Design Support Device and Design Support Method

Abstract

To support structural designing in consideration of inspection.SOLUTION: A design support apparatus comprises: a defect imparting unit 132 for setting a defect in a shape model of a structure; an inspection signal calculation unit 134 for virtually setting a sensor unit that inspects the set defect for the shape model based on sensor unit setting conditions that are predetermined conditions, and performing a defect inspection simulation using the virtually set sensor unit with a plurality of defect properties; a detection probability calculation unit 135 for calculating a defect detection probability which is a probability of detecting the defect; and a shape modification unit 142 for modifying the shape model based on the defect detection probability.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the technology of a design support device and a design support method.

Background Art

[0002] In the design of railways, automobiles, aircraft, etc., weight reduction of structures is desired. However, generally, simply reducing the weight of a structure is not preferable because the strength decreases. Therefore, as a technique for supporting the design of a structure that achieves weight reduction while satisfying the desired rigidity, shape optimization techniques such as topology optimization are known.

[0003] For example, Patent Document 1 discloses a "design support device 1 including a storage unit 21 in which a plurality of unit structure models having different volume densities and material property values are stored in association with parameters related to the volume density and material property values, an optimization processing unit 22 that acquires the external shape and required characteristics of a structure as input data and outputs, as an analysis result, the strength distribution or density distribution of a structure that satisfies the required characteristics and achieves weight reduction, a model selection unit 23 that selects corresponding unit structure models from among the plurality of unit structure models stored in the storage unit 21 based on the analysis result of the optimization processing unit 22, and an internal structure determination unit 28 that determines the internal structure of the structure by providing the selected unit structure models to corresponding locations of the structure" and a design support program (see the abstract).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Generally, when determining the shape of a structure using a shape optimization method, the shape becomes complex. As a method for manufacturing such a structure with a complex shape, processes such as forming an integrated structure using a three-dimensional printer, lamination, and adhesion have come to be used.

[0006] Even in a structure having a complex shape to which a shape optimization method is applied, defects may occur during manufacturing or may occur due to a load being applied during use. Therefore, as with conventional structures, it is necessary to ensure reliability by performing inspections at the time of shipment or operation.

[0007] On the other hand, as the shape becomes complex, there is a concern that the propagation path of the sensing physical quantity in the inspection becomes complex, making it difficult to ensure sensor accessibility. For example, even if the position where a defect occurs due to a load is inside the structure and ultrasonic inspection is suitable as an inspection method, there may be a case where there is no surface on which an ultrasonic probe can be installed. Alternatively, even if an ultrasonic probe can be installed, there may be a case where ultrasonic waves are reflected, refracted, and scattered by the structure wall surface and a reflection signal from the defect cannot be obtained.

[0008] The present invention has been made in view of such a background, and the object of the present invention is to assist in structural design considering inspection.

Means for Solving the Problems

[0009] In order to solve the above-described problems, the present invention includes a defect setting unit that sets a defect in a shape model of a structure, and a sensor unit for inspecting the set defect with respect to the shape model is virtually set based on sensor unit setting conditions that are predetermined conditions, and the inspection simulation of the defect by the virtually set sensor unit is performed for each defect property indicating the property of the defect, thereby calculating a defect detection probability that is the probability of detecting the defect, and a shape model correction unit that corrects the shape model based on the defect detection probability. Other means for solving the problem will be described as appropriate in the embodiments.

Effects of the Invention

[0010] According to the present invention, it is possible to support the structural design considering inspections.

Brief Description of the Drawings

[0011]

Figure 1

Figure 2

Figure 3A

Figure 3B

Figure 4

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Figure 6

Figure 7

Figure 8A

Figure 8B

Figure 9

Figure 10

Figure 11

Figure 12

Figure 13

Modes for Carrying Out the Invention

[0012] Next, the embodiments for carrying out the present invention (referred to as "embodiments") will be described in detail with reference to the drawings as appropriate. Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the drawings, the same components are denoted by the same reference numerals, and detailed descriptions of overlapping parts will be omitted.

[0013] (Configuration of Design Support Device 1) FIG. 1 is a functional block diagram of the design support device 1 according to the present embodiment. As shown in FIG. 1, the design support device 1 includes an input / output unit 110, a structural analysis unit 120, an inspection analysis unit 130, and an optimization unit (shape model correction unit) 140. The input / output unit 110 further includes a CAD data input processing unit 111, a shape conversion unit 112, a shape output unit 113, and a model database (DB) 114. The structural analysis unit 120 includes a structural calculation unit 121, a rigidity calculation unit 122, and a local stress calculation unit 123. Furthermore, the inspection analysis unit 130 includes a defect database (DB) 131, a defect application unit (defect setting unit) 132, an inspection database (DB) 133, an inspection signal calculation unit 134, and a detection probability calculation unit 135. The optimization unit 140 includes a structural model database (DB) 141 and a shape correction unit (shape model correction unit) 142. Each element constituting each of the units 110 to 140 in the design support device 1 will be described in detail with reference to FIGS. 3A and later. Also, the flow of information indicated by the arrows in FIG. 1 will be described in the flowchart described later.

[0014] (Hardware Configuration of Design Support Device 1) FIG. 2 is a diagram showing the hardware configuration of the design support device 1 according to the present embodiment. The design support device 1 of this embodiment is configured by a computer and includes a main storage device 151 composed of a RAM (Random Access Memory) or the like, a CPU (Central Processing Unit) 152, and an auxiliary storage device 153. Further, the design support device 1 includes an input device 154, a display device (output unit) 155, and a communication interface 156.

[0015] The auxiliary storage device 153 stores programs executed by the CPU 152 and data referred to by these programs. The main storage device 151 functions as a work area during the execution of each program. The input device 154 is composed of a keyboard, a mouse, or the like. The display device 155 is composed of a liquid crystal display device or the like that displays data. The communication interface 156 is a device for connecting to a communication network. These units 151 to 156 are connected via, for example, a bus. The auxiliary storage device 153 includes, for example, a magnetic disk, a magneto-optical disk, a semiconductor memory, etc. as an example.

