strain curve of the material. The strength prediction evaluation method according to (1), wherein the material parameter is identified.
(3) In the first step, using the yield strength, tensile strength, and true strain value of uniform elongation of the material, and the bake hardening amount measured by a method specified by an industry standard, the material (1) The strength prediction evaluation method according to (1), wherein a parameter is identified.
(4) The strength prediction evaluation method according to (2) or (3), wherein, in the first step, the bake hardening amount is estimated using a yield strength and a tensile strength of the material.
(5) A strength prediction and evaluation apparatus for predicting and evaluating the practical strength of a product manufactured using a steel material, the first means for estimating the material parameter of the steel material after bake hardening, and a molding simulation A second simulation for predicting and evaluating the practical strength of the steel product by performing a strength simulation using at least the plate thickness and strain tensor for each element of the obtained material and the material parameter estimated by the first means. Means for evaluating and evaluating the strength.
(6) The first means is based on a new stress-strain curve obtained by uniformly adding an amount of bake hardening measured by a method specified by an industry standard to the stress-strain curve of the material. The strength prediction evaluation apparatus according to (5), wherein the material parameter is identified.
(7) The first means uses the yield strength, tensile strength, and true strain value of uniform elongation of the material, and the amount of bake hardening measured by a method specified by an industry standard. The intensity prediction evaluation apparatus according to (5), wherein the parameter is identified.
(8) The strength prediction evaluation apparatus according to (6) or (7), wherein the first means estimates the bake hardening amount using a yield strength and a tensile strength of the material.
(9) A program for causing a computer to execute the first step and the second step of the intensity prediction evaluation method according to any one of (1) to (4).
(10) A computer-readable recording medium on which the program according to (9) is recorded.

  According to the present invention, it is possible to perform a quick and accurate evaluation at the initial stage of design in which there is no prototype part in consideration of the effects after bake hardening of steel materials, and to easily and accurately predict and evaluate the practical strength of steel products. In addition to shortening the development period and cost of steel products, it is possible to supply highly reliable steel products.

  Hereinafter, specific embodiments to which the present invention is applied will be described in detail with reference to the drawings.

(Strength prediction evaluation according to this embodiment)
FIG. 1 is a block diagram showing an overview of the strength prediction evaluation apparatus according to the present embodiment. FIG. 2 is a flowchart showing a method for designing a steel material of an automobile having the strength prediction evaluation method according to the present embodiment in order of steps.
The strength prediction evaluation apparatus according to the present embodiment includes a material parameter estimation unit 1 that estimates a material parameter of a steel material after bake hardening for a product (part or final product) manufactured using the steel material, and a steel product An intensity predicting unit 2 that predicts and evaluates a practical intensity is included.

The parameter estimation part 1 identifies the material parameter based on the mechanical property of the steel material after bake hardening, for example by the method mentioned later.
The strength prediction unit 2 includes various factors (here, the coordinates of each node, each element, etc.) for each element (here, a finite element used for FEM analysis) obtained by a molding simulation (molding analysis). The node number, the plate thickness of each element, the strain tensor, the stress tensor, etc.) are inherited to the elements of the strength simulation, and the strength simulation is performed using the material parameters estimated by the parameter estimation unit 1 to Strength is predicted and evaluated (collision analysis). For the intensity simulation, for example, a dynamic explicit method FEM is used.

Hereinafter, the design method of the steel material of a motor vehicle using said strength prediction evaluation apparatus is demonstrated.
First, the shape and structure of the automobile are determined (step S1).
Subsequently, a steel material and a plate thickness for each part of the automobile are selected (step S2).
Subsequently, the shape of each component is specified using a predetermined CAD (step S3) (step S4), and the mold is designed using the predetermined mold CAD (step S5). The data of the mold designed in step S5 is supplied to step S6 together with the shape data of each part specified in step S4.

In step S6, a forming simulation is executed as follows, and various factors (such as the coordinates of each node, the node number of each element, the plate thickness of each element, the strain tensor, and the stress tensor) are determined (step S7).
Here, three-point bending of a member having a hat cross section (60 mm × 60 mm) and a length of 900 mm is evaluated. As a material, for example, a 440 MPa class solid solution reinforced high tensile steel (yield strength σ _y : 302 MPa, tensile strength σ _u : 461 MPa, elongation e _t : 40%, bake hardening Δσ _BH : 60 MPa) is pressed as a material. Used for molding analysis.

