A form error measurement sampling strategy optimization method, device, equipment and medium

Abstract

The application discloses a form and position error measurement sampling strategy optimization method and device, equipment and medium, relates to the technical field of part detection, and is used for solving the technical problem that the prior art cannot improve measurement efficiency under the premise of ensuring measurement accuracy. The method comprises the following steps: preparing optimization simulation according to a to-be-measured part mathematical model, design requirements and historical measurement data of to-be-measured part feature sizes; performing form and position error simulation on the to-be-measured part according to measurement equipment error uncertainty, repeatability error uncertainty and environmental error uncertainty for various point arrangement modes; determining the optimal sampling point number of various point arrangement modes according to N form and position error values of various point arrangement modes; and taking the point arrangement mode with the least sampling point number as the optimal arrangement mode. Therefore, the application can obtain the optimal sampling point number and distribution through "measurement simulation optimization", so that the number of sampling points is kept to be the least under the premise of ensuring measurement reliability.

Description

Technical Field

[0001] This application relates to the field of component inspection technology, and provides a method, device, equipment and medium for optimizing the sampling strategy for form and position error measurement. Background Technology

[0002] Currently, the point-based determination method is commonly used when measuring form and position errors. This method mainly refers to the aviation standard HB20478 and is supplemented by human experience. While aviation standard HB20478 provides a recommended lower limit for the number of sampling points for a given type of feature, it does not consider the size, design precision of the object being measured, or the accuracy of the measuring instrument. Human experience is insufficient to determine the optimal number of sampling points. A large number of sampling points, while reflecting the true error information of the object being measured, inevitably increases measurement costs and reduces efficiency. Conversely, a small number of sampling points, while improving efficiency, does not accurately reflect the information of the measured part, resulting in inaccurate measurement results.

[0003] As shown in patent CN117592313A, it discloses an uncertainty simulation optimization algorithm for coaxiality measurement, which optimizes the measurement point by introducing roundness error; however, it has the following problems: (1) The algorithm is only for coaxiality and cannot be applied to other types of form and position errors; (2) The algorithm only considers the roundness error of the feature to be measured itself, without considering other factors such as measurement equipment error and environmental error, and the roundness error distribution used is difficult to express accurately using a mathematical model, so the simulation results are inaccurate.

[0004] Therefore, how to improve measurement efficiency while ensuring measurement accuracy has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for optimizing the sampling strategy of form and position error measurement, which solves the technical problem that the existing technology cannot improve measurement efficiency while ensuring measurement accuracy.

[0006] On the one hand, a method for optimizing the sampling strategy for form and position error measurement is provided, the method comprising: Based on the mathematical model of the part to be tested, design requirements, and historical measurement data of the feature dimensions of the part to be tested, the initial datum plane, the initial surface of the area to be tested, the tolerance size, and the error distribution of the feature dimensions of the part to be tested are determined. For any given point arrangement, based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, a form and position error simulation is performed on the part under test to obtain N form and position error values ​​corresponding to the given point arrangement; where N is the number of simulations; the point arrangement includes random distribution, gridded distribution, and layered distribution; The measurement uncertainty of the form and position error is determined based on N form and position error values ​​and a preset uncertainty formula; Based on the measurement uncertainty of the form and position error, the sampling strategy of the part to be measured is optimized to determine the optimal number of sampling points for any of the point layout methods; Based on the optimal number of sampling points corresponding to different point layout methods, the point layout method with the fewest sampling points is taken as the optimal layout method.

[0007] Optionally, the step of determining the initial datum surface, the initial surface to be measured, the tolerance size, and the error distribution of the feature dimensions of the part to be measured based on the mathematical model of the part to be measured, design requirements, and historical measurement data of the feature dimensions of the part to be measured includes: Feature extraction is performed on the mathematical model of the part to be tested to obtain the features of the reference surface and the surface features of the area to be tested. Based on the characteristics of the reference surface and the surface characteristics of the region to be measured, the initial reference surface and the initial surface of the region to be measured are established respectively. Based on the design requirements, determine the type of form and position error, the size of the tolerance, and the number of simulations for the sampling strategy; Based on historical measurement data of the feature dimensions of the part to be measured, the error distribution of the feature dimensions of the part to be measured is obtained.

