Method for determining a repeatability variance
Patent Information
- Application Number
- EP2023703422
- Authority / Receiving Office
- EP · EP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2022-03-22
- Filing Date
- 2023-02-02
- Publication Date
- 2025-01-29
AI Technical Summary
Conventional methods for evaluating measurement process capability are inadequate for small series or early-stage development processes, as they require a large number of test objects and are unable to assess measurement processes with only a few test objects.
A method for determining repeat scatter by varying measurement parameters on a single test object, allowing for the evaluation of measurement process capability using statistical analysis of scatter measures, which reduces resource and cost requirements.
Enables the assessment of measurement process capability with a small number of test objects, identifying potential improvements early in the development process and reducing costs associated with later-stage changes.
Smart Images
Figure 1.1
Abstract
Description
[0001] Description
[0002] Title:
[0003] Method for determining repeatability
[0004] The present invention relates to a method for determining repeatability. The invention also relates to a computer program implementing the aforementioned method, a machine-readable data carrier comprising such a computer program, and one or more computers comprising the aforementioned computer program.
[0005] State of the art
[0006] Statistical methods are used to demonstrate measurement process capability in the functional testing of series components. These methods are based, among other things, on determining the repeatability of a test characteristic. The %GRR method, among others, has proven very effective in this context. Typically, repeatability is considered for measurement process evaluation, which generally involves measuring a large number of test objects. For example, 25 test objects can each be measured at least twice. A spread is determined for the measured values obtained for each test object. The spreads obtained for all test objects form a distribution, the standard deviation of which is determined. The capability of the measurement process is ultimately derived or evaluated from this standard deviation.The measurement process can be deemed suitable, for example, if the ratio of repeatability to tolerance for the tested characteristic falls below a specified threshold. Such measurements are often performed by multiple testers and also require the provision of a sufficiently large number of test objects for a statistically accessible evaluation. Disclosure of the invention.
[0007] It was recognized that a test process evaluation using conventional methods is not feasible for very few test objects. Therefore, common test process evaluation methods cannot consider measurement processes involving small series of test objects or measurement processes at an early stage of development.
[0008] The present invention relates to a method for determining the repeatability of a given measurement process to be performed on at least one test object. The method comprises at least the steps outlined below.
[0009] Repeatability refers to the variation in measured values when the measurement is repeated on the same object, in the same laboratory, by the same personnel (repeatability conditions). Under otherwise identical conditions, the measurement parameters can be kept constant between measurements in a first measurement process and varied in a second measurement process. Here, "keeping constant" or "varying" can refer to the selection of measurement parameters and / or the values of these measurement parameters. In the following, the selection of measurement parameters is kept constant, but the values of the measurement parameters are varied.
[0010] In one method step, at least one test object is provided. In a next step, at least one measurement parameter of the measurement process is determined, on which the result of the measurement process also depends - in addition to the properties of the test object to be measured, which can be described by a measurand to be considered. In a further step, a variation range for the at least one measurement parameter is determined in order to then perform several measurements on the test object. Different values of the at least one measurement parameter are to be selected within the determined respective variation range so that a measured value of the measurand to be considered is obtained in the course of each measurement. By repeated measurements, several measured values are subsequently determined for each selected measurement parameter, which form at least one separate distribution with at least one measure of dispersion, in particular a spread.Finally, the desired repeatability is evaluated from the scatter measures.
[0011] This can be achieved, for example, by statistically evaluating the obtained distributions by determining a standard deviation across all obtained scatter measures and then using this standard deviation to evaluate the desired repeatability of the measurement process. The repeatability can, for example, correspond to the standard deviation across at least one scatter measure, in particular across all obtained scatter ranges. However, the repeatability can also be determined, for example, as the difference between the maximum and minimum of the obtained scatter ranges.
[0012] It should be noted that the measurement parameter which is varied in the procedure described above and below is not varied in a conventional method for evaluating the measurement process capability but is kept constant.
[0013] It is also possible to determine several measurement parameters of the measurement process, on which the result of the measurement process depends in addition to the properties of the test object to be measured, given by a measurand under consideration. A variation range is then determined for each measurement parameter. Furthermore, several measurements can then be performed on the test object for different values of each measurement parameter within the respective variation range, so that several distributions of measured values of the measurand under consideration are obtained, each with a measure of dispersion, in particular a spread. The desired repeatability dispersion can then be evaluated from the measures of dispersion of the distributions of the obtained measured values.