[0016] A series of processes for realizing the various functions constituting this embodiment are stored in the auxiliary storage device 153 in the form of a program as an example. By the CPU 152 reading these programs into the main storage device 151 and executing information processing and arithmetic operations, the various functions of the units 110 to 140 shown in FIG. 1 are realized. In addition to the form in which the program is pre-installed in the auxiliary storage device 153, a form in which the program is provided in a state stored in another computer-readable storage medium may be applied. Alternatively, a form in which the program is distributed via wired or wireless communication means may be applied. Computer-readable storage media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc.

[0017] In addition, the model database 114, defect database 131, inspection database 133, and structural model database 141 in FIG. 1 are stored in the auxiliary storage device 153 in FIG. 2. However, the model database 114, defect database 131, inspection database 133, and structural model database 141 may be held in a database server separate from the design support device 1.

[0018] (Input / Output Unit 110) Returning to the description of FIG. 1, the input / output unit 110 will be described. The CAD data input processing unit 111 that constitutes the input / output unit 110 is an interface through which a designer inputs the CAD data 200 of a structure (see FIG. 3A). The CAD data input processing unit 111 has functions equivalent to those of generally well-known CAD software, such as loading data previously stored in the auxiliary storage device 153 onto a program via file specification by the designer and editing on the screen. The CAD data 200 includes physical property values (Young's modulus, Poisson's ratio, rigidity ratio, density, etc.) given to each part of the structure as attribute data. Note that the CAD data 200 will be described later.

[0019] (Shape Conversion Unit 112) The shape conversion unit 112 that constitutes the input / output unit 110 generates a shape model 300 (see FIG. 3B) by converting the CAD data 200 input to the CAD data input processing unit 111 into a form that can be used by the structural analysis unit 120 and inspection analysis unit 130 described later. Then, the shape conversion unit 112 holds the generated shape model 300 in the main storage device 151 (see FIG. 2) and stores it in the model database 114.

[0020] (Examples of CAD Data 200 and Shape Model 300) Hereinafter, the processing of the input / output unit 110 will be described with reference to FIGS. 3A and 3A. Appropriate reference will be made to FIG. 1 as well. FIG. 3A is a diagram showing an example of the CAD data 200 input to the CAD data input processing unit 111. FIG. 3B is a diagram showing an example of the shape model 300 after being converted by the shape conversion unit 112. FIG. 3A shows the solid data before conversion by the shape conversion unit 112, and FIG. 3B shows the shape model (mesh data) 300 after conversion by the shape conversion unit 112. The CAD data 200 input to the CAD data input processing unit 111 is generally a solid model composed of a plane, a cylinder, a cone, etc. as shown in FIG. 3A. The form that can be used in the structural analysis unit 120 and the inspection analysis unit 130 is a mesh model used in analysis techniques such as the finite element method as shown in FIG. 3B. The conversion from the solid model to the mesh model is performed by using a program called a mesher or the like. The shape data of the structure after being converted into a mesh model is referred to as the shape model 300.

[0021] In addition, in the present embodiment, as shown in FIGS. 3A and 3B, an example of providing design support for a structure in which two parts E1 and E2 are welded is shown. The two parts E1 and E2 are welded at the weld W.

[0022] As described above, the shape conversion unit 112 holds the converted shape model 300 in the main storage device 151 (see FIG. 2) and also holds it in the model database 114. The shape model 300 held in the model database 114 is appropriately read out by the shape conversion unit 112. The shape model 300 read out by the shape conversion unit 112 is sent to the structural calculation unit 121.

[0023] (Shape output unit 113) Returning to the description of FIG. 1, the shape output unit 113 will be described. The shape output unit 113 that constitutes the input / output unit 110 displays the shape model 300 (see FIG. 3B) held in the model database 114 on the display device 155. The shape model 300 may be three-dimensionally displayed on the display device 155 composed of a liquid crystal display device or the like, or the shape model 300 may be stored as a file in a storage medium such as the auxiliary storage device 153 (see FIG. 2).

[0024] (Structural analysis unit 120) Next, returning to the description of FIG. 1, the structural analysis unit 120 will be described. The structural calculation unit 121 that constitutes the structural analysis unit 120 performs a structural analysis using the finite element method (FEM) with the shape model 300 sent from the shape conversion unit 112, and holds the analysis result in the main memory device 151 (see FIG. 2).

[0025] (Rigidity calculation unit 122) The rigidity calculation unit 122 that constitutes the structural analysis unit 120 calculates the rigidity of the structure that is the basis of the shape model 300 (see FIG. 3B). The rigidity calculation unit 122 calculates the rigidity of the structure, which is the most general as the objective function of the structure and is referred to by the optimization unit 140 described later, from the analysis result by the structural calculation unit 121. The rigidity calculation unit 122 calculates characteristic values such as bending rigidity and torsional rigidity, for example.

[0026] (Local stress calculation unit 123) The local stress calculation unit 123 that constitutes the structural analysis unit 120 extracts the local stress distribution of the structure from the analysis result held by the structural calculation unit 121. The local stress distribution is calculated as the distribution of equivalent stresses such as stress tensors, principal stresses, and von Mises stresses.

[0027] (Example of stress distribution) With reference to FIG. 4, the processing of the local stress calculation unit 123 will be described. FIG. 4 is a diagram showing an example of the stress distribution calculated by the local stress calculation unit 123. In the shape model 300 shown after FIG. 4, the meshes as shown in FIG. 3B are not shown. In FIG. 4, when a load (the white arrow in FIG. 4) is applied to the structure shown by the shape model 300, the stress distribution in the welded part W (indicated by reference numerals 301 to 304) is shown. In the example shown in FIG. 4, the stress is shown in four steps. In the part where the stress is applied, the part indicated by reference numeral 301 has the highest stress, and the stress applied to the part indicated by reference numeral 304 is the weakest. The method of applying the load is set based on the load that occurs during the use of similar products and the load that the user determines to occur based on experience. The calculated stress distribution (reference numerals 301 to 304) is included in the shape model 300 as attribute data. The shape model 300 with the stress analysis result attached as shown in FIG. 4 is appropriately referred to as the stress analysis result 400. Thus, the local stress calculation unit 123 calculates the local stress in the structure based on the shape model 300.