[0008] Optionally, the step of performing form and position error simulation on the part under test based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, to obtain N form and position error values ​​corresponding to any one of the point arrangement methods, includes: For any simulation cycle, using any of the point arrangement methods, m initial feature points are jointly generated on the surfaces of the offset reference plane and the offset test area surface; where m is a positive integer. The first error value is generated based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty. Based on the first error value, the m initial feature points are transformed into rectangular coordinates to obtain the coordinates of the m simulation points in the rectangular coordinate system; Using a pre-defined national standard method, the coordinates of the m simulation points are calculated to obtain the form and position error value corresponding to any simulation cycle.

[0009] Optionally, the step of generating m initial feature points on the surfaces of the offset reference plane and the offset area to be measured using any of the point arrangement methods includes: A second error value is randomly generated using a preset random sampling method and the aforementioned error distribution; Based on the mathematical model of the part to be tested, the second error value is added as an offset to the feature dimension of the part to be tested to obtain the mathematical offset model of the part to be tested. Based on the mathematical offset model of the part to be measured, the offset reference plane and the offset measurement area surface are established respectively. Using any of the point arrangement methods, m initial feature points are generated on the surfaces of the offset reference plane and the offset area to be measured.

[0010] Optionally, the step of generating a first error value based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty includes: The combined measurement uncertainty is calculated based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty. Using the combined measurement uncertainty as the variance, an error normal distribution is constructed. A first error value is randomly generated using a preset random sampling method and the error normal distribution.

[0011] Optionally, the step of optimizing the sampling strategy for the part to be measured based on the measurement uncertainty of the form and position error, and determining the optimal number of sampling points for any given point arrangement, includes: Based on the tolerance size and the characteristic dimensions of the part to be measured, determine the ratio of the tolerance zone grade to the measurement uncertainty of the form and position error; The uncertainty threshold is determined based on the size and proportion of the tolerance; Based on the measurement uncertainty and uncertainty threshold of the shape and position error, the optimal number of sampling points for any of the point layout methods is determined.

[0012] Optionally, the step of determining the optimal number of sampling points for any given point arrangement based on the measurement uncertainty and uncertainty threshold of the form and position error includes: Determine whether the measurement uncertainty of the form and position error at m simulation points is greater than the uncertainty threshold; If the target measurement uncertainty of m simulation points is determined to be greater than the uncertainty threshold, then m is increased by 1; If it is determined that the target measurement uncertainty of m simulation points is not greater than the uncertainty threshold, then m is reduced by 1, and it is determined whether the form and position error measurement uncertainty of m-1 simulation points is greater than the uncertainty threshold. If it is determined that the measurement uncertainty of the form and position error of m-1 simulation points is not greater than the uncertainty threshold, then m is reduced by 2; If the measurement uncertainty of the form and position error of m-1 simulation points is greater than the uncertainty threshold, then the m simulation points are determined as the optimal number of sampling points for any of the point arrangement methods.

[0013] On the one hand, a form and position error measurement sampling strategy optimization device is provided, the device comprising: The optimized simulation preparation unit is used to determine the initial datum plane, the initial surface of the test area, the tolerance size, and the error distribution of the characteristic dimensions of the test part based on the mathematical model of the test part, design requirements, and historical measurement data of the characteristic dimensions of the test part. The form and position error simulation unit is used to perform form and position error simulation on the part under test for any point arrangement, based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, to obtain N form and position error values ​​corresponding to the given point arrangement; wherein, N is the number of simulations; the point arrangement includes random distribution, gridded distribution, and layered distribution; The sampling strategy optimization unit is used to determine the measurement uncertainty of the form and position error based on N form and position error values ​​and a preset uncertainty formula. The sampling strategy optimization unit is also used to optimize the sampling strategy of the part to be measured based on the measurement uncertainty of the form and position error, and determine the optimal number of sampling points for any point arrangement. The sampling strategy optimization unit is also used to select the optimal sampling arrangement method based on the optimal number of sampling points corresponding to different point arrangement methods.

[0014] On one hand, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0015] On the one hand, a storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement any of the methods described above.

