Method for determining the variation of repeated occurrences
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- ROBERT BOSCH GMBH
- Filing Date
- 2023-02-02
- Publication Date
- 2026-07-16
AI Technical Summary
Conventional methods for evaluating measurement processes are impractical when dealing with a very small number of test subjects, limiting their applicability to early stages of development and small series production.
A method for determining repeatability variability by varying measurement parameters within a defined range and analyzing the resulting distributions to evaluate measurement processes, even with a single or small number of test subjects, using statistical methods to assess standard deviation and variability.
Enables cost-effective evaluation of measurement processes early in development, reducing resource requirements and identifying potential improvements, thus saving costs and time in setup and preparation.
Smart Images
Figure 0007891539000001 
Figure 0007891539000002
Abstract
Description
[Technical Field]
[0001] The present invention relates to a method for determining repeating variability. The present invention also relates to a computer program for performing the aforementioned method, a machine-readable recording medium containing such a computer program, and one or more computers containing such computer programs. [Background technology]
[0002] As proof of the capability of the measurement process within the framework of functional testing of the components of a series, statistical methods based on determining the repeatability variability of the tested features are used, in particular. The %GRR method, in particular, has proven highly effective in this regard. Typically, for the evaluation of the measurement process, the repeatability variability included in the measurement of a relatively large number of test subjects is considered. For example, 25 test subjects can be measured at least twice each. For the measurements obtained for each test subject, the range of variability is determined. The ranges of variability obtained for all test subjects form a distribution, and the standard deviation of this distribution is determined. From this standard deviation, the capability of the measurement process is finally derived or evaluated. The measurement process can be deemed competent, for example, if the ratio of the repeatability variability to the tolerance of the feature being tested falls below a predetermined threshold. In many cases, this type of measurement is performed by multiple inspectors, and furthermore, a sufficiently large number of test subjects are required for a statistically usable evaluation. [Overview of the project]
[0003] It was recognized that conventional methods for evaluating test processes are impractical when dealing with a very small number of test subjects. Consequently, conventional methods for evaluating test processes cannot be used to consider measurement processes involving small series of test subjects, or measurement processes in the early stages of development.
[0004] The present invention relates to a method for determining the repeatability of a given measurement process to be performed on at least one test subject. The method, in this case, includes at least the steps described below.
[0005] Repeat variability refers to the variability of measurements when measurements are repeated on the same object, in the same laboratory, and by the same staff (repeatable conditions). Under otherwise identical conditions, the measurement parameters are kept constant during the first measurement process and may vary during the second measurement process. Here, "kept constant" or "variable" may refer to the selection of measurement parameters and / or the values of these parameters. In the following, the selection of measurement parameters is kept constant, but the values of the measurement parameters vary.
[0006] In one step, prepare at least one test subject. In the next step, determine at least one measurement parameter of the measurement process, on which the results of this measurement process depend in addition to the characteristics of the test subject to be measured, which can be explained by the quantity to be measured. In a further step, determine the range of variation for at least one measurement parameter, and based on this, multiple measurements can be performed on the test subject, in which case different values of at least one measurement parameter within each of the determined ranges of variation should be selected, and as a result, one measurement value for each quantity to be measured is obtained during the measurement process.
[0007] Repeated measurements yield multiple measurements, each with a selected measurement parameter, that, as a result, form at least one unique distribution with at least one degree of variability, particularly a variability width. Finally, the desired repeatability variability is evaluated from the degree of variability.
[0008] This can be done, for example, by statistically evaluating the obtained distribution to determine the standard deviation for all the obtained degrees of variability, and then using this standard deviation to subsequently evaluate the desired repeating variability of the measurement process. In this case, the repeating variability may correspond, for example, to the standard deviation for at least one degree of variability, in particular, for all the obtained ranges of variability. The repeating variability may, however, be determined, for example, as the difference between the maximum and minimum ranges of variability obtained.
[0009] It should be noted that the measurement parameters, which are variable in the methods described above and below, remain constant in conventional methods for evaluating the capability of the measurement process.
[0010] Multiple measurement parameters of the measurement process may be determined, depending on the characteristics of the test object to be measured, given by the quantity to be measured. Then, the range of variation for each measurement parameter is determined. Furthermore, multiple measurements can then be performed on the test object for different values of each measurement parameter within each range of variation, resulting in multiple distributions of measured values of the quantity to be considered, each with a degree of variability, particularly a range of variability. Subsequently, the desired repeatability can be evaluated again from the degree of variability of the obtained distributions of measured values.