[0028] (Inspection and Analysis Unit 130) Hereinafter, with reference to FIGS. 5 to 10, the defect database 131, defect application unit 132, inspection database 133, inspection signal calculation unit 134, and detection probability calculation unit 135 that constitute the inspection and analysis unit 130 shown in FIG. 1 will be described in detail. Refer to FIG. 1 as appropriate.

[0029] (Defect Database 131) FIG. 5 is a diagram showing a configuration example of the defect database 131. The defect database 131 has defect information such as defect type, size, position deviation, and angle deviation. Here, the defect type is the type information of the defect. The size is the size of the defect. The position deviation is information regarding the occurrence position of the defect. Specifically, the position deviation indicates the deviation of the position with respect to the sensor. The angle deviation is information used when the defect has a direction, such as a crack, and is information regarding the occurrence angle of the defect.

[0030] The defect database 131 stores parameters (probability distributions) of probability distributions such as the mean and standard deviation for each defect property such as size, misalignment, and angular misalignment. For example, for cracks, the mean value and standard deviation of the crack size, the mean and standard deviation of the misalignment, and the mean and standard deviation of the angular misalignment in the target structure are stored in the defect database 131.

[0031] In the defect database 131, in addition to cracks, for voids, incomplete fusion, cracks, etc., the mean and standard deviation are stored for each of size, misalignment, and angular misalignment. Each piece of information stored in the defect database 131 is specified by the user through manual input or from candidates. Specifying from candidates means inputting appropriate data from pre-acquired experimental data, etc. into the defect database 131.

[0032] (Defect imparting unit 132) Next, with reference to FIG. 6, the processing performed by the defect imparting unit 132 shown in FIG. 1 will be described. FIG. 6 is a conceptual diagram for explaining the processing of the defect imparting unit 132. As shown in FIG. 6, based on the stress analysis result 400 sent from the local stress calculation unit 123, the defect imparting unit 132 adds the information in the defect database 131 to create a shape model 300a (300) including defect information (crack C in the example of FIG. 6) and boundary conditions. The boundary conditions are information on the conditions regarding the boundary B between the parts E1, E2 and the weld W.

[0033] In FIG. 6, a shape model 300a is illustrated, which is a shape model 300 that reflects, in the shape model 300, a state in which a crack C as a defect has occurred in a welded portion W (see FIG. 3B). At this time, the occurrence position of the crack C may be automatically given by the defect imparting unit 132 at a place where the stress is large or the like based on the analysis result of the local stress calculation unit 123. Specifically, the defect imparting unit 132 selects the defect occurrence position based on a quantitative index indicating that a defect may occur, such as a stress concentration coefficient, a stress intensity factor, and a maximum value of the equivalent stress, and the type of defect in the defect database 131, based on the stress distribution calculated in step S14 of FIG. 11. Alternatively, the defect imparting unit 132 may impart a defect to the shape model 300 by the user manually specifying a position where it is determined that a risk (crack C in the case of FIG. 6) occurs based on the analysis result of the local stress calculation unit 123. In this way, the defect imparting unit 132 sets a defect in the shape model 300 of the structure. That is, the defect occurrence position is set based on the local stress of the structure calculated by the local stress calculation unit 123 by the defect imparting unit 132.

[0034] Also, after the inspection signal calculation unit 134 (see FIG. 1) determines the installation position of the sensor, the defect imparting unit 132 probabilistically samples the size of the crack C, the deviation from the specified position, the occurrence angle, etc. using the information in the defect database 131. That is, the defect imparting unit 132 causes a statistical fluctuation based on the defect information in the defect database 131 with respect to the defect characteristics such as the size, position (position deviation), angle (angle deviation), etc. of the set crack C.

[0035] (Inspection database 133) FIG. 7 is a diagram showing a configuration example of the inspection database 133. The inspection database 133 stores information on inspection methods, sensor types, sensor specifications, installable conditions (sensor unit setting conditions), detection thresholds, and costs. The inspection methods stored include inspection methods such as eddy current inspection, ultrasonic inspection, and visual inspection. The sensor type (inspection method) stores information on sensor types corresponding to inspection methods (candidates for sensors that can be used in the inspection method), such as eddy current sensors, ultrasonic sensors, and distance measurement sensors used during visual inspection. The sensor specifications store information regarding the specifications of the sensor (sensor unit), such as the aperture, drive frequency, etc. The installable conditions (sensor installation conditions) store the conditions under which the target sensor can be installed, such as the flatness of the structure and the size of the installable space. The detection threshold is the detection threshold of the sensor used when calculating the defect detection probability described later, and the cost is information regarding the cost related to the installation of the sensor.

[0036] When the user selects an inspection method, the sensor type to be used, etc. is determined.

[0037] (Inspection signal calculation unit 134) Figures 8A and 8B are conceptual diagrams showing an example of the process executed by the inspection signal calculation unit 134. Figure 8A shows the irradiation wave, and Figure 8B shows the reflected wave. Here, ultrasonic inspection will be described as an example. The inspection signal calculation unit 134 identifies the installation position of the sensor (ultrasonic sensor in the example shown in Figure 8) (sensor installation position SE) from the shape model 300a of the structure. The sensor installation position SE is a virtual installation position.

[0038] Specifically, first, the inspection signal calculation unit 134 identifies the positions where sensors can be installed (installable positions) by performing optimization calculations based on the shape model 300a, the positions of defects (crack C in the examples of FIGS. 8A and 8B), the installable conditions of the inspection database 133 (see FIG. 7), and the like. For example, when the sensor is an ultrasonic sensor, the inspection signal calculation unit 134 analyzes how ultrasonic waves propagate, calculates the positions where the ultrasonic sensor can be installed based on the analysis results, and identifies the installable positions of the ultrasonic sensor by calculating the optimal calculation positions. Note that multiple installable positions may be identified. When multiple installable positions are identified, the inspection signal calculation unit 134 selects one sensor installation position SE from the multiple installable positions. Then, the inspection signal calculation unit 134 virtually installs a sensor at the washed sensor installation position SE. In this way, the inspection signal calculation unit 134 virtually sets a sensor for inspecting the set defect in the shape model 300 under the installable conditions, which are predetermined conditions.

[0039] As described above, after the inspection signal calculation unit 134 determines the sensor installation position SE, random sampling of the defect characteristics is performed by the defect imparting unit 132.