[0016] Compared with the prior art, the beneficial effects of this application are as follows: In this application, when optimizing the sampling strategy for form and position error measurement, firstly, the initial reference surface, the initial surface to be measured, the tolerance size, and the error distribution of the feature size of the part to be measured can be determined based on the mathematical model of the part to be measured, design requirements, and historical measurement data of the feature size of the part to be measured. Then, for any point arrangement, the form and position error of the part to be measured can be simulated based on the first error value generated by the uncertainty of the measurement equipment error, the uncertainty of repeatability error, and the uncertainty of environmental error, to obtain N form and position error values ​​corresponding to the point arrangement. Here, N is the number of simulations. The point arrangement includes random distribution, gridded distribution, and layered distribution. Next, the form and position error measurement uncertainty can be determined based on the N form and position error values ​​and a preset uncertainty formula. Then, the sampling strategy of the part to be measured can be optimized based on the form and position error measurement uncertainty to determine the optimal number of sampling points for the point arrangement. Finally, based on the optimal number of sampling points corresponding to different point arrangements, the point arrangement with the fewest sampling points can be selected as the optimal arrangement.

[0017] Based on this, in this application, since the form and position error simulation of the part to be measured is performed based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, compared with the prior art, this application can improve the accuracy of the form and position error simulation, making the simulation closer to the real measurement scenario, thereby greatly improving the accuracy of the actual measurement of form and position errors. In addition, since the point arrangement with the fewest sampling points is taken as the optimal arrangement, compared with the prior art, the sampling strategy obtained by this application can ensure that the measurement accuracy meets the requirements while minimizing the number of sampling points, thereby improving the measurement efficiency while ensuring quality control. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application; Figure 2 A flowchart illustrating a method for optimizing a form and position error measurement sampling strategy provided in an embodiment of this application; Figure 3 This is a schematic flowchart illustrating the calculation of geometrical errors provided in an embodiment of this application. Figure 4A flowchart illustrating the determination of the optimal number of sampling points provided in this application embodiment; Figure 5 This is a schematic diagram of a form and position error measurement sampling strategy optimization device provided in an embodiment of this application.

[0020] The diagram is labeled as follows: 10-Form and position error measurement sampling strategy optimization device, 101-processor, 102-memory, 103-I / O interface, 104-database, 50-Form and position error measurement sampling strategy optimization device, 501-optimization simulation preparation unit, 502-form and position error simulation unit, 503-sampling strategy optimization unit. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0022] Currently, the point-based determination method is commonly used when measuring form and position errors. This method mainly refers to the aviation standard HB20478 and is supplemented by human experience. While aviation standard HB20478 provides a recommended lower limit for the number of sampling points for a given type of feature, it does not consider the size, design precision of the object being measured, or the accuracy of the measuring instrument. Human experience is insufficient to determine the optimal number of sampling points. A large number of sampling points, while reflecting the true error information of the object being measured, inevitably increases measurement costs and reduces efficiency. Conversely, a small number of sampling points, while improving efficiency, does not accurately reflect the information of the measured part, resulting in inaccurate measurement results.

[0023] As shown in patent CN117592313A, it discloses an uncertainty simulation optimization algorithm for coaxiality measurement, which optimizes the measurement point by introducing roundness error; however, it has the following problems: (1) The algorithm is only for coaxiality and cannot be applied to other types of form and position errors; (2) The algorithm only considers the roundness error of the feature to be measured itself, without considering other factors such as measurement equipment error and environmental error, and the roundness error distribution used is difficult to express accurately using a mathematical model, so the simulation results are inaccurate.

[0024] Based on this, this application provides a method for optimizing the sampling strategy of form and position error measurement. In this method, firstly, based on the mathematical model of the part to be measured, design requirements, and historical measurement data of the feature dimensions of the part to be measured, the initial reference surface, the initial surface of the area to be measured, the tolerance size, and the error distribution of the feature dimensions of the part to be measured are determined. Then, for any point arrangement, based on the first error value generated by the uncertainty of the measuring equipment error, the uncertainty of the repeatability error, and the uncertainty of the environmental error, form and position error simulation is performed on the part to be measured to obtain N form and position error values ​​corresponding to the given point arrangement; where N is the number of simulations; the point arrangement includes random distribution, gridded distribution, and layered distribution. Next, the form and position error measurement uncertainty is determined based on the N form and position error values ​​and a preset uncertainty formula. Then, based on the form and position error measurement uncertainty, the sampling strategy for the part to be measured is optimized to determine the optimal number of sampling points for the given point arrangement. Finally, based on the optimal number of sampling points corresponding to different point arrangements, the point arrangement with the fewest sampling points is selected as the optimal arrangement. Based on this, in this application, since the form and position error simulation of the part to be measured is performed based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, compared with the prior art, this application can improve the accuracy of the form and position error simulation, making the simulation closer to the real measurement scenario, thereby greatly improving the accuracy of the actual measurement of form and position errors. In addition, since the point arrangement with the fewest sampling points is taken as the optimal arrangement, compared with the prior art, the sampling strategy obtained by this application can ensure that the measurement accuracy meets the requirements while minimizing the number of sampling points, thereby improving the measurement efficiency while ensuring quality control.