[0011] The method described above and below makes it possible to evaluate a measurement process, that is, to determine its capability, when only a small number of test subjects are present. Therefore, this method allows us to determine whether a measurement process is adequately suited even for a small number of test subjects leading up to a single test subject. This method is therefore suitable for evaluating a measurement process when there is only one test subject or only a small number of test subjects. The measurement uncertainty of the measurement method under consideration can be investigated, thereby reducing or saving resources in the form of stable components that would otherwise need to be prepared and, in some cases, selected for capability testing. This can result in significant potential savings in terms of cost for a series of components and setup time in the corresponding experimental apparatus.
[0012] A further advantage of the methods described above and below is that capability testing for a measurement process with one or a small number of test subjects can be conducted as early as the corresponding development process, when only a small number of sample test subjects are available. This allows for the demonstration of the measurement process's capability in the early stages of the project. In particular, at such an early stage, possible improvements can be recognized and implemented. This, on the other hand, can lead to significant cost savings compared to later capability testing. In later capability testing processes, the need for substantial changes or improvements to the measurement process may only be recognized after significant costs have already been incurred.
[0013] According to one embodiment, the range of variation for the measurement parameter is determined based on the target value. Additionally, or instead, the range of variation for the measurement parameter may be determined based on a predetermined range of fluctuation for the measured quantity, in relation to a known relationship between the measured quantity and the measurement parameter.
[0014] A known relationship between a measured quantity and a measured parameter may, for example, be a characteristic line representing the dependence of the measured quantity on the measured parameter. For example, a functional relationship between the measured quantity and the measured parameter may be known in the form of an equation from which a characteristic line can be obtained by substituting at least a corresponding value for the measured parameter, and, depending on the circumstances, another parameter characterizing the measurement process and / or the object of test. The functional relationship may also be obtained additionally or exclusively experimentally and stored, for example, in the form of a table of values, from which corresponding representative values for the measured parameter and measured quantity can be extracted, for example. The table of values, or a corresponding graphical indication of the relationship between the measured quantity and the measured parameter, with suitable interpolation depending on the circumstances, may also be stored in digital and / or datasheet form. The target value of the measured quantity and the range of variation of the measured parameter may also be stored in digital or analog form within the datasheet.
[0015] For example, the measurement parameters may be progressively varied within the framework of this method, and the step size of the variation in subsequent steps may be fitted, specifically, to the interval of the measured values, the distribution obtained in the preceding step, and / or the median or mean of this distribution, relative to a predetermined target value of the measured quantity.
[0016] This can be achieved, for example, by considering, on the one hand, the distribution or median or mean of the measured values, and on the other hand, the amount of difference between the measured quantity and the target value. For measured values near the target value, for example, a smaller step size can be selected when the measurement parameter varies, thereby achieving a more accurate consideration of this range of the measured quantity. This allows for reinforced consideration of the range around the target value that is particularly important to the measurement process.
[0017] Furthermore, the step size for varying the measurement parameters may be selected to be smaller within the range around the target value of the measured quantity, and larger near the upper and lower limits of the fluctuation range for the measured quantity. This allows the range of measurements that are further away from the target value of the measured quantity to receive relatively weaker weighting in the capability verification investigation of the measurement process, while the range that is close to the target value of the measured quantity receives relatively stronger weighting in the capability verification.
[0018] In another embodiment, the range of variation for the measurement parameters is determined based on the measurement process, the equipment used for the measurement process, and / or the technical specifications of the object under test. Thus, a reasonable amount of prior knowledge can be used to determine the repeatable variability.
[0019] In another embodiment, the target value of the measured quantity, the range of variation of the measured quantity, the known relationship between the measured quantity and the measurement parameter, and / or the technical specifications are obtained from at least one datasheet. Additionally or alternatively, the aforementioned variables may be obtained from digitally stored datasets of the measurement process, the equipment used for the measurement process, and / or the object under test. In this case, these sources of prior knowledge can also be used for a method to determine the repeated variability.
[0020] According to one embodiment, at least one sample of a component manufactured individually or in a series may be selected as the test subject. Advantageously, this allows a small number of test subjects to be manufactured, for example, only one or two, to be used in the process of the method. This reduces the costs associated with the preparation of test subjects and the implementation of the measurement process. Furthermore, the method can also be used in the evaluation of measurement processes where a small series of components exists. At a minimum, a portion of such a small series can be used as the test subject within the framework of the method, avoiding the need for corresponding preparation of a larger number of test subjects in the small series.