Using material data based on the mechanical properties of the material, for example, press molding analysis is performed up to the bottom dead center of the mold, and various factors (coordinates of each node, node numbers of each element, plate thickness of each element, strain tensor, and Stress tensor).
Specifically, for example, the above-described factors are obtained by analyzing the drawing and bending of the hat-shaped portion by dynamic explicit FEM. The thickness reduction rate distribution after the forming analysis is shown in FIG. In this forming analysis, the material inflow is restricted by wrinkle pressing for drawing bending forming, and the thickness is reduced by the tension acting on the vertical wall portion. In addition, bending and unbending that occur when passing through the shoulder portion of the die are also added, and the strain on the surface layer of the vertical wall portion is high.
Next, for example, a finite element model for collision analysis in which spot welding processing (fixed relative displacement between two nodes) is performed with a flat plate at a flange portion at intervals of 30 mm is created.

Subsequently, the parameter estimation unit 1 identifies material parameters based on the mechanical properties of the steel material after bake hardening based on the steel material and plate thickness for each part selected in step S2 (step S8).
Specifically, for example, the plastic anisotropy is assumed to be the same as the material properties of the material, and the work hardening curve after paint baking was measured by the method defined in the industry standard for the stress-strain curve of the material. A new stress-strain curve obtained by uniformly adding the bake hardening amount is used. An example of this new stress-strain curve is shown in FIG.

  Subsequently, the strength predicting unit 2 reflects the factors obtained by the press forming analysis as the initial conditions of the strength simulation in the collision analysis finite element model obtained in step S2, and the baking identified in step S3. Using the material parameters after curing, strength simulation is performed by, for example, dynamic explicit FEM, and the practical strength of the steel product is predicted and evaluated (collision analysis) (step S9). The conditions for collision analysis are shown in FIG.

Subsequently, based on the intensity simulation in step S9, the absorbed energy of each component at the time of the collision, the deformation amount of the automobile toward the passenger room, the acceleration, and the like are estimated (step S10).
Subsequently, it is determined whether or not the estimate in step S10 satisfies the specification specified in the intended state (step S11). If it is determined that the specification is satisfied, the design is terminated. If it is determined that the specification is not satisfied, the material, shape, and structure need to be changed, and the process is executed again from step S2.

Here, the result of the collision analysis according to the present embodiment will be described based on a comparison with the collision analysis using the raw material data.
FIG. 6 is a characteristic diagram showing the relationship between the displacement and the reaction force until 10 ms elapses after the collision. According to the collision analysis according to the present embodiment, the peak load is predicted to be 36% higher than that of the collision analysis of the comparative example, confirming the effectiveness of the present embodiment.

(Specific example of identification of material parameters after bake hardening)
Here, the identification of the material parameters after the bake hardening in step 3 will be described with a specific example.

The bake hardening amount Δσ _BH is generally defined as the amount of increase in tensile stress in the same direction applied to a tensile test piece subjected to tensile prestrain (JIS-G3135). Specifically, the strain introduced at the time of forming the outer panel is simulated by a 2% uniaxial tensile test in the rolling direction. Next, the pre-strained material is subjected to a baking process at 170 ° C. for 20 minutes corresponding to the thermal cycle in the paint baking process of the automobile. Furthermore, a tensile test is performed again in the same direction as the pre-strain direction, and the amount of increase in yield stress due to the baking process is defined as the BH amount.

Normally, the strength and work-hardening characteristics of the material are input in commonly used strength simulation software. Of these, Swift's equation in which parameters K, ε ₀ , and n are identified by fitting to a true stress σ _eq −true strain ε _eq curve obtained by a uniaxial tensile test is often used for work hardening characteristics.
σ _eq = K (ε ₀ + ε _eq ) ⁿ

The parameters are identified by discretizing the stress-strain curve into a number of points and approximating them to a least squares. Therefore, when estimating the material parameters after bake hardening, a new stress-strain curve is obtained by adding the bake hardening amount Δσ _BH measured by the method specified by the industry standard to the stress-strain curve of the material. The material parameters may be identified on the basis of them.