[0025] After introducing the design concept of the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application can be applied. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0026] like Figure 1 The diagram shown illustrates an application scenario provided by an embodiment of this application. This application scenario may include a form and position error measurement sampling strategy optimization device 10.

[0027] The form and position error measurement sampling strategy optimization device 10 can be used to optimize the sampling strategy for form and position error measurement. For example, it can be a personal computer (PC), server, or laptop. The form and position error measurement sampling strategy optimization device 10 may include one or more processors 101, memory 102, I / O interfaces 103, and databases 104. Specifically, the processor 101 can be a central processing unit (CPU) or a digital processing unit, etc. The memory 102 can be volatile memory, such as random-access memory (RAM); the memory 102 can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or the memory 102 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory 102 can be a combination of the above-mentioned memories. The memory 102 can store some program instructions of the form and position error measurement sampling strategy optimization method provided in the embodiments of this application. When these program instructions are executed by the processor 101, they can be used to implement the steps of the form and position error measurement sampling strategy optimization method provided in the embodiments of this application, so as to solve the technical problem that the prior art cannot improve the measurement efficiency while ensuring measurement accuracy. The database 104 can be used to store data such as the mathematical model of the part to be measured, design requirements, and historical measurement data of the feature dimensions of the part to be measured involved in the solution provided in the embodiments of this application.

[0028] In this embodiment, the form and position error measurement sampling strategy optimization device 10 can obtain strategy optimization instructions through the I / O interface 103. Then, the processor 101 of the form and position error measurement sampling strategy optimization device 10 will solve the technical problems such as the inability of the prior art to improve measurement efficiency while ensuring measurement accuracy, according to the program instructions of the form and position error measurement sampling strategy optimization method provided in this embodiment in the memory 102. In addition, the mathematical model of the part to be measured, design requirements, and historical measurement data of the feature dimensions of the part to be measured can also be stored in the database 104.

[0029] Of course, the methods provided in the embodiments of this application are not limited to... Figure 1 The application scenarios shown can also be used in other possible scenarios, and this application embodiment does not impose any limitations. Figure 1The functions that the various devices in the application scenarios shown can achieve will be described in subsequent method embodiments, and will not be elaborated on here. Below, the methods of the embodiments of this application will be described in conjunction with the accompanying drawings.

[0030] like Figure 2 The diagram shown is a flowchart illustrating a method for optimizing a form and position error measurement sampling strategy according to an embodiment of this application. This method can... Figure 1 The form and position error measurement sampling strategy optimization device 10 is used to execute the method. Specifically, the process of this method is described as follows.

[0031] Step 201: Based on the mathematical model of the part to be tested, design requirements, and historical measurement data of the feature dimensions of the part to be tested, determine the initial datum surface, the initial surface of the area to be tested, the tolerance size, and the error distribution of the feature dimensions of the part to be tested.

[0032] In this application, before performing optimization simulation, "optimization simulation preparation" can be carried out first. Specifically, firstly, feature extraction can be performed on the mathematical model of the part to be tested (e.g., the CAD model of the part to be tested) to obtain the datum surface features and the surface features of the area to be tested; then, based on the datum surface features and the surface features of the area to be tested, initial datum surfaces and initial surfaces of the area to be tested can be established respectively for the extraction points; next, the type of form and position error and the tolerance size can be determined according to the design requirements. u The number of simulations for the sampling strategy is N; where N is a positive integer greater than 10000; finally, the error distribution of the feature dimensions of the part under test can be obtained based on the historical measurement data of the feature dimensions of the part under test.

[0033] Step 202: For any point arrangement, based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty and environmental error uncertainty, perform form and position error simulation on the part to be measured to obtain N form and position error values ​​corresponding to any point arrangement.

[0034] The point arrangement methods include random distribution, gridded distribution, and hierarchical distribution.

[0035] Specifically, for the i-th simulation cycle (i=1,2,3,……,N), such as Figure 3 The diagram shown is a flowchart of a method for calculating the form and position error value provided in an embodiment of this application. First, "initial point generation" can be performed, that is, any point arrangement method can be used to generate m initial feature points on the surfaces of the offset reference plane and the offset area to be measured; where m is a positive integer.

