JP6920972B2 - Method for optimizing simulation conditions, manufacturing process simulation equipment, manufacturing process simulation system and program - Google Patents

Description

The present invention relates to a method for optimizing simulation conditions, a manufacturing process simulation device, a manufacturing process simulation system, and a program.

In recent years, machine tools capable of complicated and advanced machining have been provided, and even members that could not be machined in the past are being treated as machining targets. When machining with a machine tool, it is necessary to find and set appropriate machining conditions for each object to be machined. Generally, in order to find an appropriate processing condition, it is necessary to repeat the processing many times to determine whether the processing condition is appropriate. However, as the processing target expands, a great deal of labor and cost are required to set the processing condition. I need it. In order to reduce this labor, efforts are being made to find and set appropriate machining conditions without actually performing machining by simulating machine tool machining with a computer.

Patent Document 1 describes a simulator of a manufacturing process having a plurality of processes. Patent Document 1 discloses a method of optimizing a manufacturing process by closely linking a production control system and a simulator of a manufacturing process in manufacturing an LSI. In the method described in Patent Document 1, the simulator performs a simulation for each manufacturing process to generate data instructing the optimum work procedure and manufacturing conditions, and the production control system uses the generated instruction data for each manufacturing process. Enter into the manufacturing equipment in charge of.

Japanese Unexamined Patent Publication No. 9-34533

By the way, Patent Document 1 does not disclose a technique for optimizing a simulator in a manufacturing process. For example, when a new material or a processed product having a new shape is manufactured through a plurality of processes, the simulator of each process does not correspond to the new material, and the accuracy of the simulation may be low. In that case, since the user cannot set the machining conditions by simulation, for example, there is a possibility that the machining conditions must be set by actually repeating the machining in all the steps.

Therefore, an object of the present invention is to provide a method for optimizing simulation conditions, a manufacturing process simulation device, a manufacturing process simulation system, and a program that can solve the above-mentioned problems.

According to one aspect of the present invention, the method for optimizing the simulation conditions is a method for optimizing the conditions for simulating a manufacturing process including a plurality of steps by a computer, and all the steps of the manufacturing process are executed. The step of calculating the first final result, which is the simulation result of the final product obtained, and the second, which is the result information of the final product obtained by actually executing all the steps of the manufacturing process. The difference between the step of acquiring the final result by the computer, the step of evaluating the degree of agreement between the first final result and the second final result, and the degree of agreement when the degree of agreement is equal to or less than a predetermined threshold value. The step of specifying the process in which the difference was generated, the step of changing the preconditions for simulation in the process in which the difference was generated, and the step after the step in which the difference was generated by applying the changed preconditions. The computer has a step of executing a simulation for the process and re-evaluating the degree of agreement between the newly calculated first final result and the second final result, and the degree of agreement is a predetermined threshold value. The first final result is repeatedly calculated while changing the preconditions until the above is exceeded.

According to one aspect of the present invention, in the step of re-evaluating the degree of agreement, simulation is performed only for the step after the step in which the difference is generated and in which the process related to the step in which the difference is generated is performed. To execute.

According to one aspect of the present invention, in the step of changing the precondition, the precondition is adjusted based on the measurement information about the precondition measured when the step that caused the difference is actually executed. do.

According to one aspect of the present invention, the method of optimizing the conditions of the simulation further includes, for each of the steps, a step of calculating a range of set conditions relating to the operation of the machining machine that performs the machining in the step. ..

According to one aspect of the present invention, the precondition includes at least one of a parameter relating to the performance of a processing machine for performing machining in the step and a parameter relating to a material to be manufactured in the step according to the precondition. Is done.

According to one aspect of the present invention, the method of optimizing the conditions of the simulation is based on the step of accumulating the preconditions when the degree of agreement exceeds a predetermined threshold and the accumulated preconditions. It further has a step of calculating the optimum value of the precondition.

According to one aspect of the present invention, the manufacturing process simulation device is a manufacturing process simulation device that simulates a manufacturing process including a plurality of steps, and is a final manufacturing obtained by executing all the steps of the manufacturing process. The calculation unit that calculates the first final result, which is the simulation result of the product, and the second final result, which is the result information of the final product obtained by actually executing all the steps of the manufacturing process, are acquired. The acquisition unit, the evaluation unit that evaluates the degree of agreement between the first final result and the second final result, and the step that causes a difference in the degree of agreement when the degree of agreement is equal to or less than a predetermined threshold. The calculation unit has a specific unit that specifies A simulation is executed for the above steps, the degree of agreement between the newly calculated first final result and the second final result is re-evaluated, and the preconditions are set until the degree of agreement exceeds a predetermined threshold. The first final result is repeatedly calculated while being changed.

According to one aspect of the present invention, the manufacturing process simulation system includes the manufacturing process simulation apparatus and a processing machine that executes processing of a plurality of the steps included in the manufacturing process. The simulation device acquires manufacturing result information indicating the result of machining executed by the machining machine, and performs a manufacturing process simulation.

According to one aspect of the present invention, the program is a computer program that simulates a manufacturing process including a plurality of steps, and is a simulation result of a final product obtained by executing all the steps of the manufacturing process. A step of calculating the first final result, and a step of the computer acquiring the second final result, which is the result information of the final product obtained by actually executing all the steps of the manufacturing process. , A step of evaluating the degree of agreement between the first final result and the second final result, and a step of specifying the step that caused the difference in the degree of agreement when the degree of agreement is equal to or less than a predetermined threshold. The step of changing the preconditions for the simulation in the process in which the difference was generated, and the process after the process in which the difference was generated by applying the changed preconditions were executed and newly calculated. The computer is made to execute the step of re-evaluating the degree of agreement between the first final result and the second final result, and the preconditions are changed until the degree of agreement exceeds a predetermined threshold value. This is a program in which the first final result is repeatedly calculated.

According to the present invention, it is possible to construct a highly accurate simulator of a manufacturing process having a plurality of processes.

It is a block diagram which shows an example of the simulation system in one Embodiment which concerns on this invention. It is a figure which shows an example of the processing content and setting condition in one Embodiment which concerns on this invention. It is a flowchart which shows an example of the optimization processing of the simulation in one Embodiment which concerns on this invention. It is a figure explaining the range of setting conditions in one Embodiment which concerns on this invention. It is a figure explaining the adjustment process of the precondition of the simulation in one Embodiment which concerns on this invention. It is a figure explaining the accuracy improvement processing of the simulation in one Embodiment which concerns on this invention. It is a figure which shows an example of the hardware composition of the simulation apparatus which concerns on this invention.