[0014] The method described above and below makes it possible to evaluate a measuring process, i.e. to assess its measurement process capability, when only a small number of test objects are available. Using the method, it can therefore be determined whether a measuring process is sufficiently qualified, even for a small number of test objects, right down to a single test object. The method is therefore suitable for evaluating a measuring process when there is only one test object or a small number of test objects. The measurement uncertainty of a measuring method in question can be investigated, whereby resources in the form of stable parts, which must be provided and possibly selected for a capability study, can be reduced or saved. This can lead to considerable savings potential in terms of the costs for series parts and with regard to setup times in corresponding test facilities.
[0015] A further advantage of the method described above and below is that the capability study with regard to the measurement process can be carried out using one test object or a small number of test objects at an early stage of a corresponding development process, in which only a few sample test objects are available. This enables the capability of the measurement process to be demonstrated at an early stage of the project. In particular, any potential improvements can be identified and implemented at such an early stage. This, in turn, can lead to considerable savings potential compared to the case of a later capability study, in which the need for substantial changes or improvements to the measurement process might only be recognized after high costs have already been incurred.
[0016] According to one embodiment, the variation range for the measurement parameter is defined based on a target value. Additionally or instead, the variation range for the measurement parameter can be defined based on a predefined fluctuation range for the measured variable in conjunction with a known relationship between the measured variable and the measurement parameter.
[0017] The known relationship between the measurand and the measurement parameter can, for example, be a characteristic curve which describes the dependence of the measurand on the measurement parameter. A functional relationship between the measurand and the measurement parameter can be known in the form of a formula which, for example, can be used to obtain a characteristic curve by inserting at least corresponding values for the measurement parameter and, under certain circumstances, further parameters which characterise the measurement process and / or the test object. It is also possible that a functional relationship has been determined additionally or exclusively experimentally and, for example, stored in the form of a table of values from which, for example, representative corresponding values for the measurement parameter and the measurand can be taken.A table of values or a corresponding graphical representation of the relationship between the measured variable and the measured parameter, possibly with suitable interpolation, can be stored in digital form and / or as a data sheet. The setpoint value of the measured variable and the variation range of the measured parameter can also be stored in a data sheet in digital or analog form.
[0018] For example, the measurement parameter can be varied step by step within the scope of the method, whereby the step size of the variation is adjusted in the subsequent step, depending on the distance of the distribution of measured values obtained in the previous step, and / or the median or mean value of this distribution, from the specified target value of the measured variable.
[0019] This can be achieved, for example, by considering the magnitude of the difference between the distribution of measured values, or the median or mean, on the one hand, and the target value of the measured variable on the other. For measured values close to the target value, for example, a smaller step size can be selected when varying the measurement parameter to achieve more precise consideration of this range of the measured variable. This allows for a region around the target value that is particularly relevant for the measurement process to be given greater consideration.
[0020] Furthermore, it can be provided that the step size with which the measurement parameter is varied is selected to be smaller in a range around the target value of the measured variable and larger near an upper and lower limit of the fluctuation range for the measured variable. This allows ranges of the measured values that are further away from the target value of the measured variable to have less weighting in the capability assessment of the measurement process, while ranges that are close to the target value of the measured variable receive greater weighting in the capability assessment.
[0021] According to another embodiment, the variation range for the measurement parameter is determined based on a technical specification of the measurement process, a device used for the measurement process, and / or the test object. In this way, corresponding prior knowledge can be utilized to determine the repeatability.
[0022] According to a further embodiment, the target value of the measured variable, the fluctuation range of the measured variable, the known relationship between the measured variable and the measurement parameter, and / or the technical specification are obtained from at least one data sheet. Additionally or alternatively, the aforementioned variables can be obtained from a digitally stored data set of the measurement process, a device used for the measurement process, and / or the test object. These sources of prior knowledge can then also be used for the method for determining the repeatability.
[0023] According to one embodiment, at least one example of a component manufactured as a single piece or in series can be selected as the test object. Advantageously, a small number of manufactured test objects can thus be used in the course of the method, for example only one or two test objects. This can reduce the costs associated with the provision of the test objects and the implementation of the measurement process. Furthermore, the method can also be used in the case of an evaluation of a measurement process in which small series with a small number of series components are available. At least a portion of such a small series can thus be used as the test object within the scope of the method, and a corresponding provision of a larger number of test objects from the small series can be avoided. According to one embodiment of the invention, a fuel injector for an internal combustion engine can be selected as the test object.The provision of a larger number of fuel injectors in order to determine a repeatability of a corresponding measurement process as part of a test process evaluation is no longer necessary in the procedure described above and below.