[0036] Then, "form and position error calculation" can be performed, that is, the measurement equipment error uncertainty caused by the measurement instrument indication error can be calculated.u 1. Uncertainty of repeatability error caused by repeatability error u 2. Environmental error uncertainty caused by the measurement environment u 3. Generate the first error value b .

[0037] Next, based on the first error value... b The m initial feature points are transformed into Cartesian coordinates to obtain the coordinates of the m simulation points in a Cartesian coordinate system. Specifically, an angle value α can be randomly generated within the interval [0, 360], and an angle value β can be randomly generated within the interval [0, 180]. Assuming the coordinates of an initial feature point in the Cartesian coordinate system are (x, y, z), the coordinates of the simulation point in the Cartesian coordinate system can be calculated using the following formula. :

[0038] Then, the coordinate transformation process described above can be repeated until all m initial feature points have been incorporated into the first error value. b Convert to simulation point coordinates.

[0039] Next, a preset national standard method (e.g., GB / T 1958-2004 "Technical Specification for Geometric Measurement of Products (GPS) - Regulations for the Inspection of Shape and Position Tolerances") can be used to calculate the coordinates of the m simulation points to obtain the form and position error value corresponding to any simulation cycle.

[0040] Finally, a "repeated measurement simulation" can be performed, that is, the process of "initial point generation" and "form and position error calculation" is repeated N times to obtain N form and position error values. , where i = 1, 2, ..., N.

[0041] In one possible implementation, when generating m initial feature points on the surfaces of the offset reference plane and the offset surface of the area to be measured using any of the aforementioned point arrangement methods, firstly, a second error value can be randomly generated using a preset random sampling method (e.g., Monte Carlo algorithm) and the error distribution of the feature dimensions of the part to be measured. a Then, based on the mathematical model of the part to be tested, the second error value can be... a The offset is added to the feature dimension of the part to be tested to obtain the mathematical offset model of the part to be tested. Next, the offset reference plane and the offset test area surface can be established according to the mathematical offset model of the part to be tested. Finally, m initial feature points can be generated on the surfaces of the offset reference plane and the offset test area surface by any of the point arrangement methods.

[0042] In one possible implementation, when generating the first error value based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, firstly, the measurement equipment error uncertainty can be used as a basis for determining the first error value. u 1. Repeatability error uncertainty u 2. Environmental error uncertainty u 3. Calculate the combined measurement uncertainty. u 4; wherein, the combined measurement uncertainty u The formula for calculating 4 is as follows:

[0043] Then, the combined measurement uncertainty can be used as the variance to construct an error normal distribution (0, u 4). Finally, a pre-defined random sampling method (e.g., Monte Carlo algorithm) can be used in conjunction with the error normal distribution (0, u 4) Randomly generate the first error value. b In this application, since the error follows a normal distribution (0, ... u 4) The shape is spherical when mapped to a rectangular coordinate system, therefore, a subsequent transformation to rectangular coordinates is required.

[0044] Step 203: Determine the measurement uncertainty of the form and position error based on the N form and position error values ​​and the preset uncertainty formula.

[0045] In this application, the preset uncertainty formula is as follows:

[0046] in, For the measurement uncertainty of form and position error; There are i geometrical errors; This represents the mean of the geometrical errors.

[0047] Step 204: Based on the measurement uncertainty of form and position error, optimize the sampling strategy for the part to be measured and determine the optimal number of sampling points for any point arrangement.

[0048] Specifically, firstly, it can be based on the size of the tolerance. u Determine the ratio of the tolerance zone grade to the measurement uncertainty of the form and position error based on the characteristic dimensions of the part to be measured. d .

[0049] Then, based on the tolerance size... u With proportion d Determine the uncertainty threshold d × u .

[0050] Next, the uncertainty u of the form and position error can be measured. δ With uncertainty threshold d × u The optimal number of sampling points for any of the point layout methods is determined.

[0051] In one possible implementation, when determining the optimal number of sampling points for any given point arrangement based on the measurement uncertainty and uncertainty threshold of the form and position error, such as... Figure 4 The diagram shown is a flowchart illustrating a method for determining the optimal number of sampling points according to an embodiment of this application.

[0052] First, the measurement uncertainty u of the form and position error can be determined using m simulation points. δ Is it greater than the uncertainty threshold d×u?