<Embodiment>
Hereinafter, a simulation system for a manufacturing process according to an embodiment of the present invention will be described with reference to FIGS. 1 to 7.
FIG. 1 is a block diagram showing an example of a simulation system according to an embodiment of the present invention. As shown in FIG. 1, the simulation system 1 includes a simulation device 10 for a manufacturing process, machine tools or processing devices 3a to 3e for casting, processing, coating, etc., and CAD (computer aided design) systems 2a to 2e. .. Machine tools or processing devices 3a to 3e are referred to as processing devices 3a to 3e. The simulation device 10 and the processing devices 3a to 3e are communicably connected via a network (NW). The processing devices 3a to 3e may be collectively referred to as a processing device 3, and the CAD systems 2a to 2e may be collectively referred to as a CAD system 2. Although not shown, the processing devices 3b to 3e have the same configuration as the processing devices 3a. The control devices 30a to 30e may be collectively referred to as the control device 30, and the input / output units 31a to 31e may be collectively referred to as the input / output unit 31. The same applies to the other functional units (CAM systems 32a to 32e, sensor data processing units 33a to 33e) included in the control devices 30a to 30e. Further, in the simulation system 1, the number of the simulation device 10, the processing device 3, and the CAD system 2 is not limited to the number shown in the figure. For example, two or more simulation devices 10 may be included, and one or four or more processing devices 3 and CAD systems 2 may be included. Further, the processing devices 3a to 3e may be installed in different factories, or may be installed in one factory. The simulation device 10 and the CAD system 2 are computers equipped with a CPU (Central Processing Unit) such as a server.

For example, the manufacturing step α includes a plurality of steps A to E, and the manufacturing of the step A is executed by the processing apparatus 3a. Similarly, the production of the process B is executed by the processing device 3b, and the production in the processes C, D, and E is executed by the processing devices 3c, 3d, and 3e, respectively. The simulation device 10 simulates the manufacturing process α (processes A to E). More specifically, the simulation device 10 inputs the machining contents and setting conditions of the machining performed by the machining devices 3a to 3e into the simulator of each process, simulates the machining by the machining devices 3a to 3e, and manufactures the manufacturing process. Calculate the final product β by α. Here, the processing content is a processing requirement and specification for an intermediate product in each process. Further, the setting condition is an operating condition (machining condition) of the machining apparatus 3 set in the machining apparatus 3 in order to perform appropriate machining. The processing content and the range of setting conditions will be described with reference to FIG.

FIG. 2 is a diagram showing an example of processing contents and setting conditions in one embodiment according to the present invention. As an example of the processing contents shown in FIG. 2A, the hole diameter at the inlet is “w2 (mm)” and the hole diameter at the outlet is “w3 (w3)” in a member made of the material “w1” and having a plate thickness of “w4 (mm)”. The processing content indicating that a hole of "mm)" is formed is shown. For example, a predetermined tolerance is allowed for the hole diameter (inlet) and the hole diameter (outlet). This tolerance is also part of the processing content. In addition, the processing content includes not only items related to shape such as hole diameter and hole depth, but also items related to quality. Items related to quality include, for example, the cross-sectional area of the altered layer, the height of burrs, the size of deposits, and the surface roughness.

FIG. 2B shows an example of a range of setting conditions for realizing this processing content. FIG. 2B shows an example of setting conditions when the processing apparatus 3 is a laser processing machine. The setting conditions of the laser processing machine include, for example, the power of the output laser, the piercing time, the rotation speed of the turning head of the laser, the XY axis feed speed, the defocus amount, the taper angle, the gas pressure of the assist gas, the gas type, and the laser. There is the diameter of the swivel.
When the machining device 3 is a machining center, NC lathe, etc., the setting conditions are, for example, the type of cutting tool, the number of spindle rotations, the feed speed of the linear moving shaft, the presence / absence / type / discharge of cutting water / cutting oil (coolant). Pressure and so on.
As shown in the figure, in the present embodiment, the values of each item of the setting conditions are given in a range. As will be described later, the range of each item is a range determined in consideration of the influence of disturbance such as the installation environment of the processing apparatus 3 and individual differences (material, shape) of the manufacturing object.

The user inputs a value selected from the processing content information of steps A to E and the range information of the setting conditions into the simulation device 10, and sets the simulation result of the final product β calculated by the simulation device 10 as the desired final product β. By comparison, it is confirmed whether or not the desired final product β can be obtained under the input setting conditions. The user adjusts the value of the setting condition selected from the range of the setting condition until the desired final product β is obtained. When the appropriate setting conditions are obtained, the user sets the setting conditions selected in each of the steps A to E in the actual processing devices 3a to 3e, and starts the actual processing. As a result, the desired final product β can be produced in the production process α. Further, the simulation device 10 can efficiently set the setting conditions in the steps A to E for obtaining the final product β.

By using the simulation apparatus 10 in this way, the user can obtain appropriate setting conditions for obtaining the desired final product β before performing the actual production. However, if the simulation by the simulation device 10 deviates from the machining results by the actual machining devices 3a to 3e, the setting conditions set by the simulation device 10 are not appropriate, and the quality of the final product β by the manufacturing process α. May not be accurately represented. In response to such a problem, the simulation device 10 has a function of adjusting various parameters of the simulator (simulation model) used for the manufacturing process simulation according to the actual manufacturing process α to improve the simulation accuracy (optimization). The various parameters are parameters related to the functions and performances of the processing devices 3a to 3e and parameters related to the material of the manufacturing object. In the present embodiment, the accuracy of the simulator is improved by adjusting various parameters according to the machining results by the actual machining devices 3a to 3e and the characteristics of the actual manufacturing target, and the final product β calculated by the simulation device 10 is improved. The simulation result of is closer to the actual final product β.

The simulation device 10 includes an input / output unit 11, a simulation execution unit 12, a manufacturing result evaluation unit 13, a model optimization unit 14, a difference process identification unit 15, a learning unit 16, a storage unit 17, and a communication unit 18.
The input / output unit 11 outputs the machining content information which is the information indicating the machining content, the setting condition information which is the information indicating the setting condition in the machining, and the manufacturing result about the machining actually performed by the machining devices 3a to 3e. Acquire manufacturing result information which is the information to be shown. Further, the manufacturing result information includes, for example, an image obtained by photographing the intermediate product or the final product β, and information on the shape and quality obtained by analyzing the image.