[0040] Among the installable positions, the unselected positions are sequentially selected after the completion of the processing for the selected positions.

[0041] The inspection signal calculation unit 134 performs a propagation analysis simulation on the sensor corresponding to the selected inspection method, the virtual installation position of the determined sensor (sensor installation position SE in FIGS. 8A and 8B), and the defect characteristics given according to the probability distribution in the defect database 131. In the examples shown in FIGS. 8A and 8B, an ultrasonic propagation analysis simulation (hereinafter, appropriately referred to as a simulation: inspection simulation) of an ultrasonic wave with respect to a crack C by an ultrasonic sensor set in the shape model 300a is being performed. That is, in the examples shown in FIGS. 8A and 8B, the ultrasonic sensor is virtually installed on the surface of the structure. Note that FIG. 8A shows the simulation result when an ultrasonic wave is irradiated from the ultrasonic sensor, and FIG. 8B shows the simulation result of the reflected wave.

[0042] By such calculation, the signal intensity (response signal intensity) of the response received by the sensor (response to a defect by a virtually set sensor: the reflected wave in the example of FIG. 8B) can be calculated by simulation. The calculation performed by the inspection signal calculation unit 134 can be realized using a general physical simulation method. In this way, the response to a defect by a virtually set sensor is calculated by simulation.

[0043] (Signal intensity distribution) FIG. 9 is a conceptual diagram showing an example of the signal intensity distribution that is the calculation result by the inspection signal calculation unit 134. The inspection signal calculation unit 134 repeatedly performs random sampling calculations based on the defect characteristics (a plurality of defect characteristics) randomly sampled by the defect application unit 132, the inspection conditions set with reference to the inspection database 133, etc., and the installation position of the sensor. The defect characteristics indicate the characteristics of the defect, and are the size, position, and angle of the defect. The inspection conditions are information including the type of sensor used (sensor type), the specifications of the sensor, etc.

[0044] That is, based on the information on the size, positional deviation, and angular deviation of the defects stored in the defect database 131 and the inspection conditions in the inspection database 133, the inspection signal calculation unit 134 simulates the response signal intensity when performing inspections on the sizes of various defects, the positions and angles of the defects with respect to the sensors. That is, the inspection signal calculation unit 134 performs simulations of defects by a virtually set sensor for each defect property indicating the properties of the defects. Thereby, the inspection signal calculation unit 134 calculates the response signal intensity of the sensor with respect to the defect size by a so-called Monte Carlo calculation. By this calculation, as shown in FIG. 9, a signal intensity distribution reflecting information such as the uncertainty of the defect occurrence conditions and the possibility of sensor installation is obtained. In FIG. 9, each plot indicates the response signal intensity simulated by the Monte Carlo calculation performed for various defect properties.

[0045] Note that the dashed line 401 in FIG. 9 is a threshold value for determining the presence or absence of detection by the sensor when calculating the defect detection probability shown in FIG. 10. Incidentally, the threshold value indicated by the dashed line 401 is the detection threshold value stored in the inspection database 133 shown in FIG. 7.

[0046] (Detection probability calculation unit 135) FIG. 10 is a diagram showing an example of the defect detection probability calculated by the detection probability calculation unit 135. Based on the calculation result of the inspection signal calculation unit 134, the detection probability calculation unit 135 calculates the detection probability (defect detection probability), which is the probability of detecting a defect. The detection probability calculation unit 135 estimates the defect detection probability as shown in FIG. 10 using the signal intensity distribution as exemplified in FIG. 9. As a method of estimating the defect detection probability as shown in FIG. 10 from the signal intensity distribution as shown in FIG. 9, maximum likelihood estimation methods (statistical analysis of virtual responses) such as the Berans method and the Hit / Miss method are known.

[0047] Here, although the case where an ultrasonic sensor is used is illustrated, when using sensors other than the ultrasonic sensor (such as an eddy current sensor or a distance measuring sensor), the defect detection probability can be calculated by the same process. Also, in FIG. 10, the defect detection probability with respect to the size of the defect (defect size) is obtained, but the defect detection probability with respect to the installation position of the sensor or the installation angle of the sensor with respect to the defect may be obtained.

[0048] (Configuration and Processing of Optimization Unit 140) Returning to the description of FIG. 1, the optimization unit 140 will be described. The optimization unit 140 corrects the shape of the structure that satisfies a predetermined required characteristic based on the defect detection probability calculated by the detection probability calculation unit 135, the bending rigidity, torsional rigidity, and other rigidity information calculated by the rigidity calculation unit 122. Specifically, the optimization unit 140 corrects the shape model 300 (see FIG. 3B) so that the rigidity calculated by the rigidity calculation unit 122 of the structural analysis unit 120 and the defect detection probability calculated by the detection probability calculation unit 135 of the inspection analysis unit 130 satisfy the required characteristics while the mass is reduced. The required characteristics will be described later.

[0049] (Structure Model Database 141) The structure model database 141 stores the required characteristics as various conditions necessary for optimizing the structure. Examples of the required characteristics include constraint conditions, objective functions (required conditions), and the like. The objective functions stored in the structure model database 141 include those related to the mechanical characteristics satisfied by the structure. For example, as the objective function, there are characteristic values such as the bending rigidity and torsional rigidity calculated by the rigidity calculation unit 122. The objective function varies depending on the structure, and the characteristics required by the user for the structure are input as the objective function.

[0050] Among the constraint conditions stored in the structural model database 141, there is a defect detection probability. The structural model database 141 also holds basic structures (such as the flatness of the surface near the defect, etc.) for which a predetermined detection probability (defect detection probability) for defects is guaranteed. For example, the structure of the structure with a high defect detection probability is held in the structural model database 141.

[0051] (Shape correction unit 142) The shape correction unit 142 corrects the shape information of the shape model 300 under the required characteristics while referring to the information stored in the structural model database 141, the information on rigidity (bending rigidity, torsional rigidity, etc.) calculated by the rigidity calculation unit 122, and the defect detection probability calculated by the detection probability calculation unit 135. Specifically, by correcting the shape information of the shape model 300, the shape model 300 itself is corrected and the deformation of the shape model 300 is performed. The corrected shape model 300 is stored in the model database 114 in the input / output unit 110.