[0053] Next, if the target measurement uncertainty at m simulation points is determined to be greater than the uncertainty threshold (i.e., If the result is positive, it proves that m simulation points are insufficient to meet the measurement accuracy requirements and more simulation points are needed to improve the measurement accuracy. Therefore, m can be increased by 1 to enter the next cycle and determine whether the measurement uncertainty of the form and position error of m+1 simulation points is greater than the uncertainty threshold.

[0054] Conversely, if the target measurement uncertainty of m simulation points is determined to be no greater than the uncertainty threshold (i.e., If the m simulation points can meet the measurement accuracy requirements, it is necessary to further reduce the number of sampling points to improve the measurement efficiency. Therefore, m can be reduced by 1 to enter the next cycle and determine whether the measurement uncertainty of the form and position error of m-1 simulation points is greater than the uncertainty threshold.

[0055] If the measurement uncertainty of the form and position error of m-1 simulation points is determined to be no greater than the uncertainty threshold, it proves that m-1 simulation points can meet the measurement accuracy requirements. It is necessary to further reduce the number of sampling points to improve the measurement efficiency. Therefore, m can be reduced by 2.

[0056] Conversely, if the measurement uncertainty of the form and position error of m-1 simulation points is greater than the uncertainty threshold, it proves that m-1 simulation points cannot meet the measurement accuracy requirements. Therefore, the number of simulation points cannot be reduced further. The number of simulation points has reached the minimum value that can meet the measurement accuracy requirements. At this time, m simulation points can be directly determined as the optimal number of sampling points for any point arrangement.

[0057] Step 205: Based on the optimal number of sampling points corresponding to different point layout methods, the point layout method with the fewest sampling points is taken as the optimal layout method.

[0058] In summary, this application has the following advantages: (1) Since the form and position error simulation of the part to be measured is based on the measurement equipment error uncertainty, repeatability error uncertainty and environmental error uncertainty, this application can greatly improve the accuracy of the actual measurement of form and position error by improving the accuracy of the form and position error simulation and making the simulation closer to the real measurement scenario.

[0059] (2) Since the optimal arrangement is the point layout with the fewest sampling points, the sampling strategy obtained in this application ensures that the measurement accuracy meets the requirements and minimizes the number of sampling points, thus improving the measurement efficiency while ensuring quality control.

[0060] Based on the same inventive concept, embodiments of this application provide a form and position error measurement sampling strategy optimization device 50, such as... Figure 5 As shown, the form and position error measurement sampling strategy optimization device 50 includes: The optimized simulation preparation unit 501 is used to determine the initial reference surface, the initial surface of the area to be measured, the tolerance size, and the error distribution of the feature size of the part to be measured based on the mathematical model of the part to be measured, the design requirements, and the historical measurement data of the feature size of the part to be measured. The form and position error simulation unit 502 is used to perform form and position error simulation on the part under test for any point arrangement method, based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty and environmental error uncertainty, to obtain N form and position error values ​​corresponding to any point arrangement method; where N is the number of simulations; the point arrangement method includes random distribution, gridded distribution and layered distribution; The sampling strategy optimization unit 503 is used to determine the measurement uncertainty of the form and position error based on N form and position error values ​​and a preset uncertainty formula; The sampling strategy optimization unit 503 is also used to optimize the sampling strategy of the part to be measured based on the measurement uncertainty of the form and position error, and determine the optimal number of sampling points for any point arrangement. The sampling strategy optimization unit 503 is also used to select the point arrangement with the fewest sampling points as the optimal arrangement based on the optimal number of sampling points corresponding to different point arrangement methods.

[0061] Optionally, the optimized simulation preparation unit 501 is also used for: Feature extraction is performed on the mathematical model of the part to be tested to obtain the features of the reference surface and the surface features of the area to be tested. Based on the characteristics of the reference surface and the surface characteristics of the region to be measured, the initial reference surface and the initial surface of the region to be measured are established respectively. Based on the design requirements, determine the type of form and position error, the size of the tolerance, and the number of simulations for the sampling strategy; Based on historical measurement data of the feature dimensions of the part to be measured, the error distribution of the feature dimensions of the part to be measured is obtained.

[0062] Optionally, the form and position error simulation unit 502 is also used for: For any simulation cycle, using any point arrangement, m initial feature points are generated on the surfaces of the offset reference plane and the offset test area surface; where m is a positive integer. The first error value is generated based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty. Based on the first error value, perform a Cartesian coordinate transformation on the m initial feature points to obtain the coordinates of the m simulation points in the Cartesian coordinate system; Using a pre-defined national standard method, the coordinates of m simulation points are calculated to obtain the form and position error value corresponding to any simulation cycle.