The simulation execution unit 12 refers to a storage unit 17 in which each of the steps A to E included in the manufacturing process α and the execution order thereof and the identification information of the simulator simulating the manufacturing result of each process are stored in association with each other, for example. , Identify the simulation model for each process. Then, the simulation execution unit 12 executes the simulation of each process in order, and calculates the simulation result of the manufacturing result information of the final product. More specifically, the simulation execution unit 12 describes the processing content information and the setting condition information of the process A as the simulation model A of the process A (hereinafter, the simulation model of the process A is described as the simulation model A). The same applies to the process), and the manufacturing result of the process A is calculated. Similarly, the simulation execution unit 12 inputs the machining content information of the process B and the setting condition information into the simulation model B of the process B. Then, the simulation execution unit 12 manufactures the intermediate calculation result B of the process B with respect to the calculation result of the intermediate product of the process A (described as the intermediate calculation result A. The same applies to the other steps). Is calculated. The same applies to the subsequent steps C to E. The intermediate calculation result A includes information on the shape and quality of the intermediate product of the step A, for example, a two-dimensional image and a three-dimensional image of the intermediate product. The simulation execution unit 12 calculates the shape of the intermediate product, which is the result of processing by laser processing or cutting processing, by a known analysis method such as a finite element method or first-principles calculation. The simulation execution unit 12 executes, for example, a program for CAE (computer aided engineering) to perform a simulation. The simulation models A to E included in the simulation execution unit 12 have various calculation formulas (machined hole diameter, machined depth, machined groove width) executed in the CAE program for each process for calculating the intermediate calculation results A to E. Includes calculation formulas for analyzing etc.), parameters applied to the calculation formulas, etc. In this parameter, in addition to the external parameters for setting the machining content information and setting condition information for the processes A to E input from the outside, the internal parameters set internally (parameters related to the performance of the machining devices 3a to 3e, etc.) , Parameter related to material) exists. For example, when the processing device 3b is a laser processing machine, if the material item of the processing content information is "w1", the simulation execution unit 12 selects the material of the manufacturing object among the internal parameters related to the material of the simulation model B. A predetermined value is set for the absorption rate of the laser beam according to the material "w1". Alternatively, the simulation execution unit 12 changes the processing device 3b over time with respect to the output of the laser oscillator and the optical system (for example, lens performance) of the laser processing machine among the internal parameters related to the performance of the processing device 3b of the simulation model B. Set the value according to. For example, if the operating time of the processing device 3b is less than X hours, the simulation execution unit 12 sets 100% for the output of the laser oscillator and 100% for the transmittance of the lens, and if it is X hours or more, the laser oscillator Set the output to 90% (only 90% of the specified output is actually output) and the transmittance of the lens to 90% (only 90% of the output output by the oscillator is transmitted due to deterioration of the lens).
Further, the simulation execution unit 12 has a function of inversely analyzing the setting content information based on the simulation model B when the processing content information is given. As the inverse analysis method, for example, an inverse formulation method, an output error method, a minimum variance estimation method, or the like is used.

The manufacturing result evaluation unit 13 includes the manufacturing result information of the actual final product β of the manufacturing process α acquired by the input / output unit 11 (images of the final product β, measured values measured by a sensor, etc.). The simulation result of the final product β calculated by the simulation execution unit 12 is compared with the simulation result information of the final product β calculated by the simulation execution unit 12, and the simulation result of the final product β is evaluated. Further, the manufacturing result evaluation unit 13 compares the manufacturing result information of the actual intermediate products of each process A to E acquired by the input / output unit 11 with the intermediate calculation results A to E calculated by the simulation execution unit 12. The simulation result by the simulation execution unit 12 is evaluated.

The model optimization unit 14 performs a process of optimizing the simulators (simulation models A to E) executed by the simulation execution unit 12. For example, the model optimization unit 14 optimizes (improves) the simulator by adjusting the values of the internal parameters of the models A to E based on the evaluation results for each process by the manufacturing result evaluation unit 13. ..

In the difference process identification unit 15, when the degree of agreement between the production result information of the final product β in the manufacturing process α and the simulation result information of the final product β is equal to or less than the threshold value, the simulations of any of the processes A to E match. Identify whether the process caused a difference in degree.
The learning unit 16 learns the values of the internal parameters optimized by the model optimization unit 14 to further improve the accuracy of the simulation model.
The storage unit 17 stores processing content information, setting condition information, manufacturing result information, internal parameter values of simulation models A to E, and the like in the processing performed by the processing apparatus 3. The storage unit 17 stores a large number of manufacturing result information of the intermediate products received from a plurality of different processing devices such as the processing devices 3a to 3e in association with the processing content information and the setting condition information at that time. .. Although the storage unit 17 will be described on the premise that it is arranged in the simulation device 10, the storage unit 17 may be arranged in a place where it can be connected from the simulation device 10 via the network (NW). Of course.
The communication unit 18 communicates with the processing devices 3a to 3e. For example, the communication unit 18 receives the manufacturing result information of the intermediate product from the processing devices 3a to 3e.

The processing devices 3a to 3e are processing machines in charge of processing in steps A to E. For example, any of the processing devices 3a to 3e is a laser processing machine that performs processing by irradiating a laser beam. The processing device 3 includes a control device 30 of the processing device 3, a processing execution device 38, and a sensor 39.
The control device 30 is, for example, a computer equipped with an MPU (Micro Processing Unit) such as a microcomputer. The control device 30 controls the operation of the machining execution device 38 based on the machining content information, and processes the machining object.
The machining execution device 38 includes, for example, when the machining device 3 is a laser machining machine, a laser oscillator, a head drive mechanism, an assist gas injection mechanism, an intermediate product installation mechanism, a user's operation panel, and the like. It is the main body.
The sensor 39 is sensors such as a camera, an X-ray CT (computed tomography), a vibration sensor, a displacement sensor, a thermometer, and a scanner that measure a machining result and a machining environment. The sensor 39 may be included in the machining execution device 38, or may be a single sensor independent of the machining execution device 38. The sensor 39 measures the shape of the intermediate product, the processing environment (temperature, vibration, position during processing), and the like.

In the processing device 3, the control device 30 controls the operation of the processing execution device 38 by allowing only the setting conditions within a predetermined range as illustrated in FIG. 2 (b). The control device 30 includes an input / output unit 31, a CAM (computer aided manufacturing) system 32, a sensor data processing unit 33, a processing device control unit 34, a setting condition determination unit 35, a communication unit 36, and a storage unit 37. And have.

The input / output unit 31 accepts input of operation information and setting conditions input by the user from the operation panel, and receives input of CAD data indicating the shape of the object to be manufactured from the CAD system 2. The CAD data includes processing content information. Further, the input / output unit 31 outputs information to be notified to the user to the display provided on the operation panel.

The CAM system 32 generates NC (numerical control) data for processing from the CAD data acquired by the input / output unit 31.
The sensor data processing unit 33 acquires measurement information (measured values and images) obtained by measuring the object to be manufactured by the sensor 39, calculates other information related to processing as necessary, and performs an intermediate product. And the production result information of the final product β is generated. For example, the sensor data processing unit 33 calculates a hole diameter (diameter of a machined hole) by image analysis from an image of an object to be manufactured, or calculates a taper angle using the calculated hole diameter or the like. A known method is used as the image analysis method for calculating the hole diameter.
The processing device control unit 34 controls the operation of the processing execution device 38 based on the NC data generated by the CAM system 32 and the setting condition information, and performs processing.