[0052] In this way, the shape correction unit 142 performs shape optimization. As a method of shape optimization, for example, there is topology optimization. In topology optimization, when the rigidity does not satisfy the objective function, a material or member with higher rigidity is assigned, and the shape model 300 is corrected. When it is determined that the defect detection probability does not satisfy the required characteristics, the shape model 309 is corrected by flattening the surface shape or the like so that the candidates for the sensor installation positions increase based on the inspection database 133. In this way, the shape correction unit 142 corrects the shape model 300 based on the defect detection probability.

[0053] Also, as one of the topology optimizations, a known algorithm called the density method can be used. In the topology optimization based on the density method, the density is decreased for the elements (each mesh region) that contribute less to the rigidity of the structure among the elements constituting the FEM model.

[0054] The design support device 1 repeats the structural analysis in the structural analysis unit 120, the defect detection probability in the inspection analysis unit 130, and the shape correction in the optimization unit 140 until the required characteristics (constraint conditions, objective function) are satisfied. As a result, an optimized structure of the structure can be obtained that ensures inspectability, extracts unnecessary elements of the structure, and achieves weight reduction within the range where the desired rigidity can be ensured. The obtained optimized structure is output by the shape output unit 113.

[0055] (Flowchart) Next, the processing performed by the design support device 1 according to the present embodiment will be described with reference to FIGS. 11 and 12.

[0056] (Overall flowchart) FIG. 11 is an overall flowchart showing the procedure of the design support method according to the present embodiment. The design support method shown in FIG. 11 is realized, for example, by the CPU 152 reading out a design support program stored in the auxiliary storage device 153 into the main storage device 151 and executing information processing and arithmetic processing. Refer to FIGS. 1 to 10 as appropriate.

[0057] First, the user inputs CAD data 200 (S11). Next, the shape conversion unit 112 converts the input CAD data 200 into a shape model 300 (mesh model: refer to FIG. 3B) and stores it in the shape conversion unit 112 itself (S12). The process of step S12 is the process described in FIGS. 3A and 3B.

[0058] Subsequently, an inspection database (DB) 133, a defect database (DB) 131, and a structural model database (DB) 141 to be used in subsequent calculations are set (S13). The user may input arbitrary values via the input device 154 (see FIG. 2). Alternatively, for example, a candidate list of data to be stored in the inspection database 133, the defect database 131, and the structural model database 141 in advance may be prepared through experiments or the like, and the user may select from the candidates to set the inspection database (DB) 133, the defect database (DB) 131, and the structural model database (DB) 141. In this flowchart, the inspection database 133, the defect database 131, and the structural model database 141 are set in step S13. However, the inspection database 133, the defect database 131, and the structural model database 141 may be set in advance before step S11 is started.

[0059] Next, the structural calculation unit 121 calculates the rigidity and local stress in the shape model 300 (S14). The process of step S14 is the process described in FIG. 4. Then, the inspection analysis unit 130 calculates the defect detection probability with reference to the inspection database 133, the defect database 131, local stress, etc. (S15). The detailed procedure of step S15 will be described later. Then, the optimization unit 140 determines whether the current shape model 300 satisfies the required characteristics stored in the structural model database 141 (S16). Specifically, whether the required characteristics are satisfied is, for example, whether the defect detection probability is equal to or greater than a predetermined value.

[0060] When it is determined that the required characteristics are not satisfied (S16 → No), the shape correction unit 142 corrects the shape model 300 (S17: shape model correction step). Specifically, the shape correction unit 142 corrects the shape model 300 with reference to the structural model database 141, rigidity, and defect detection probability for the elements in the shape model 300 that are determined not to satisfy the required characteristics. Further specifically, the shape correction unit 142 corrects the shape model 300 so that the rigidity of the structure calculated by the rigidity calculation unit 122 satisfies an objective function with a predetermined condition. With reference to the rigidity calculated by the rigidity calculation unit 122, the defect detection probability calculated by the detection probability calculation unit 135, and the structural model database 141, the shape model 300 is corrected by a method such as topology optimization.

[0061] After the correction of the shape model 300 in step S17, the design support device 1 returns the process to step S14. Again, the structural calculation unit 121 calculates the rigidity and local stress, the inspection analysis unit 130 calculates the defect detection probability, and the optimization unit 140 determines whether the current shape model 300 satisfies the required characteristics. In step S16, the shape model 300 is corrected until it is determined that the required characteristics are satisfied.

[0062] Then, in step S16, when it is determined that the required characteristics are satisfied (S16 → Yes), the shape correction unit 142 determines the current shape model 300 as the final shape. The shape model 300 determined as the final shape is stored in the model database 114. Then, the shape output unit 113 displays the final shape model 300 (the shape model 300 corrected by the shape correction unit 142), which is the shape model 300 determined as the final shape, on the display device 155 (S18). The display method in step S18 will be described later.

[0063] (Calculation of Detection Probability) Subsequently, the detailed procedure for calculating the defect detection probability performed in step S15 of FIG. 11 will be described with reference to FIG. 12.

[0064] FIG. 12 is a flowchart showing a procedure for calculating a defect detection probability according to the present embodiment. First, the defect imparting unit 132 sets a defect occurrence position (S150: defect setting step). The process of step S150 has been described with reference to FIGS. 5 and 6.

[0065] The selection of the defect occurrence position may be selected by manual input by the user as described above, or may be automatically selected by the defect imparting unit 132. When the defect imparting unit 132 automatically selects the defect occurrence position, as described above, the defect imparting unit 132 is based on the stress distribution calculated in step S14 of FIG. 11, and is a quantitative index indicating that a defect may occur, such as a stress concentration coefficient, a stress intensity factor, and a maximum value of the equivalent stress, and the defect type in the defect database 131. The defect occurrence position is selected based on the defect type. Based on a database (not shown) in which a quantitative index indicating that a defect may occur, such as a stress concentration coefficient, a stress intensity factor, and a maximum value of the equivalent stress, and the defect type are associated with each other, the defect type may be selected. Note that no statistical fluctuation is set for the defect occurrence position at the stage of step S150.