[0063] Optionally, the form and position error simulation unit 502 is also used for: A second error value is randomly generated using a preset random sampling method and error distribution. Based on the mathematical model of the part to be tested, the second error value is added as an offset to the feature dimension of the part to be tested to obtain the mathematical offset model of the part to be tested. Based on the mathematical offset model of the part to be measured, the offset reference plane and the offset measurement area surface are established respectively. Using any point arrangement method, m initial feature points are generated on the surfaces of the offset reference plane and the offset area to be measured.

[0064] Optionally, the form and position error simulation unit 502 is also used for: The combined measurement uncertainty is calculated based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty. Using the combined measurement uncertainty as the variance, a normal distribution of the error is constructed. The first error value is randomly generated using a preset random sampling method and an error normal distribution.

[0065] Optionally, the sampling strategy optimization unit 503 is also used for: Based on the tolerance size and the characteristic dimensions of the part to be measured, determine the ratio of the tolerance zone grade to the measurement uncertainty of the form and position error; The uncertainty threshold is determined based on the size and proportion of the tolerance; Based on the measurement uncertainty and uncertainty threshold of the form and position error, the optimal number of sampling points for any point arrangement method is determined.

[0066] Optionally, the sampling strategy optimization unit 503 is also used for: Determine whether the measurement uncertainty of the form and position error at m simulation points is greater than the uncertainty threshold; If the target measurement uncertainty of m simulation points is determined to be greater than the uncertainty threshold, then m is increased by 1; If it is determined that the target measurement uncertainty of m simulation points is not greater than the uncertainty threshold, then m is reduced by 1, and it is determined whether the form and position error measurement uncertainty of m-1 simulation points is greater than the uncertainty threshold. If it is determined that the measurement uncertainty of the form and position error of m-1 simulation points is not greater than the uncertainty threshold, then m is reduced by 2; If the measurement uncertainty of the form and position error of m-1 simulation points is greater than the uncertainty threshold, then the m simulation points are determined as the optimal number of sampling points for any point arrangement.

[0067] The form and position error measurement sampling strategy optimization device 50 can be used to execute... Figures 2-4 The method performed in the illustrated embodiment is described above. Therefore, the functions that each functional module of the form and position error measurement sampling strategy optimization device 50 can achieve can be referred to. Figures 2-4 The embodiments shown are described in detail below.

[0068] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of the methods according to the various exemplary embodiments of this application described above. For example, the computer device may perform actions such as... Figures 2-4 The method performed in the illustrated embodiment.

[0069] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0070] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing a sampling strategy for form and position error measurement, characterized in that, The method includes: Based on the mathematical model of the part to be tested, design requirements, and historical measurement data of the feature dimensions of the part to be tested, the initial datum plane, the initial surface of the area to be tested, the tolerance size, and the error distribution of the feature dimensions of the part to be tested are determined. For any given point arrangement, based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, a form and position error simulation is performed on the part under test to obtain N form and position error values ​​corresponding to the given point arrangement; where N is the number of simulations; the point arrangement includes random distribution, gridded distribution, and layered distribution; The measurement uncertainty of the form and position error is determined based on N form and position error values ​​and a preset uncertainty formula; Based on the measurement uncertainty of the form and position error, the sampling strategy of the part to be measured is optimized to determine the optimal number of sampling points for any of the point layout methods; Based on the optimal number of sampling points corresponding to different point layout methods, the point layout method with the fewest sampling points is taken as the optimal layout method.

2. The method as described in claim 1, characterized in that, The steps of determining the initial datum plane, the initial surface to be measured, the tolerance size, and the error distribution of the feature dimensions of the part to be measured based on the mathematical model of the part to be measured, design requirements, and historical measurement data of the feature dimensions of the part to be measured include: Feature extraction is performed on the mathematical model of the part to be tested to obtain the features of the reference surface and the surface features of the area to be tested. Based on the characteristics of the reference surface and the surface characteristics of the region to be measured, the initial reference surface and the initial surface of the region to be measured are established respectively. Based on the design requirements, determine the type of form and position error, the size of the tolerance, and the number of simulations for the sampling strategy; Based on historical measurement data of the feature dimensions of the part to be measured, the error distribution of the feature dimensions of the part to be measured is obtained.