The setting condition determination unit 35 determines whether or not the input setting condition is included in the range of the predetermined setting condition.
The communication unit 36 communicates with the simulation device 10. For example, the communication unit 36 transmits the production result information of the intermediate product and the final product β to the simulation device 10.
The storage unit 37 stores information such as CAD data acquired by the input / output unit 31.

By using the simulation device 10, the user can efficiently set the optimum value of the setting conditions to be set in the machining devices 3a to 3e in each process without actually performing the machining. For that purpose, as described above, the simulation model of each process is required to have high accuracy. Next, the optimization process of the manufacturing process simulation included in the simulation device 10 will be described.

FIG. 3 is a flowchart showing an example of simulation optimization processing according to the embodiment of the present invention.
First, the user inputs the processing content information of the final product β in the manufacturing process α into the simulation device 10. The input / output unit 11 acquires processing content information (step S11). The storage unit 17 is associated with the processing content information of the final product β (for example, the shape of the final product β, the hole having a diameter of x mm, the coating on the surface, etc.), and each process A to the manufacturing process α. The processing content information in E (forming a shape by cutting in process A, making a hole with a diameter of x mm in process B, coating the surface in process C ...) is stored. The simulation execution unit 12 sets the processing content information in each of the steps A to E based on this information. Further, the simulation execution unit 12 inputs the machining content information of each process A to E into the simulation models A to E as the machining result, and sets the setting in the machining so that the machining result can be obtained by the inverse analysis. The range of conditions or setting conditions is calculated (step S12). Alternatively, the simulation execution unit 12 sets a range of setting conditions in each process based on the machining content information and the machining characteristic information (FIG. 4) (step S12). Here, the range of setting conditions will be described with reference to FIG.

FIG. 4 is a diagram illustrating a range of setting conditions in one embodiment according to the present invention.
The graph of FIG. 4 shows the relationship between the power (setting condition) and the plate thickness (machining content), which are the outputs of the laser when a hole having a predetermined diameter is drilled in a plate made of the material "w1" by a laser processing machine. It is a graph. The vertical axis of the graph of FIG. 4 shows the plate thickness (mm), and the horizontal axis shows the laser power (w). The marks P1 to P16 in the graph are processed to output a laser with the power indicated by the coordinates of the horizontal axis on which the marks are located, and to form holes in the plate of the material "w1" of the plate thickness indicated by the coordinates of the vertical axis. It is the processing result when it was done. The ○ and × marks indicate whether the processing was successful or unsuccessful, respectively. Specifically, the ○ mark indicates the result of satisfying the processing content (success), and the × mark indicates the result of not satisfying the processing content (failure). For example, the mark P1 is a hole satisfying a predetermined processing content when a laser of α (W) is output to a plate of a material “w1” having a plate thickness of w5 (mm) to perform drilling, for example, the hole diameter and quality are good. It shows that a hole was made. From these processing results, when the boundary line for separating the case where the processing is successful and the case where the processing is unsuccessful is calculated by using a predetermined method (statistical analysis, machine learning, etc.), for example, the boundary lines L1 and L2 can be obtained. The region sandwiched between the boundary lines L1 and L2 is considered to be a range of appropriate values that can be set in the setting condition "power" in order to realize the desired machining. According to this idea, for example, when processing a plate having a plate thickness of "w4" mm, the range R1 sandwiched between the boundary lines L1 and L2 on the vertical axis "w4" mm is an appropriate range of laser power. Conceivable.

The storage unit 17 of the simulation device 10 receives a large number of machining characteristic information in which the machining results as illustrated in FIG. 4 and the machining content information in the machining and the setting condition information are associated with each other from the machining devices 3a to 3e and stores a large number of them. In addition to calculating the range of setting conditions by inverse analysis based on simulation models A to E, the simulation execution unit 12 also performs calculation processing of boundary lines L1 and L2 and processing content information (for example, plate thickness "w4"). The range of setting conditions may be calculated by calculating the range of setting conditions (R1) according to "mm".

The processing related to the marks P1 to P16 was performed under various conditions. For example, there are various types depending on the purity of "w1" which is the material of the member, the type and content of components other than "w1", the manufacturing method, and the like. Alternatively, there are various environments in which the processing apparatus 3b performs processing. The simulation execution unit 12 sets a range of setting conditions based on machining results under various non-uniform conditions. As a result, the simulation execution unit 12 can calculate a range of setting conditions in consideration of disturbances that affect the machining result, such as the installation environment of the machining apparatus and individual differences in the manufacturing objects.

For example, the machining results indicated by the marks P1 to P16 include machining content information (plate thickness, etc.) and setting condition information (power, etc.), as well as machining time, machining location, material of the manufacturing object, and machining environment (temperature, temperature, etc.). Information such as humidity, vibration, etc.), the type / model number of the processing apparatus 3, and the total operating time (machining time) after the introduction of the processing apparatus 3 may be associated with the information. Then, the simulation execution unit 12 uses the same material (for example, a member made of “w4” having high purity) from the marks P1 to P16 based on the detailed information of the material of the manufacturing object included in the input processing content information. The range of setting conditions may be set by extracting only the processing result of. Alternatively, when the input / output unit 11 receives the input of information related to the processing environment together with the manufacturing result information, and the simulation execution unit 12 is performed in a processing environment similar to the processing environment input from the processing characteristic information. The range of setting conditions may be calculated by extracting only the processing result of. As a result, it is possible to calculate a range of setting conditions that are more limited according to the actual processing conditions. Further, the user of the processing devices 3a to 3e must find an appropriate setting condition suitable for the actual characteristics of the processing devices 3a to 3e after the simulator is optimized, but the appropriate setting condition is included. The range can be specified by the simulation execution unit 12. As a result, the users of the processing devices 3a to 3e can set the setting conditions in a shorter time.

In addition to the machining characteristic information illustrated in FIG. 4, the storage unit 17 stores, for example, machining characteristic information indicating the relationship between the power and the diameter of the hole for each material, and the simulation execution unit 12 stores the machining characteristic information. , Calculate the appropriate value range of the setting conditions for these other machining characteristic information. Then, the simulation execution unit 12 sets those common ranges as the range for the setting condition "power" for the above-mentioned machining contents. For example, if the laser output is specified in the range of "50 to 100" from the relationship between power and plate thickness and in the range of "40 to 80" from the relationship between power and hole diameter, the simulation execution unit 12 calculates the range "50 to 80" for the setting condition "power".

Further, for example, when the machining apparatus 3 is a cutting machine, the simulation execution unit 12 is based on, for example, machining characteristic information indicating the correspondence between the “cutting amount” (machining content) and the “spindle rotation speed” (setting condition). Set the range of the setting condition "spindle speed".

Here, although the range of the setting condition is calculated based on the inverse analysis and the machining characteristic information, the setting condition (one value) may be calculated. In that case, for example, the simulation execution unit 12 sets the median value of the range of the setting conditions calculated by any of the above methods and the average value of the setting conditions corresponding to the manufacturing result information included in the range as the value of the setting conditions. May be. Further, the simulation execution unit 12 may extract the machining result closest to the machining content information to be simulated this time from the machining characteristic information, and set the value of the setting condition corresponding to the machining result as the value of the setting condition.