[0021] According to an embodiment of the present invention, a fuel injector for an internal combustion engine can be selected as a test object. Within the framework of evaluating the test process, it is no longer necessary to prepare a larger number of fuel injectors in order to determine the variation in repetition of the corresponding measurement process, as described above and below.
[0022] According to one embodiment, it is possible to determine whether the variation in repetition obtained within the framework of this method meets a predetermined criterion. If this is established, it can be declared that this measurement process is qualified for quality control in serial production.
[0023] For example, the predetermined criterion can be a condition that the ratio of the obtained variation in repetition to a predetermined tolerance does not exceed a predetermined value, or does not fall below a value depending on the setting of conditions. In this case, a version of the %GRR proof of capability adapted to the method described here can be used.
[0024] According to one embodiment, the measured quantity can include the liquid injection quantity, gas supply quantity, output, current, and / or torque.
[0025] According to another embodiment, the measurement parameters can include pressure, injection time, voltage, and / or temperature.
[0026] Furthermore, the present invention relates to a computer program including machine-readable instructions, which cause one or more computers to execute the method according to the present invention when the computer program is executed on one or more computers. The present invention also includes a machine-readable recording medium storing the above computer program, and a computer implementing the above computer program or the above machine-readable recording medium.
[0027] Further measures for improving the present invention will be described in detail below based on the drawings together with the description of the preferred embodiments of the present invention.
Brief Description of the Drawings
[0028] [Figure 1] This figure shows one embodiment of a method 1000 for determining the repeatability variability of a given measurement process to be performed on one test subject 1. [Figure 2] This figure shows another embodiment of Method 1000. [Modes for carrying out the invention]
[0029] As shown in Figure 1, the method includes at least the steps described below. In step 100, at least one test subject 1 is prepared. In this case, test subject 1 may be, for example, a fuel injector. In step 200, at least one measurement parameter 2 of the measurement process is determined, which, in addition to the characteristics to be measured of test subject 1, depends on the results of this measurement process. In this case, the characteristics to be measured of test subject 1 relate to the quantity to be measured 3 that should be considered within the framework of the measurement process. It should be noted that in the conventional method of determining the variability of repeated measurement processes, the aforementioned measurement parameter is selected to remain constant and unaffected by fluctuations. In a further step 300, the variation range 21 for the measurement parameter 2 is determined. Following this, in step 400, multiple measurements are performed on test subject 1, and for this purpose, the measurement parameter 2 is changed within the determined variation range 21, and multiple (at least two) measured values of the quantity to be considered are obtained for corresponding values of the measurement parameter. In step 500, the variability ranges 310a, 311a, and 312a are calculated from the distributions 310, 311, and 312 of the measured values for each measured parameter variation that was performed. In step 600, the desired repeatability variability of the measurement process is finally determined from at least the standard deviation 41 of the individual variability ranges 310a, 311a, and 312a. The relationships between all the variables listed are explained in detail below with reference to Figure 2.
[0030] Figure 2 shows an exemplary implementation of Method 1000 in the serial production of fuel injectors. A fuel injector is prepared as Test Subject 1, in which a measurement process is to be performed and the repeatability of the measurement process is to be determined within that framework. The purpose of Method 1000 is to determine whether the repeatability satisfies a predetermined standard, and if the standard is adequately met, this measurement process can be declared suitable for quality control in serial production.
[0031] Next, we determine measurement parameter 2 of the measurement process. Measurement parameter 2 is a parameter that does not fluctuate in the conventional method of determining repeated variability within the corresponding measurement frame. The difference between the method described here and the conventional method for evaluating the measurement process is, in particular, that in the former case, parameter fluctuations occur within the frame of measurement value generation, whereas in the latter case, numerous test subjects are used in the process of generating corresponding measurement data, and the measurement parameter in this case, however, takes a predetermined fixed value. The results of the measurement process clearly depend, in particular, on the selection of measurement parameter 2. In the example shown in Figure 2, the drive control time t of the fuel injector is selected as measurement parameter 2.
[0032] In the method described here, therefore, the variation range 21 for the measurement parameter 2 is determined, and multiple measurements are performed on the test subject 1 at different values 210, 211, and 212 within the variation range 21 of the measurement parameter 2. Here, the drive control time t is the minimum time t as the lower limit 22 of the variation range 21. min And the maximum time t as the upper limit 23 of the variation range 21. max It fluctuates between [values].