3. The method as described in claim 1, characterized in that, The step of performing form and position error simulation on the part under test based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, and obtaining N form and position error values ​​corresponding to any one of the point arrangement methods, includes: For any simulation cycle, using any of the point arrangement methods, m initial feature points are jointly generated on the surfaces of the offset reference plane and the offset test area surface; where m is a positive integer. The first error value is generated based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty. Based on the first error value, the m initial feature points are transformed into rectangular coordinates to obtain the coordinates of the m simulation points in the rectangular coordinate system; Using a pre-defined national standard method, the coordinates of the m simulation points are calculated to obtain the form and position error value corresponding to any simulation cycle.

4. The method as described in claim 3, characterized in that, The step of generating m initial feature points on the surfaces of the offset reference plane and the offset measured area surface using any of the point arrangement methods includes: A second error value is randomly generated using a preset random sampling method and the aforementioned error distribution; Based on the mathematical model of the part to be tested, the second error value is added as an offset to the feature dimension of the part to be tested to obtain the mathematical offset model of the part to be tested. Based on the mathematical offset model of the part to be measured, the offset reference plane and the offset measurement area surface are established respectively. Using any of the point arrangement methods, m initial feature points are generated on the surfaces of the offset reference plane and the offset area to be measured.

5. The method as described in claim 3, characterized in that, The step of generating a first error value based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty includes: The combined measurement uncertainty is calculated based on the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty. Using the combined measurement uncertainty as the variance, an error normal distribution is constructed. A first error value is randomly generated using a preset random sampling method and the error normal distribution.

6. The method as described in claim 1, characterized in that, The step of optimizing the sampling strategy for the part under test based on the measurement uncertainty of the form and position error, and determining the optimal number of sampling points for any given point arrangement, includes: Based on the tolerance size and the characteristic dimensions of the part to be measured, determine the ratio of the tolerance zone grade to the measurement uncertainty of the form and position error; The uncertainty threshold is determined based on the size and proportion of the tolerance; Based on the measurement uncertainty and uncertainty threshold of the shape and position error, the optimal number of sampling points for any of the point layout methods is determined.

7. The method as described in claim 6, characterized in that, The step of determining the optimal number of sampling points for any given point arrangement based on the measurement uncertainty and uncertainty threshold of the form and position error includes: Determine whether the measurement uncertainty of the form and position error at m simulation points is greater than the uncertainty threshold; If the target measurement uncertainty at m simulation points is determined to be greater than the uncertainty threshold, then increase m by 1; If it is determined that the target measurement uncertainty of m simulation points is not greater than the uncertainty threshold, then m is reduced by 1, and it is determined whether the form and position error measurement uncertainty of m-1 simulation points is greater than the uncertainty threshold. If it is determined that the measurement uncertainty of the form and position error of m-1 simulation points is not greater than the uncertainty threshold, then m is reduced by 2; If the measurement uncertainty of the form and position error of m-1 simulation points is greater than the uncertainty threshold, then the m simulation points are determined as the optimal number of sampling points for any of the point arrangement methods.

8. A device for optimizing a sampling strategy for form and position error measurement, characterized in that, The device includes: The optimized simulation preparation unit is used to determine the initial datum plane, the initial surface of the test area, the tolerance size, and the error distribution of the characteristic dimensions of the test part based on the mathematical model of the test part, design requirements, and historical measurement data of the characteristic dimensions of the test part. The form and position error simulation unit is used to perform form and position error simulation on the part under test for any point arrangement, based on the first error value generated by the measurement equipment error uncertainty, repeatability error uncertainty, and environmental error uncertainty, to obtain N form and position error values ​​corresponding to the given point arrangement; wherein, N is the number of simulations; the point arrangement includes random distribution, gridded distribution, and layered distribution; The sampling strategy optimization unit is used to determine the measurement uncertainty of the form and position error based on N form and position error values ​​and a preset uncertainty formula. The sampling strategy optimization unit is also used to optimize the sampling strategy of the part to be measured based on the measurement uncertainty of the form and position error, and determine the optimal number of sampling points for any point arrangement. The sampling strategy optimization unit is also used to select the optimal sampling arrangement method based on the optimal number of sampling points corresponding to different point arrangement methods.

9. An electronic device, characterized in that, The device includes: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method described in any one of claims 1-7 according to the obtained program instructions.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions for causing a computer to perform the method described in any one of claims 1-7.

Images