Return to the explanation of the flowchart. Next, the user inputs information requesting execution of the simulation to the simulation device 10. Then, the input / output unit 11 receives the input of the simulation execution request, and the simulation execution unit 12 executes the simulation (step S13). Specifically, the simulation execution unit 12 inputs the machining content information of the process A set in step S12 and the representative value (for example, the median value) of the range of the setting conditions into the simulation model A, and sets the internal parameters to a predetermined initial value. Set the value and perform the simulation of step A. When the storage unit 17 stores the processing content information for the simulation of the current process A and the internal parameters optimized for the conditions similar to the setting condition information, the simulation execution unit 12 stores the values. It may be read out and set as an internal parameter of the simulation model A. The simulation execution unit 12 stores the intermediate calculation result A in the storage unit 17.

Next, the simulation execution unit 12 simulates the process B by using the intermediate calculation result A, the processing content information of the process B set in step S12, the representative value of the range of the setting conditions, and the simulation model B. Hereinafter, in the same manner, the simulation execution unit 12 performs simulations in the order of steps C to E, and calculates the simulation result of the final product β. The simulation execution unit 12 outputs the simulation result information to the manufacturing result evaluation unit 13. Further, the simulation execution unit 12 transmits the setting condition information of each of the steps A to E used in the simulation to the processing devices 3a to 3e via the communication unit 18. The processing devices 3a to 3e actually execute the manufacturing process α under the same conditions as the simulation performed for the manufacturing process α (step S14).

For example, in the processing device 3a, the communication unit 36a of the control device 30a receives the setting condition information, and outputs the received setting condition information to the processing device control unit 34a. Further, by the user's operation, the CAD system 2a inputs the CAD data including the machining content information set by the simulation device 10a to the control device 30a. The input / output unit 31a outputs CAD data to the CAM system 32a. Further, the user inputs an operation instructing the execution of machining to the control device 30a. Then, the processing apparatus 3a executes the processing of the step a under the same conditions as the simulation in step S13. Specifically, the CAM system 32 generates NC data from the machining content information, and the machining device control unit 34 controls the operation of the machining execution device 38 based on the NC data and the setting condition information to execute the machining.

When the step A is completed, the camera (sensor 39a) takes a picture of the intermediate product, and the sensor data processing unit 33a analyzes the image of the intermediate product to calculate the shape of the intermediate product and the quality of the intermediate product. (Surface roughness) is calculated. Further, the sensor 39a measures an actually measured value of information regarding the internal parameters of the simulation model A. For example, when the processing device 3a is a laser processing machine, the power of the laser light output from the head of the laser processing machine or the power of the reflected light reflected on the surface of the intermediate product is used by using the power meter (sensor 39a). To measure. Further, the sensor data processing unit 33 analyzes the image of the intermediate product and calculates the width and size of the processing mark by the laser. The power of the laser beam measured by the power meter is related to the performance values of the oscillator and lens among the internal parameters, and the power of the reflected light measured by the power meter is related to the absorption rate of the material among the internal parameters. The width of is related to the beam diameter among the internal parameters. As will be described later, the measured values of the items related to these internal parameters on the actual machine are used for adjusting the internal parameters.
The sensor data processing unit 33a transmits the calculated manufacturing result information (shape, quality) and information on internal parameters to the simulation device 10 via the communication unit 36a. In the simulation device 10, the manufacturing result evaluation unit 13 and the difference process identification unit 15 acquire the manufacturing result information via the communication unit 18.

Next, the production of step B is executed for the intermediate product of step A. For example, the processing device 3b acquires the processing content information and the setting condition information used in the simulation of the process B in the simulation device 10 and performs processing. The sensor 39b takes an image, measures, and the like on the intermediate product in the process B, and transmits the production result information to the simulation device 10. After that, in steps C to E as well, manufacturing, measurement of intermediate products, and transmission of manufacturing result information are performed in order to manufacture the final product β. Further, the manufacturing result information about the final product β is transmitted to the simulation device 10.

When the final product is manufactured, the manufacturing result evaluation unit 13 then compares the manufacturing result information of the final product β with the simulation result information to evaluate the degree of agreement (step S15). For example, the manufacturing result evaluation unit 13 compares the items included in the processing content information of the final product input in step S11. For example, when the processing content information includes three items of outer shape, hole diameter, and surface roughness, the manufacturing result evaluation unit 13 determines the manufacturing result information of the final product β for each of these three items. And the difference between the simulation result information is calculated. The manufacturing result evaluation unit 13 sets the degree of coincidence according to the calculated magnitude of the difference. When the degree of agreement exceeds the threshold value in all the items to be evaluated (step S16; Yes), the accuracy of the simulation of the manufacturing process is sufficiently high, and it is considered that the simulator does not need to be optimized. The simulation execution unit 12 stores the internal parameters of the processes A to E set this time in association with the processing content information, the setting condition information, the simulation result information of the process, and the degree of matching of each item calculated in step S15. It is stored in 17, and the process of this flowchart ends. The items to be evaluated and the threshold value of the degree of agreement that can be set in the simulation device 10 are variable and may be set by the user of the simulation device 10.

When there is an item whose matching degree is equal to or less than the threshold value (step S16; No), the difference process specifying unit 15 identifies a step causing the difference in matching degree (step S17). For example, the difference process identification unit 15 compares the intermediate calculation result A for the process A with the manufacturing result information of the intermediate product acquired from the processing apparatus 3a after executing the process A, and describes the items processed in the process A. Evaluate the degree of agreement. The difference process identification unit 15 evaluates the degree of coincidence in order from step A (or retroactively from step E one step at a time), searches for a step in which the degree of coincidence is equal to or less than the threshold value, and sets the step as the degree of match. Identify as the process that caused the difference.

Alternatively, the difference process specifying unit 15 identifies a process that causes a difference in the degree of matching based on the item in which the degree of matching calculated in step S15 is equal to or less than the threshold value. For example, when the degree of agreement regarding the hole diameter is low, the difference process specifying unit 15 specifies the process B in which the hole is machined as the process in which the difference in the degree of agreement is generated.