[0066] Subsequently, the user selects an inspection method suitable for the defect occurring at the defect occurrence position set in step S150 via the input device 154 (S151). The process of step S151 is performed by manual input of the user. Note that the user may refer to the inspection database 133 when selecting a sensor. Also, when the user selects an inspection method, the type of sensor used by the inspection database 133 is also determined.

[0067] Next, the inspection signal calculation unit 134 identifies the positions where the sensors can be installed (installable positions). At this time, the inspection signal calculation unit 134 refers to the sensor setting conditions such as the position of the defect (defect occurrence position) selected in step S150, the inspection method selected in step S151, the sensor corresponding to the inspection method (candidate sensors), and the installable conditions in the inspection database 133 (see FIG. 7: sensor installation conditions) for the shape model 300 with defects. The inspection signal calculation unit 134 identifies the installable positions by using a method such as optimization calculation. As described above, there may be a plurality of identified installable positions (installation positions of a plurality of sensors). When a plurality of installable positions are identified, the inspection signal calculation unit 134 selects one sensor installation position SE (see FIGS. 8A and 8B) from the plurality of installable positions and virtually installs (sets) the sensor on the shape model 300 (S152: inspection analysis step).

[0068] Then, the defect imparting unit 132 samples the defect characteristics and determines the defect characteristics (S153). As described above, the defect characteristics are the size, position, angle, etc. of defects such as cracks. The defect imparting unit 132 randomly samples the size, angular deviation, and deviation from the position selected in step S150 based on the probability distribution for each defect characteristic registered in the defect database 131. That is, the defect imparting unit 132 randomly samples the defect characteristics such as the size, position, and angle of the defect. In this embodiment, random sampling is appropriately referred to as sampling. Thereafter, the defect imparting unit 132 imparts the sampled defect characteristics to the defect occurrence position selected in step S150. Further, the defect imparting unit 132 determines the defect characteristics by setting the size of the defect itself to the sampled size. In this way, the defect imparting unit 132 sets the defect characteristics randomly sampled for the defect.

[0069] Next, the inspection signal calculation unit 134 calculates the response signal intensity acquired by the sensor (S154: inspection analysis step). At this time, the inspection signal calculation unit 134 refers to the following information. (A1) The sensor selected in step S151 (eddy current sensor, ultrasonic sensor, distance measuring sensor, etc.). (A2) The sensor installation position SE installed in step S152 (see FIGS. 8A and 8B). (A3) The defect characteristics randomly sampled in step S153. In step S154, the inspection signal calculation unit 134 simulates the response signal intensity (the acquired response signal intensity) of the sensor based on each condition of (A1) to (A3). The simulation method is appropriately selected from electromagnetic calculations, ultrasonic propagation analysis, heat and light propagation calculations, etc. according to the inspection method. Note that the processing of steps S152 to S154 has been described with reference to FIGS. 7 to 9.

[0070] Subsequently, the inspection signal calculation unit 134 determines whether the number of samples of the response signal intensity acquired in step S154 is sufficient (S155). That is, the inspection signal calculation unit 134 determines whether the response signal intensity calculation with a sufficient number of samples has been performed in the estimation of the defect detection probability described later. Whether the number of samples is sufficient is determined by the following method (B1) or (B2). (B1) The user directly inputs in advance the number of samples considered to be sufficient, and the inspection signal calculation unit 134 determines whether the number of samples has been reached. (B2) The inspection signal calculation unit 134 determines whether the confidence interval of the estimated value of the defect detection probability falls within the range specified in advance by the user. However, when the method of (B2) is performed, it is preferable that the determination of step S155 is performed after the calculation of the defect detection probability in step S156.

[0071] If it is determined that the calculation with a sufficient number of samples has not been performed (S155→No), the inspection signal calculation unit 134 returns to the process of step S153, resamples and determines the defect characteristics. That is, the setting of the defect characteristics by sampling the defect characteristics by the defect imparting unit 132 and the simulation by the inspection signal calculation unit 134 are repeatedly executed.

[0072] When it is determined that a sufficient number of samples have been calculated (S155 → Yes), the detection probability calculation unit 135 calculates the defect detection probability based on the response signal strengths of the sensors calculated up to step S155 (S156: inspection analysis step). In step S156, a signal strength distribution as shown in FIG. 9 is used. As described above, as a method for estimating the defect detection probability using the signal strength distribution as shown in FIG. 9, maximum likelihood estimation methods such as the Berans method and the Hit / Miss method are known. As described above, as the threshold value (lower limit value of the response signal strength at which the sensor determines detection) used at this time, the detection threshold value (see FIG. 7) specified in advance in the inspection database 133 is referred to. Note that the processing of step S156 has been described with reference to FIG. 10.

[0073] Subsequently, the inspection analysis unit 130 determines whether there is an unprocessed sensor installation position SE for the sensor that is the calculation target (S157). The installation position of the unprocessed sensor is an installable position that was not selected in step S152. If there is an unprocessed sensor installation position SE (step S157 → Yes), the inspection analysis unit 130 returns the process to step S152. That is, the inspection signal calculation unit 134 calculates the response of the virtual sensor for the installation positions of the plurality of sensors, and the detection probability calculation unit 135 calculates the defect detection probability for each sensor installation position.

[0074] If there is no unprocessed sensor installation position SE (S157 → No), the inspection analysis unit 130 determines whether there is an unprocessed inspection method (S158). If there is an unprocessed inspection method (step S158 → Yes), the inspection analysis unit 130 returns the process to step S151. If there is no unprocessed inspection method (S158 → No), the inspection analysis unit 130 determines whether there is an unprocessed defect occurrence position (S159). If there is an unprocessed defect occurrence position (step 159 → Yes), the inspection analysis unit 130 returns the process to step S150. When there is no unprocessed defect occurrence position (step S159 → No), the inspection analysis unit 130 ends the process of step S15 in FIG. 11, and the optimization unit 140 performs step S16 in FIG. 11.

[0075] By performing the process of FIG. 12, each time the inspection analysis unit 130 passes through step S156, it calculates the defect detection probability based on a predetermined defect occurrence position, a predetermined inspection method, and a predetermined sensor arrangement.