[0033] As shown in Figure 2, the variation range 21 of the measurement parameter 2, which can be represented in particular by the upper limit 23 and the lower limit 22, can be determined based on the target value 31 of the measured quantity 3 and the corresponding deflection range 32 of the measured quantity 3. The upper limit 23 of the variation range 21 of the measurement parameter 2, here, is the maximum drive control time t. maxIn other words, for example, the upper limit 34 of the fluctuation range 32 of the measured quantity 3, where Q is the maximum injection amount. max This can correspond to the lower limit 22 of the variation range 21 of the measurement parameter 2, where the minimum drive control time t min This is the lower limit 33 of the fluctuation range 32 of the measured quantity 3, where the minimum injection quantity Q is min This can be addressed. Alternatively or additionally, a known relationship 5 between the measured quantity 3 and the measured parameter 2, which is valid within the variation range 21 of the measured parameter 2, can also be used. This relationship 5 may be a known characteristic line 5 between the measured quantity 3 and the measured parameter 1, which can be shown in a table or recorded in graph form. The corresponding table, and also the graph representation, of the relationship between the measured quantity 3 and the measured parameter 2 may be obtained, for example, by interpolation using previous measurements on one or more reference test subjects, and can be used within the framework of the method described herein. The corresponding information regarding the characteristic line 5 can be stored in a database or datasheet 6 in digital or analog form and is available in the corresponding method steps.
[0034] Therefore, multiple measurements are performed on test subject 1, and the different values 210, 211, and 212 of measurement parameter 2 within the variation range 21 are examined. For each value 210, 211, and 212 of measurement parameter 2, distributions 310, 311, and 312 of measured values with variability widths 310a, 311a, and 312a are obtained. The points (symbol "·") marked in Figure 2 represent the mean values of distributions 310, 311, and 312, and further similar distributions, respectively. These mean values are scattered around characteristic line 5.
[0035] Within the framework of the measurement process, the measurement parameter 2 can be progressively varied each time. In this case, the step widths 220 and 221 between consecutive measurements can be adapted according to the interval that the distribution 310 (or, for example, the median or mean of this distribution 310) has from the target value 31 of the measured quantity 3. For example, if the distribution 310 or its mean or median in the preceding step has a small interval 320 from a predetermined target value 31 of the measured quantity 3, the step width 221 in the subsequent step can be selected to be smaller than the step width 220 in the preceding step. A small interval can be understood, for example, as a quantitative deviation within a predetermined limit. This makes it possible to generate a larger number of measurements in the vicinity of the target value 31 of the measured quantity 3, where the measured values have a larger interval 320 from the target value 31. Additionally, and in a similar manner, if the distribution 310 or its mean or median in the preceding step has a larger interval 320 from a predetermined target value 31 of the measured quantity 3, then the step size 220 of the subsequent step may be selected to be larger than the step size 221 of the preceding step.
[0036] Furthermore, the variation range 21 of the measurement parameter 2, and in some cases the parameter value to be selected, can be determined by the technical specifications of the measurement process, the equipment used for the measurement process, and / or the test subject 3. Corresponding data and specifications can be obtained, in particular, from analog or digital datasets or datasheets 6. This also applies to the target value 31, the variation range 32, and the known relationships 5 of the measured quantity 3.
[0037] For the distributions 310, 311, and 312 of the obtained measured values, each representing the injection amount Q, the widths of variation 310a, 311a, and 312a were ultimately determined to be 41, σ Qis determined as the variation of the target repetition. Alternatively, the variation of the target repetition may be determined from the difference between the maximum value and the minimum value of the widths of all variations. Finally, under the application of the standardized criteria, it can be examined whether this measurement process is qualified for quality control in serial production.