When the process that causes the difference is specified, the model optimization unit 14 adjusts the simulation model of the process (step S18). Here, a method of adjusting the value of the internal parameter by utilizing the measurement information about the internal parameter measured in step S14 will be described with reference to FIG. FIG. 5 is a diagram illustrating an adjustment process of preconditions for simulation in one embodiment of the present invention. FIG. 5 shows an example of simulation preconditions (internal parameters). It is assumed that the process that causes the difference is the process B, and the processing device 3b in the process B is a laser processing machine. “Oscillator output” and “lens transmittance” are examples of internal parameters related to the performance of the processing apparatus 3b, and “material absorption rate” is an example of internal parameters related to the material. For convenience of explanation, it is assumed that 100% is set for each parameter as an initial setting. The "oscillator output" of 100% means that when the laser power is set to 100 W under the setting conditions, the simulation model performs the simulation on the premise that the laser beam of 100 w is output from the oscillator. Similarly, the "lens transmittance" of 100% means that the 100w laser beam output from the oscillator is assumed to be output from the head as it is at 100w without being attenuated, and the "material absorption rate". 100% means that the simulation is executed on the premise that all the 100w laser light output from the head is absorbed by the intermediate product.

The model optimization unit 14 acquires measurement information regarding internal parameters from the manufacturing result evaluation unit 13 and adjusts these internal parameters. For example, if the power of the laser set by the setting conditions is 100w but the power of the laser measured by the head is 90W, the model optimization unit 14 sets the internal parameter “oscillator output” to 90, for example. % Is set (Adjustment plan 1). Alternatively, the model optimization unit 14 may set the internal parameter “lens transmittance” to 90% (adjustment plan 2). Alternatively, the model optimization unit 14 may be set so that, for example, the value obtained by multiplying the “oscillator output” and the “lens transmittance” is 90%. With these adjustments, it is possible to execute the machining simulation on the premise that only 90w is actually output even if 100w is set in the setting condition, and it is possible to perform a simulation similar to the machining actually performed by the machining apparatus 3b.

Further, for example, when the reflectance of the intermediate product measured by the power meter is 10%, it is assumed that the total of the absorbed light and the reflected light is the total output without considering the light transmitted through the intermediate product. Since it is considered that 90% of the laser power output from the head is absorbed by the intermediate product, the model optimization unit 14 sets the internal parameter "material reflectance" to 90% (adjustment plan). 3). With this adjustment, even if a 100w laser is output, it is possible to execute a machining simulation on the premise that only 90w is actually absorbed by the intermediate product due to the influence of the shape of the intermediate product, for example. It is possible to simulate machining similar to that performed in 3b.

Further, for example, if the initial setting value of the internal parameter "beam diameter" is Z and the width of the processing mark obtained by image analysis is about 80%, the model optimization unit 14 can perform the internal parameter "beam diameter". Is set to 80%.

By optimizing the simulation model B based on the measurement information about the internal parameters obtained from the result of the actual machining by the machining apparatus 3b in this way, a more realistic simulation model can be constructed, and the steps B and The accuracy of the simulation of the entire manufacturing process α can be improved. When the internal parameters are adjusted, the simulation execution unit 12 executes the simulation of the process B again using the adjusted simulation model B without changing the machining content information and the setting condition information (step S19). The simulation execution unit 12 and the model optimization unit 14 adjust the internal parameters until the degree of agreement between the intermediate calculation result B of the process B and the manufacturing result information of the process B acquired from the processing device 3b exceeds the threshold, and the process The simulation of B is repeated (step S20; No).

When the degree of agreement for the step (step B) that causes the difference exceeds the threshold value (step S20; Yes), the simulation execution unit 12 executes the simulation after the step B (step S21). For example, the simulation execution unit 12 executes simulations of the subsequent steps, steps C to E, using the intermediate calculation result B when the degree of coincidence exceeds the threshold value. Alternatively, the simulations of steps B to E are executed. Further, for example, if additional machining (for example, finishing of the hole) for the hole formed in step B is performed in step E, and steps C to D are steps unrelated to the hole, the simulation execution unit 12 Is that the same result as the intermediate calculation result C and the intermediate calculation result D obtained when the previous simulation is performed is calculated for the intermediate calculation result B without simulating the steps C and D. As a premise, only the simulation of step E may be executed. As a result, the amount of calculation can be reduced and the simulator can be optimized in a shorter time. When the simulation of step E is performed and the simulation result of the new final product β is obtained, the simulation apparatus 10 repeats the process from step S15. After optimizing the simulation model of the process that causes the difference between the simulation result and the actual manufacturing result information of the intermediate product, the process of performing the simulation again and confirming the result is repeated to confirm the manufacturing result of the final product β. When the degree of agreement between the information and the simulation result information exceeds the threshold value, the simulation execution unit 12 determines the internal parameters, the processing content information, the setting condition information, the simulation result information, and the degree of agreement for each item of the processing content for steps A to E. It is stored in the storage unit 17 in association with each other.

Further, the input / output unit 11 displays on the display that the simulation of the manufacturing process has been optimized and notifies the user. The input / output unit 11 displays the range of setting conditions for each process at the time of optimization on the display. The user refers to this display, arbitrarily selects a value from the display, and inputs the value to the simulation device 10. In addition, the user inputs the processing content information to be performed to the simulation device 10. Then, the simulation execution unit 12 is made to execute the simulation of the manufacturing process. The user obtains the simulation result of the final product β by the optimized simulation model. The user adjusts the setting conditions until the simulation result matches the desired final product β. As a result, the user can obtain appropriate setting conditions in steps A to E more efficiently than when setting the setting conditions while actually repeating the processing in the processing devices 3a to 3e.

Note that, for example, if a result in which the degree of agreement exceeds a predetermined threshold value is not obtained even after adjusting the internal parameters a predetermined number of times, a warning message may be notified and the optimization process may be stopped.

According to this embodiment, the accuracy of the simulation model can be improved by adjusting the internal parameters by utilizing the information about the internal parameters measured by the actual machine, and the simulation of the manufacturing process with high accuracy can be realized. In addition to improving the accuracy of the simulation, it is possible to provide the user with a range of setting conditions for each process. As a result, the user can adjust the simulator and obtain information as a guideline for the setting conditions when performing a manufacturing process without accumulating experience such as processing of a new material, so that the user can obtain information in a shorter time. It becomes possible to efficiently find an appropriate setting condition and produce a desired final product.

When the information on the internal parameters is not measured in step S14, the model optimization unit 14 has, for example, the items and the difference values that are stored in the storage unit 17 and have a difference between the manufacturing result information and the simulation result information. The internal parameters may be adjusted based on the items of the internal parameters to be adjusted, the correspondence table of the values, and the current production result information and the simulation result information. For example, if the actual manufacturing result information indicates a machining state (such as a shallow machining depth) in which the laser power is insufficient compared to the simulation result, the influence of the shape and surface condition of the manufacturing object. It is considered that the laser beam is reflected at the site, and the actual absorption rate may be lower than initially expected. With reference to the table in which the internal parameter "material absorption rate" and its adjustment amount are set based on such an assumption, the model optimization unit 14 sets the material absorption rate from 100% among the internal parameters related to the material. Make adjustments such as reducing it to 90%.
Alternatively, the learning unit 16 learns the relationship between the items having a difference, the difference, the internal parameters to be adjusted and the adjustment amount, and the model optimization unit 14 adjusts the parameters based on the learning result. good.