[0024] According to one embodiment, it can be determined whether the repeatability variation determined within the method meets a specified criterion. If this is the case, the measurement process can be declared suitable for quality control in series production.
[0025] For example, the specified criterion may be the condition that the ratio of the determined repeatability variation to a specified tolerance does not exceed or, depending on the formulation of the condition, falls below a specified value. A version of a %GRR capability assessment adapted from the procedure described here may be used.
[0026] According to one embodiment, the measured variable may include an injection quantity of a liquid, a supply quantity of a gas, an output power, a current and / or a torque.
[0027] According to a further embodiment, the measurement parameter may include a pressure, an injection time, an electrical voltage and / or a temperature.
[0028] Furthermore, the invention relates to a computer program containing machine-readable instructions which, when executed on one or more computers, cause the computer(s) to carry out a method according to the invention. The invention also encompasses a machine-readable data carrier on which the above computer program is stored, as well as a computer equipped with the aforementioned computer program or machine-readable data carriers. Further measures improving the invention are presented in more detail below, together with the description of the preferred embodiments of the invention, with reference to figures.
[0029] Examples of implementation
[0030] It shows:
[0031] Figure 1 shows an embodiment of the method 1000 for determining the repeatability of a given measurement process to be carried out on a test object 1;
[0032] Figure 2 shows another embodiment of the method 1000.
[0033] According to Figure 1, the method comprises at least the steps described below. In step 100, at least one test object 1 is provided. The test object 1 can be, for example, a fuel injector. In step 200, at least one measurement parameter 2 of the measurement process is determined, on which the result of the measurement process depends in addition to the properties of the test object 1 to be measured. The properties of the test object 1 to be measured relate to a measurand 3 to be considered within the scope of the measurement process. It should be noted that in conventional methods for determining a repeatability scatter of a measurement process, the aforementioned measurement parameter is not subject to variation but is selected to be constant. In a further step 300, a variation range 21 for the measurement parameter 2 is determined.Subsequently, in step 400, several measurements are performed on the test object 1, whereby the measurement parameter 2 is varied within the determined variation range 21 and several measured values (at least 2) of the measured variable under consideration are obtained for corresponding values of the measurement parameter. In step 500, a scatter range 310a, 311a, 312a is calculated from the distribution 310, 311, 312 of the obtained measured values of the measured variable for each measurement parameter variation performed. Finally, in step 600, the desired repeatability of the measurement process is determined at least from the standard deviation 41 of the individual scatter ranges 310a, 311a, 312a. The relationships between all of the aforementioned variables are explained in detail below with reference to Figure 2.
[0034] Figure 2 shows an exemplary implementation of method 1000 in the series production of fuel injectors. A fuel injector is provided as test object 1, on which a measurement process is to be performed, and within the scope of which the repeatability of the measurement process is to be determined. One goal of method 1000 is to determine whether the repeatability of the measurement process meets a specified criterion. If the criterion is met, the measurement process can be declared suitable for quality control in series production.
[0035] Furthermore, a measurement parameter 2 of the measurement process is determined. This is a parameter that is not varied in common methods for determining repeatability during the associated measurements. One difference between the method described here and common methods aimed at evaluating the measurement process is that in the former, a parameter variation is carried out during the generation of measured values, whereas in the latter, a large number of test objects are used in the course of generating corresponding measurement data and the measurement parameter assumes a predetermined fixed value. The result of the measurement process obviously depends, among other things, on the selection of measurement parameter 2. In the example shown in Figure 2, a control time t of the fuel injector is selected as measurement parameter 2.