Explanation of Signs
[0038] 1 Test Subject 2 Measurement Parameter 3 Measured Quantity 5 Relationship, Characteristic Curve 6 Data Sheet 21 Variation Range 22 Lower Limit 23 Upper Limit 31 Target Value 32 Swing Range 33 Lower Limit 34 Upper Limit 41 Standard Deviation 100 Steps 200 Steps 210 Value 211 Value 212 Value 220 Step Width 221 Step Width 300 Steps 310 Distribution 310a Width of Variation 311 Distribution 311a Width of Variation 312 Distribution 312a Width of Variation 320 Interval 400 Steps 500 Steps 600 Steps 1000 Method Q Injection Quantity Q max Maximum Injection Quantity Q min Minimum Injection Quantity t Drive Control Time t max Maximum Time, Maximum Drive Control Time t min Minimum time, minimum drive control time
Claims
1. A method (1000) for determining the repeatability of a given measurement process to be performed on a test subject (1), Step (100) of preparing at least one test subject (1), A step (200) to determine at least one measurement parameter (2) of the measurement process that correlates with a measured quantity (3), which is a quantity to be measured that represents the characteristics of the test subject (1), The steps include determining the range of variation (21) of at least one measurement parameter (2), Step (300): Perform multiple measurements on the test subject (1) for each of the multiple different values (210, 211, 212) of the at least one measurement parameter (2) within the variation range (21), obtain multiple measurement values of the measured quantity (3) in the multiple measurements, and obtain a distribution (310, 311, 312) showing the range of variation of the multiple measurement values for each of the multiple different values (210, 211, 212); A step of evaluating the repeating variability based on the distribution (310, 311, 312), Includes, The multiple measurements at each of the multiple different values (210, 211, 212) are performed in accordance with the gradual change in the value of at least one measurement parameter (2). The interval (320) between the maximum, minimum, median, or mean value in the distribution (310) obtained at the first value among the multiple different values (210, 211, 212) and the target value (31) of the measured quantity (3) is adjusted to match the range from the first value of the second value, which is the value following the first value among the multiple different values (210, 211, 212). A method (1000) for determining the variability of repeated measurements of a given measurement process to be performed on the test subject (1).
2. The method according to Claim 1 (1000), wherein the smaller the interval (320), the smaller the width of the second value from the first value, and the larger the interval (320), the larger the width of the second value from the first value.
3. The at least one measurement parameter (2) is a plurality of measurement parameters (2) that correlate with the measured quantity (3), The variation range (21) for each of the plurality of measurement parameters (2) is determined. Multiple measurements are performed on the test subject (1) at each of the multiple different values (210, 211, 212) within the variation range (21) of each of the multiple measurement parameters (2), and a distribution (310, 311, 312) is obtained that shows the range of variation of the multiple measured values at each of the multiple different values (210, 211, 212) of each of the multiple measurement parameters (2). The variation of the repetitions is evaluated based on the obtained distribution (310, 311, 312). The method according to claim 1 or 2, characterized by (1000).
4. The method according to claim 1 or 2 (1000), wherein the standard deviation (41) of the distribution (310, 311, 312) is determined as the variation of the repetitions.
5. The method according to claim 1 or 2, wherein the repeating variation is determined as the difference between the maximum and minimum values in all of the distributions (310, 311, 312).
6. The method according to claim 1 or 2 (1000), wherein the range of variation (21) with respect to the measurement parameter (2) is determined based on the technical specifications of the measurement process, the equipment used for the measurement process and / or the test subject (3).
7. The method according to claim 1 or 2 (1000), wherein the range of variation (21) is determined based on the target value (31) and / or a predetermined range of variation (32) in relation to a known relationship (5) between the measured quantity (3) and the at least one measured parameter (2).
8. The method according to claim 7 (1000), wherein the target value (31) of the measured quantity (3), the fluctuation range (32) of the measured quantity (3), the known relationship (5) between the measured quantity (3) and the measurement parameter (2), and / or technical specifications are obtained from at least one data sheet (6) and / or from a digitally stored dataset of the measurement process, the equipment used for the measurement process and / or the test subject (1).
9. The method according to claim 1 or 2 (1000), wherein at least one sample of any component is selected as the test subject (1).
10. The method according to claim 9 (1000), wherein a fuel injector for an internal combustion engine is selected as the test subject (1).
11. The method according to claim 9 (1000), wherein, in response to the determined repeating variability satisfying a predetermined standard, the measurement process is deemed suitable for quality control of cereal production.
12. The method according to claim 1 or 2 (1000), wherein the measured quantity (3) includes the amount of liquid injected, the amount of gas supplied, the output, the current and / or the torque.
13. The method according to claim 1 or 2 (1000), wherein the measurement parameter (2) includes pressure, injection time, supply time, voltage and / or temperature.
14. A computer program comprising machine-readable instructions, wherein, when the computer program is executed on one or more computers, the instructions cause one or more of the computers to perform a calculation step according to at least claim 1 or 2.
15. A machine-readable recording medium containing the computer program described in claim 14.
16. A computer implementing the computer program described in claim 14.
17. A computer equipped with the machine-readable recording medium described in claim 15.