Note that FIG. 1 shows an example of a manufacturing process α in which each process is configured by processing by the processing devices 3a to 3e, but the processing is not limited to the processing by the processing device. For example, step E may be a manual step. In that case, for example, the worker in step E may take an image of the intermediate product (final product) and transmit the result of analyzing the image by the computer device to the simulation device 10. Further, the process B may be a transportation process between factories or the like, or may be a storage process of storing the product in a warehouse or the like for a certain period of time.
Further, although the case where the processing device 3b is a laser processing machine has been described as an example, the processing device 3a and the like are not limited to the laser processing machine, and may be other processing machines such as a machining center and an NC lathe.

When the process described with the flowchart of FIG. 3 is performed, the value of the internal parameter when the degree of coincidence between the manufacturing result information and the simulation result information exceeds a predetermined threshold value is accumulated in the storage unit 17. By using this accumulated information, the accuracy of the simulator can be further improved. For example, the storage unit 17 stores a plurality of sets of internal parameters of the simulation model B when the degree of coincidence exceeds a predetermined threshold value. For example, among the internal parameters, the combination of the values of "oscillator output", "lens transmittance", and "material absorption rate" (set of internal parameters) and the combination were used to perform the simulation (drilling) of step B. An example of the degree of matching (the degree of matching of the hole diameter) is shown below. Each value is "oscillator output", "lens transmittance", "material absorption rate", and "matching degree" in order from the left.

The learning unit 16 learns these internal parameter sets 1 to 4, and calculates the optimum values for each of the internal parameters "oscillator output", "lens transmittance", and "material absorption rate" of the simulation model B. .. For example, the learning unit 16 may calculate the average value of the four internal parameter sets and set the average value as the optimum value of each internal parameter. Alternatively, the learning unit 16 may calculate a weighted average based on the degree of agreement and use it as the optimum value for each internal parameter. (For example, the optimum value of "oscillator output" may be calculated by (90% x 95% + 95% x 96% + 100% x 92% + 95% x 98%) / 4.

Alternatively, the learning unit 16 outputs the simulation result information when the processing content information and the setting condition information are input using the processing content information, the setting condition information, and the simulation result information when the degree of coincidence exceeds the threshold as teacher data. The logical model to be used may be constructed by a machine learning or deep learning method (for example, a neural network).

FIG. 6 is a diagram illustrating a simulation accuracy improving process according to the embodiment of the present invention.
First, the simulation execution unit 12 performs the optimization process described with reference to FIG. 3, and obtains processing content information, setting condition information, and simulation result information when the degree of agreement between the manufacturing result information and the simulation result information exceeds a predetermined threshold value. , The value of the internal parameter and the degree of coincidence are associated and stored in the storage unit 17 (step S31).
Next, the learning unit 16 learns the relationship between the machining content information, the setting condition information, and the internal parameter for each process, and calculates the optimum value of the internal parameter for each machining content information and the setting condition information (step S32). As for the calculation method of the optimum value, for example, the learning unit 16 groups the data in which the values of each item of the processing content information and the setting condition information are similar, and averages the values of the internal parameters of the data belonging to the same group. A method such as setting the weighted average value based on the value or the degree of agreement as the optimum value may be used. The learning unit 16 stores the calculated optimum values of the internal parameters in the storage unit 17 in association with the values of the processing content information and the setting condition information for being classified into the group.

Next, when the simulation execution request is received, the simulation execution unit 12 executes the simulation using the optimum values of the internal parameters calculated in step S32 (step S33). For example, the simulation execution unit 12 determines which group the machining content information and setting condition information in this simulation corresponds to in step S32, based on the machining content information and setting condition information set for each process. , The optimum value of the internal parameter of the group determined to be applicable is read from the storage unit 17 and set in the simulation model of the process together with the machining content information and the setting condition information. Then, the simulation execution unit 12 executes the simulation of the process. Thereby, the high accuracy of the simulation can be further improved.

The storage unit 17 of the simulation device 10 stores the values of the internal parameters optimized for each of various machining content information and setting condition information, and stores these machining content information, setting condition information, and optimized internal parameters. A service may be provided to the user as a template of a simulator for each process to be assembled. For example, the input / output unit 11 displays a screen for accepting language selection, and when a language is selected, an input field for processing content information of the final product, setting condition information for each process, and a template selection field for each process. , Display the screen displaying the simulation execution instruction button etc. in the selected language. Then, upon receiving the input of the processing content information of the final product and the input of the simulation execution instruction, the simulation execution unit 12 identifies the simulation model of each process based on the input processing content information of the final product. Furthermore, the values of the internal parameters in the selected template are set in the simulation model of the process, and the simulation is executed. Then, the input / output unit 11 displays the simulation result information of the final product by the simulation execution unit 12 on the display. When a desired simulation result is obtained, the simulation apparatus 10 may register a template for each process using the machining content information, setting condition information, and internal parameters used in this simulation as a new simulator. Further, the simulation device 10 and the billing system may be linked so that the user charges each time the simulation is performed.

Similarly, a service may be provided in which the processing content information, setting condition information, and manufacturing result information of each process are input to the user to optimize the simulation and provide a simulator after the optimization. As a result, the user can perform the simulation by the simulator applied to the actually executed manufacturing process.

(Hardware configuration)
The simulation device 10 can be realized by using a general computer 500. FIG. 7 shows an example of the configuration of the computer 500.
FIG. 7 is a diagram showing an example of the hardware configuration of the simulation device according to the present invention.
The computer 500 includes a CPU (Central Processing Unit) 501, a RAM (Random Access Memory) 502, a ROM (Read Only Memory) 503, a storage device 504, an external I / F (Interface) 505, an input device 506, an output device 507, and communication. It has I / F 508 and the like. These devices send and receive signals to and from each other via bus B.

The CPU 501 is an arithmetic unit that realizes each function of the computer 500 by reading programs and data stored in the ROM 503, the storage device 504, and the like on the RAM 502 and executing processing. For example, each of the above-mentioned functional units is a function provided in the computer 500 by the CPU 501 reading and executing a program stored in the ROM 503 or the like. The RAM 502 is a volatile memory used as a work area or the like of the CPU 501. The ROM 503 is a non-volatile memory that retains programs and data even when the power is turned off. The storage device 504 is realized by, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), or the like, and stores an OS (Operation System), an application program, various data, and the like. The external I / F 505 is an interface with an external device. The external device includes, for example, a storage medium 509 and the like. The computer 500 can read and write to the storage medium 509 via the external I / F 505. The storage medium 509 includes, for example, an optical disk, a magnetic disk, a memory card, a USB (Universal Serial Bus) memory, and the like.