[0076] And when the inspection analysis unit 130 exits the repetitive process of steps S150 to S159, it means that it has calculated all the defect detection probabilities necessary to determine the constraint conditions used by the optimization unit 140.

[0077] (Display screen) Finally, the display screen 500 in the design support device 1 according to the present embodiment will be described with reference to FIG. 13.

[0078] FIG. 13 is a diagram showing an example of the display screen 500. In step S18 of FIG. 11, the input / output unit 110 displays the sensor arrangement position, the initial shape of the shape model 300, and the final shape of the shape model 300 on the display screen 500 shown in FIG. 13. The initial shape is the shape model 300 before the shape correction by the optimization unit 140, and the final shape is the shape model 300 after the shape correction by the optimization unit 140 is completed. Specifically, the input / output unit 110 displays the display screen 500 shown in FIG. 13 on the display device 155 (see FIG. 2).

[0079] The display screen 500 has an initial shape display area 510, a final shape display area 520, and an analysis result display area 530.

[0080] In the initial shape display area 510, the initial shape of the shape model 300 corresponding to the CAD data 200 input by the user in step S11 of FIG. 11 (see FIG. 3B) is displayed. That is, as described above, the shape model 300 before the shape correction by the optimization unit 140 is displayed in the initial shape display area 510.

[0081] In the final shape display area 520, the final shape of the shape model 300 (the modified shape model 300b (300)) when it is determined (S16→Yes) that the required characteristics are satisfied in step S16 of FIG. 11 is displayed.

[0082] Furthermore, in the final shape display area 520, a sensor arrangement (reference numeral 521) with the highest defect detection probability among a plurality of installable positions (sensor installation positions) is displayed (information regarding the installation position of the sensor with the highest defect detection probability). The installable positions are calculated in step S152 of FIG. 12. A high defect detection probability means, for example, that the defect detection probability curve shown in FIG. 10 rises from a small defect size. In the example shown in FIG. 13, two installation positions (reference numeral 521) are shown as the installation positions of the sensors with the best defect detection probability. Incidentally, the reference numeral 522 in the structure shown in the final shape display area 520 is a defect (a crack in the example of FIG. 13). Also, the coordinate display unit 523 is the coordinates of the installation position of the sensor with the best defect detection probability (information regarding the installation position of the sensor with the highest defect detection probability). In order to display the sensor arrangement (reference numeral 521) with the highest defect detection probability among the plurality of sensor positions, the processes of FIGS. 11 and 12 are performed for each of the plurality of sensor positions.

[0083] The shape model 300b displayed in the final shape display area 520 is made lighter in weight by removing the interior of the structures indicated by the reference numerals E1a and E2a.

[0084] Furthermore, in the analysis result display area 530, an optimization result display area 531, the installation position of the sensor (display window 532a (532)), the adopted inspection method (display window 532b (532)), the result of the modification of the shape model 300, and the achieved defect detection probability (display window 532c (532)) are displayed. The defect detection probability displayed in the display window 532c is the defect detection probability when the sensor is installed at the symbol 521 in the final shape display area 520. That is, it is the highest defect detection probability among a plurality of installable positions (installation positions of a plurality of sensors).

[0085] In the optimization result display area 531, the change history of the shape model 300 performed by the shape modification unit 142 is displayed. In the optimization result display area 531, "Part A" is the component E1 in FIG. 3B, "Part B" is the component E2 in FIG. 3B, and "Part C" is the welded part W in FIG. 3B. Also, the installation position of the sensor displayed in the display window 532a shows the coordinate information regarding the installation position of the sensor (symbol 521) shown in the final shape display area 520 (that is, the coordinate information displayed in the coordinate display unit 523 of the final shape display area 520). The inspection method selected in step S151 of FIG. 12 is displayed in the display window 532b. The defect detection probability displayed in the display window 532c is the defect detection probability when the sensor is installed at the installation position of the sensor (symbol 521) shown in the final shape display area 520. The defect detection probability displayed in the display window 532c is calculated, for example, as the probability of detecting a defect of a preset size from the defect detection probability curve shown in FIG. 10.

[0086] In addition, in the analysis result display area 530, the target value of rigidity (display window 532d (532)), the realized value of rigidity realized in the final shape (modified shape model 300b) (display window 532e (532)), and the mass of the structure in the final shape (structure with respect to the modified shape model 300b) (display window 532f (532)) are displayed. Each piece of information displayed in the analysis result display area 530 is information necessary for the user to determine whether the processing by the design support device 1 is satisfactory and to reflect it in the actual inspection plan. Also, the target value of rigidity (display window 532d) is one of the objective functions and is stored in the structural model database 141. Further, the realized value of rigidity and the mass are calculated by the shape output unit 113 from the shape model 300 of the final shape. Note that each piece of information displayed in the analysis result display area 530 can be recorded in the auxiliary storage device 153 (for example, the model database 114).

[0087] More generally, when the user clicks on any one of the display windows 532a to 532d, the clicked display window 532 becomes blank, and the user may input a value into the blank display window 532. For example, the user inputs the installation position of the sensor into the blank display window 532a. Alternatively, the user inputs the inspection method into the blank display window 532b. Then, the design support device 1 performs the processing shown in FIG. 11 based on the data input into the blank display window 532. And the shape output unit 113 displays, in each display window 532, data other than the newly input data among the results of the processing shown in FIG. 11. For example, when the user inputs the installation position of the sensor into the display window 532a, data other than the installation position of the sensor is displayed in the display windows 532b to 532f. Note that when the user inputs the installation position of the sensor into the display window 532a, in step S152 of FIG. 12, the installation position of the sensor is not set by optimization, and the installation position of the sensor input into the display window 532a is set as it is.

[0088] According to such a design support device 1, it is possible to quantitatively evaluate the inspection index and present to the designer the shape of a structure that is easy to inspect considering non-destructive inspection. Thereby, it becomes possible to support the design.

[0089] That is, according to such a design support device 1, the inspection index is quantitatively evaluated by the defect detection probability. By doing so, when determining the shape of the structure, indices related to future inspectability (feasibility of sensor installation, defect detection probability) can be added, and a shape model 300 of a structure that is easy to inspect considering non-destructive inspection can be presented to the designer, making it possible to support the design.