Specific procedures of the parameter identification method are shown in the following (1) to (4).
(1) Calculate the nominal stress-nominal strain curve after bake-hardening by adding the bake-hardening amount Δσ _BH to the nominal stress-nominal strain curve of the material.
(2) Nominal stress-nominal plastic strain conversion obtained by subtracting elastic strain from nominal strain data.
(3) Convert nominal plastic strain to true plastic strain and nominal stress to true stress.
(4) Appropriate initial values are given to K, ε ₀ , n, and fitting to an approximation rule so that the error is minimized by the least square method.

FIG. 7 shows a stress-strain curve after bake hardening whose parameters are identified by the above procedure.
By the way, the material parameter identification method after bake hardening shown so far requires experimental data of the stress-strain curve of the material. However, it is not always possible to prepare experimental data of stress-strain curves used for strength evaluation at the initial design stage where there are no prototype parts. Therefore, Swift is obtained from the amount of bake hardening Δσ _BH estimated as the yield strength σ _y , tensile strength σ _u , and the true strain value ε _{u of the} material that is relatively easily available (or assumed by the designer). It is desired to identify the parameters K, ε ₀ , and n.

The present inventors use the plastic instability conditional expression for the diffusion neck, the yield strength increases to σ _y + Δσ _BH by the baking process, and the parameters K, ε ₀ , n are determined from the relationship between the uniform elongation and the tensile strength. I found out that it is given by
K = σ _u (1 + ε _u ) / n ⁿ
ε ₀ = {(σ _y + Δσ _BH ) / K} ^{1 / n}
n = ε _u

Furthermore, the present inventors have various high strength steel sheets of 340 MPa class to 1180 MPa class (solid solution strengthened steel based on single phase ferrite hardened steel and aluminum killed steel, precipitation strengthened steel, composites such as dual phase and TRIP steel). BH content is measured for (structural steel), and it is found that there is a strong correlation between the yield strength σ _y and tensile strength σ _{u of the material} and the bake hardening amount Δσ _BH, and the bake hardening amount Δσ _BH is estimated. I came up with a way to According to this, the bake hardening amount Δσ _BH can be approximated by the following equation.
Δσ _BH = 1.34σ _y ^1.75 σ _u ^-1

Thereby, the actually measured BH amount can be estimated with reasonable accuracy. FIG. 8 shows the relationship between the measured BH amount and the estimated BH amount.
By using this estimation formula, the material parameters after bake-hardening can be easily obtained without carrying out a tensile test by the method described above.

  As described above, according to the present embodiment, it is possible to perform a quick and accurate evaluation at the initial stage of design in which there is no prototype part in consideration of the effect after the bake hardening of the steel material, and the practical strength of the steel product is facilitated. Moreover, it is possible to accurately predict and evaluate the steel product, for example, to shorten the development period of the vehicle body and to reduce the cost and to supply a highly reliable steel product.

(Other embodiments)
The function of each component (such as the parameter estimation unit 1 and the intensity prediction unit 2 in FIG. 1) that constitutes the above-described intensity prediction evaluation apparatus according to the present embodiment is that a program stored in a RAM or ROM of a computer operates. Can be realized. Similarly, each step (steps S6 to S10 in FIG. 2) of the intensity prediction evaluation method can be realized by operating a program stored in a RAM or ROM of a computer. This program and a computer-readable storage medium storing the program are included in the present invention.

  Specifically, the program is recorded on a recording medium such as a CD-ROM or provided to a computer via various transmission media. As a recording medium for recording the program, besides a CD-ROM, a flexible disk, a hard disk, a magnetic tape, a magneto-optical disk, a nonvolatile memory card, or the like can be used. On the other hand, as the program transmission medium, a communication medium in a computer network system for propagating and supplying program information as a carrier wave can be used. Here, the computer network is a WAN such as a LAN or the Internet, a wireless communication network, or the like, and the communication medium is a wired line such as an optical fiber or a wireless line.

  Further, the program included in the present invention is not limited to the one in which the functions of the above-described embodiments are realized by the computer executing the supplied program. For example, such a program is also included in the present invention when the function of the above-described embodiment is realized in cooperation with an OS (operating system) or other application software running on the computer. Further, when all or part of the processing of the supplied program is performed by the function expansion board or function expansion unit of the computer and the functions of the above-described embodiment are realized, the program is also included in the present invention.

  For example, FIG. 9 is a schematic diagram illustrating an internal configuration of a personal user terminal device. In FIG. 9, reference numeral 1200 denotes a personal computer (PC) having a CPU 1201. The PC 1200 executes device control software stored in the ROM 1202 or the hard disk (HD) 1211 or supplied from the flexible disk drive (FD) 1212. The PC 1200 generally controls each device connected to the system bus 1204.