The input device 506 is composed of, for example, a mouse, a keyboard, or the like, and inputs various operations or the like to the computer 500 in response to an instruction from the operator. The output device 507 is realized by, for example, a liquid crystal display, and displays the processing result by the CPU 501. The communication I / F 508 is an interface for connecting the computer 500 to a network such as the Internet by wire communication or wireless communication. The bus B is connected to each of the above-mentioned constituent devices, and various signals and the like are transmitted and received between the constituent devices.

The process of each process in the simulation device 10 described above is stored in a computer-readable storage medium in the form of a program, and the program is read and executed by the computer 500 on which the simulation device 10 is mounted. The above processing is performed. Here, the computer-readable storage medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like. Further, this computer program may be distributed to a computer via a communication line, and the computer receiving the distribution may execute the program.

Further, the above program may be for realizing a part of the above-mentioned functions. Further, it may be a so-called difference file (difference program) that can realize the above-mentioned function in combination with a program already stored in the computer system.
Further, the simulation device 10 may be composed of one computer or a plurality of computers connected so as to be able to communicate with each other. Further, even if the functional units (simulation execution unit 12, manufacturing result evaluation unit 13, model optimization unit 14, learning unit 16, storage unit 17) of the simulation device 10 are mounted on any or all of the control devices 30a to 30e. good.

In addition, it is possible to replace the components in the above-described embodiment with well-known components as appropriate without departing from the spirit of the present invention. Further, the technical scope of the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present invention.
The simulation device 10 is an example of a manufacturing process simulation device, the simulation system 1 is an example of a manufacturing process simulation system, the optimization process of FIG. 3 is an example of a manufacturing process simulation optimization method, and the simulation result information of the final product is the first. An example of the final result, the production result information of the final product is an example of the second final result. The simulation execution unit 12 and the model optimization unit 14 are examples of the calculation unit. The difference process specifying unit 15 is an example of the specific unit. The communication unit 18 is an example of an acquisition unit. The manufacturing result evaluation unit 13 is an example of the evaluation unit. The processing devices 3a to 3e are examples of processing machines. The adjustment of the internal parameters of the simulation model is an example of the method of optimizing the conditions of the machining simulation.

1 ... Simulation system 2, 2a, 2b ... CAD system 3, 3a, 3b ... Processing device 10 ... Simulation device 11 ... Input / output unit 12 ... Simulation execution unit 13 ... Manufacturing result evaluation unit 14 ... Model optimization unit 15 ... Difference process identification unit 16 ... Learning unit 17 ... Storage unit 18 ... Communication unit 30, 30a ... Control devices 31, 31a ... Input / output units 32, 32a ... CAM system 33, 33a ... Sensor data processing units 34, 34a ... Processing device control units 35, 35a ... Setting condition determination units 36, 36a ... Communication Units 37, 37a ... Storage units 38, 38a ... Processing execution devices 39, 39a ... Sensors

Claims (9)

It is a method of optimizing the conditions for computer simulation of a manufacturing process that includes multiple processes.
A step of calculating a first final result, which is a simulation result of a final product obtained by executing all the steps of the manufacturing process, and a step of calculating the first final result.
A step in which the computer acquires a second final result, which is result information of a final product obtained by actually executing all the steps of the manufacturing process.
A step of evaluating the degree of agreement between the first final result and the second final result, and
When the degree of coincidence is equal to or less than a predetermined threshold value, the step of specifying the step that caused the difference in the degree of coincidence and the step of identifying the step.
The step of changing the simulation preconditions in the process that caused the difference, and
A simulation is executed for the steps after the step in which the difference is generated by applying the changed preconditions, and the degree of agreement between the newly calculated first final result and the second final result is re-matched. Steps to evaluate and
Have,
The computer repeatedly calculates the first final result while changing the preconditions until the degree of agreement exceeds a predetermined threshold.
How to optimize the simulation conditions. In the step of re-evaluating the degree of agreement, the simulation is executed only for the step after the step in which the difference is generated and in which the process related to the step in which the difference is generated is performed.
The method for optimizing the simulation conditions according to claim 1. In the step of changing the precondition, the precondition is adjusted based on the measurement information about the precondition measured when the step that caused the difference is actually executed.
The method for optimizing the simulation conditions according to claim 1 or 2. For each of the steps, a step of calculating a range of setting conditions relating to the operation of a machining machine that executes machining in the step.
The method for optimizing the simulation conditions according to any one of claims 1 to 3, further comprising. The precondition includes at least one of a parameter relating to the performance of a processing machine for performing machining in the process and a parameter relating to a material to be manufactured in the process according to the precondition.
The method for optimizing the simulation conditions according to any one of claims 1 to 4. A step of accumulating the preconditions when the degree of agreement exceeds a predetermined threshold, and
A step of calculating the optimum value of the precondition based on the accumulated precondition, and
The method for optimizing the simulation conditions according to any one of claims 1 to 5, further comprising. A manufacturing process simulation device that simulates a manufacturing process that includes multiple processes.
A calculation unit that calculates a first final result, which is a simulation result of a final product obtained by executing all the steps of the manufacturing process.
An acquisition unit that acquires a second final result, which is result information of a final product obtained by actually executing all the steps of the manufacturing process.
An evaluation unit that evaluates the degree of agreement between the first final result and the second final result,
When the degree of coincidence is equal to or less than a predetermined threshold value, it has a specific unit for specifying the step that caused the difference in the degree of coincidence.
The calculation unit
The prerequisites for the simulation in the process that caused the difference were changed.
A simulation is executed for the steps after the step in which the difference is generated by applying the changed preconditions, and the degree of agreement between the newly calculated first final result and the second final result is re-matched. Evaluate and
The first final result is repeatedly calculated while changing the preconditions until the degree of agreement exceeds a predetermined threshold value.
Manufacturing process simulation device. The manufacturing process simulation apparatus according to claim 7,
A processing machine that executes the processing of the process included in the manufacturing process, and
Including
The manufacturing process simulation device acquires manufacturing result information indicating the result of machining executed by the processing machine, and performs a manufacturing process simulation.
Manufacturing process simulation system. A computer program that simulates a manufacturing process that includes multiple processes.
A step of calculating a first final result, which is a simulation result of a final product obtained by executing all the steps of the manufacturing process, and a step of calculating the first final result.
A step in which the computer acquires a second final result, which is result information of a final product obtained by actually executing all the steps of the manufacturing process.
A step of evaluating the degree of agreement between the first final result and the second final result, and
When the degree of coincidence is equal to or less than a predetermined threshold value, the step of specifying the step that caused the difference in the degree of coincidence and the step of identifying the step.
The step of changing the simulation preconditions in the process that caused the difference, and
A simulation is executed for the steps after the step in which the difference is generated by applying the changed preconditions, and the degree of agreement between the newly calculated first final result and the second final result is re-matched. Steps to evaluate and
Let the computer run
The first final result is repeatedly calculated while changing the preconditions until the degree of agreement exceeds a predetermined threshold.
program.

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