[0090] In addition, by randomly sampling the defect characteristics based on the probability distribution of the defect characteristics stored in the defect database 131, it is possible to calculate the response signal intensity using quantitative Monte Carlo calculation. Also, by virtually installing the sensor based on the inspection database 133 in which inspection methods, sensor types, sensor specifications, installable conditions of the sensor, etc. are stored, virtual installation of a realistic sensor becomes possible.

[0091] Then, by setting a defect based on the local stress, it is possible to set a defect at a location where a defect is likely to actually occur. Also, based on the rigidity of the structure calculated by the rigidity calculation unit 122, when the shape correction unit 142 corrects the shape model 300, it becomes possible to correct the shape model 300 considering rigidity.

[0092] In addition, by displaying a display screen 500 as shown in FIG. 13, the user can confirm the correction result of the shape model 300 obtained by the design support method.

[0093] Note that the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present invention and are not necessarily limited to those having all the configurations described.

[0094] In addition, each of the above-described configurations, functions, processing units, processing means, etc. may be implemented in hardware by designing part or all of them, for example, using an integrated circuit. Further, each of the above-described configurations, functions, etc. may be implemented in software by a processor interpreting and executing a program for realizing each function. Information such as programs, tables, files, etc. for realizing each function can be stored in a memory, a recording device such as a hard disk, an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.

[0095] In addition, the control lines and information lines show those considered necessary for explanation, and not necessarily all the control lines and information lines are shown in the product. In practice, it may be considered that almost all the components are interconnected.

Explanation of Reference Numerals

[0096] 1 Design support device 110 Input / output unit 111 CAD data input processing unit 112 Shape conversion unit 113 Shape output unit 114 Model database 120 Structural analysis unit 121 Structural calculation unit 122 Rigidity calculation unit 123 Local stress calculation unit 130 Inspection analysis unit 131 Defect database 132 Defect application unit (defect setting unit) 133 Inspection database 134 Inspection signal calculation unit 135 Detection probability calculation unit 140 Optimization unit (shape model correction unit) 141 Structural model database 142 Shape correction unit (shape model correction unit) 154 Input device 155 Display device (output unit) 156 Communication interface 200 CAD data 300 Shape Model 300a Shape Model 300b Shape Model 400 Stress Analysis Result 500 Display Screen 510 Initial Shape Display Area 520 Final Shape Display Area 523 Coordinate Display Section 530 Analysis Result Display Area 531 Optimization Result Display Area 532 Display Window 532a Display Window 532b Display Window 532c Display Window 532d Display Window 532e Display Window 532f Display Window B Boundary C Crack E1 Component E1a Symbol E2 Component E2a Symbol SE Sensor Installation Position (Virtual Installation Position of Sensor Unit) W Weld S17 Modification of Shape Model (Shape Model Modification Step) S150 Set Defect Generation Position (Defect Setting Step) S152 Select Sensor for Shape Model (Inspection Analysis Step) S154 Calculate Response Signal Strength (Inspection Analysis Step) S156 Calculate Defect Detection Probability (Inspection Analysis Step)

Claims

1. A defect setting unit that sets a defect in the shape model of the structure, For the shape model, a sensor unit for inspecting the set defect is virtually set based on sensor unit setting conditions that are predetermined conditions, and the inspection simulation of the defect by the virtually set sensor unit is performed for each defect property indicating the property of the defect, thereby calculating a defect detection probability that is the probability of detecting the defect. An inspection analysis unit, A shape model correction unit that corrects the shape model based on the defect detection probability, A design support device characterized by comprising.

2. The design support device according to claim 1, The defect property is at least the size, position, and angle of the defect, A defect database that holds a probability distribution for each defect property Comprising, The defect setting unit, Based on the probability distribution of the defect property, by randomly sampling the defect property, the randomly sampled defect property is set for the defect, The inspection analysis unit, By statistically analyzing the virtual response of the sensor unit obtained as a result of repeatedly executing the setting of the defect property by random sampling of the defect property by the defect setting unit and the inspection simulation by the inspection analysis unit, the defect detection probability is calculated A design support device characterized by this.

3. The design support device according to claim 1, Having an inspection database that stores an inspection method, candidates for the sensor unit that can be used in the inspection method, and installation conditions for the sensor unit, The inspection analysis unit, For the shape model having the defect set by the defect setting unit, with the inspection method, candidates for the sensor unit, and installation conditions for the sensor unit as the sensor unit setting conditions, the sensor unit is virtually set for the shape model, and the response to the defect by the virtually set sensor unit is calculated by the inspection simulation A design support device characterized by this.

4. The design support device according to claim 1, A local stress calculation unit that calculates local stress in the structure based on the shape model Comprising, The defect setting unit, Sets the occurrence position of the defect based on the local stress of the structure calculated by the local stress calculation unit A design support device characterized by this.

5. The design support device according to claim 1, A rigidity calculation unit that calculates the rigidity of the structure that is the basis of the shape model is provided, the shape model correction unit modifies the shape model so that the rigidity of the structure calculated by the rigidity calculation unit satisfies a required condition that is a predetermined condition characterized by a design support device.

6. The design support device according to claim 5, an output unit that outputs the shape model corrected by the shape model correction unit is provided, the inspection and analysis unit calculates the response of the virtual sensor unit for the installation positions of the plurality of sensor units, the inspection and analysis unit calculates the defect detection probability for the installation position of each sensor unit, to the output unit, together with the corrected shape model, information on the installation position of the sensor unit with the highest defect detection probability among the installation positions of the plurality of sensor units, the inspection method, the highest defect detection probability among the installation positions of the plurality of sensor units, the mass of the structure with respect to the corrected shape model, the rigidity value with respect to the corrected shape model, and the target value of the rigidity value are output characterized by a design support device.

7. A design support device performs a defect setting step of setting a defect in a shape model of a structure, for the shape model, a sensor unit for inspecting the set defect is virtually set based on a sensor unit setting condition that is a predetermined condition, and the inspection simulation of the defect by the virtually set sensor unit is performed for each defect property indicating the property of the defect, thereby calculating a defect detection probability that is the probability of detecting the defect (inspection analysis step), a shape model correction step of correcting the shape model based on the defect detection probability characterized by a design support method of executing.

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