  By the program stored in the CPU 1201, the ROM 1202, or the hard disk (HD) 1211 of the PC 1200, the procedure of steps S6 to S10 in FIG.

  Reference numeral 1203 denotes a RAM which functions as a main memory, work area, and the like for the CPU 1201. A keyboard controller (KBC) 1205 controls instruction input from a keyboard (KB) 1209, a device (not shown), or the like.

  Reference numeral 1206 denotes a CRT controller (CRTC), which controls display on a CRT display (CRT) 1210. Reference numeral 1207 denotes a disk controller (DKC). The DKC 1207 controls access to a hard disk (HD) 1211 and a flexible disk (FD) 1212 that store a boot program, a plurality of applications, an editing file, a user file, a network management program, and the like. Here, the boot program is a startup program: a program for starting execution (operation) of hardware and software of a personal computer.

  Reference numeral 1208 denotes a network interface card (NIC) that exchanges data bidirectionally with a network printer, another network device, or another PC via the LAN 1220.

It is a block diagram which shows the outline | summary of the intensity | strength prediction evaluation apparatus by this embodiment. It is a flowchart which shows the design method of the steel material of the motor vehicle which has the intensity | strength prediction evaluation method by this embodiment in order of a step. It is a schematic diagram which shows the analysis result of hat-shaped draw-bending forming by forming simulation. It is a characteristic view which shows an example of a new stress-strain curve. It is a schematic diagram which shows the outline | summary of the three-point bending which is a verification object of strength evaluation simulation. FIG. 6 is a characteristic diagram showing the relationship between the deformation reaction force and displacement in consideration of the processing history obtained from the strength evaluation simulation and the influence of bake hardening. It is a characteristic view which shows the relationship of the stress-strain curve after baking hardening. It is a characteristic view which shows the relationship between the measured BH amount and the estimated BH amount. It is a schematic diagram which shows the internal structure of a personal user terminal device.

Explanation of symbols

1 Parameter estimation unit 2 Strength prediction unit

Claims (10)

A strength prediction evaluation method for predicting and evaluating the practical strength of a product manufactured using steel materials,
A first step of estimating material parameters of the steel material after bake hardening;
Strength simulation is performed using at least the plate thickness and strain tensor for each element of the material obtained by the forming simulation and the material parameters estimated in the first step, and the practical strength of the steel product is predicted and evaluated. A strength prediction evaluation method comprising: a second step.   In the first step, the material is based on a new stress-strain curve obtained by uniformly adding an amount of bake hardening measured by a method specified by an industry standard to the stress-strain curve of the material. The method according to claim 1, wherein the parameter is identified.   In the first step, the material parameters are identified using the yield strength, tensile strength, and true strain value of uniform elongation of the material and the bake hardening amount measured by a method specified by an industry standard. The strength prediction evaluation method according to claim 1, wherein:   The strength prediction evaluation method according to claim 2 or 3, wherein, in the first step, the bake hardening amount is estimated using a yield strength and a tensile strength of the material. A strength prediction and evaluation device that predicts and evaluates the practical strength of products manufactured using steel materials,
A first means for estimating material parameters of the steel material after bake hardening;
Strength simulation is performed using at least the plate thickness and strain tensor for each element of the material obtained by the forming simulation and the material parameter estimated by the first means, and the practical strength of the steel product is predicted and evaluated. A strength prediction evaluation apparatus comprising: a second means.   The first means is based on a new stress-strain curve obtained by uniformly adding a bake hardening amount measured by a method specified by an industry standard to the stress-strain curve of the material. The intensity prediction evaluation apparatus according to claim 5, wherein a parameter is identified.   The first means identifies the material parameters using the yield strength, tensile strength, and true strain value of uniform elongation of the material and the bake hardening amount measured by a method specified by an industry standard. The intensity | strength prediction evaluation apparatus of Claim 5 characterized by the above-mentioned.   The strength prediction evaluation apparatus according to claim 6 or 7, wherein the first means estimates the bake hardening amount using a yield strength and a tensile strength of the material.   The program for making a computer perform the said 1st step and said 2nd step of the intensity | strength prediction evaluation method of any one of Claims 1-4.   A computer-readable recording medium on which the program according to claim 9 is recorded.

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