[0036] In the method described here, a variation range 21 for the measurement parameter 2 is determined, and several measurements are carried out on the test object 1 with different values 210, 211, 212 within the variation range 21 of the measurement parameter 2. Here, the control time t varies between a minimum time t m in as the lower limit 22 of the variation range 21 and a maximum time t ma x as the upper limit 23 of the variation range 21. As outlined in Fig. 2, it is possible to determine the variation range 21 of the measurement parameter 2, which can be described, among other things, by an upper limit 23 and a lower limit 22, using a setpoint 31 of the measured variable 3 and an associated fluctuation range 32 of the measured variable 3. The upper limit 23 of the variation range 21 of the measured parameter 2, here the maximum control time t max, can therefore, for example, correspond to an upper limit 34 of the fluctuation range 32 of the measured variable 3, here the maximum injection quantity Q ma x. Analogously, the lower limit 22 of the variation range 21 of the measurement parameter 2, here the minimum control time t m in, correspond to a lower limit 33 of the fluctuation range 32 of the measured variable 3, here the minimum injection quantity Q min. Alternatively or additionally, a known relationship 5 between the measured variable 3 and the measured parameter 2 that is valid in the variation range 21 of the measured parameter 2 can also be used. The relationship 5 can, for example, be a known characteristic curve 5 between the measured variable 3 and the measured parameter 1, which can be recorded in tabular or graphical form. A corresponding table, but also a graphical representation of the relationship between the measured variable 3 and the measured parameter 2, can, for example, have been obtained by interpolation using previous measurements on one or more reference test objects and can be used within the framework of the method described here. The corresponding information regarding the characteristic curve 5 can be stored in digital or analog form in a database or a data sheet 6 and used in the corresponding method step.
[0037] Several measurements are now performed on test object 1, considering different values 210, 211, 212 of measurement parameter 2 within the variation range 21. For each value 210, 211, 212 of measurement parameter 2, a distribution 310, 311, 312 of measured values with a spread 310a, 311a, 312a is obtained. The points plotted in Figure 2 (symbol •) each denote mean values of distributions 310, 311, 312 and other similar distributions. These mean values scatter around characteristic curve 5. During the measurement process, measurement parameter 2 can be varied incrementally. It is possible that the step size 220, 221 between successive measurements is adjusted in each case depending on the distance that the distribution 310, or also, for example, a median or mean value over this distribution 310, has from the target value 31 of the measured variable 3.For example, the step size 221 in a subsequent step can be selected to be smaller than the step size 220 in a previous step if the distribution 310, or its mean or median, in the previous step has a small distance 320 from the specified target value 31 of the measured variable 3. A small distance can be understood, for example, as a deviation in amount within a specified limit. Thus, within the scope of the presented method, a larger number of measured values can be generated in an area around the target value 31 of the measured variable 3 than in areas in which the measured value has a greater distance 320 from the target value 31. Additionally and in an analogous manner, the step size 220 of a subsequent step can be selected to be larger than a step size 221 of a previous step if the distribution 310, orits mean or median, should have a greater distance 320 from the specified target value 31 of the measured variable 3 in the previous step.
[0038] It is also possible that the variation range 21 and possibly also the parameter values to be selected for the measurement parameter 2 are determined by a technical specification of the measurement process, a device used for the measurement process, and / or the test object 3. Corresponding data and specifications can be obtained, in particular, from an analog or digital data set or data sheet 6. This also applies to the target value 31 of the measured variable 3, the fluctuation range 32, and the known relationship 5.
[0039] Finally, a standard deviation 41, 0Q is determined as the desired repeatability using the spreads 310a, 311a, 312a of the distributions 310, 311, 312 of the obtained measured values, each representing injection quantities Q. Alternatively, the desired repeatability can also be determined from the difference between the maximum and minimum of all spreads. Finally, using a standardized criterion, it can be checked whether the measurement process can be considered suitable for quality control in series production.
Claims
Claims 1. Method (1000) for determining the repeatability of a given measurement process to be carried out on a test object (1) comprising the steps: Provide (100) at least one test object (1); Determine (200) at least one measurement parameter (2) of the measurement process on which the result of the measurement process depends in addition to the properties of the test object (1) to be measured, given by a measurand (3) to be considered; Determining a range of variation (21) for the at least one measurement parameter (2); Performing (300) several measurements on the test object (1) for different values (210, 211, 212) of the at least one measurement parameter (2) within the respective range of variation (21), so that at least one distribution (310, 311, 312) of measured values of the measured quantity (3) with at least one measure of dispersion (310a, 311a, 312a), in particular a range of dispersion, is obtained; Evaluate the desired repeat dispersion from the at least one measure of dispersion (310a, 311a, 312a) of the at least one distribution (310, 311, 312) of the obtained measured values.
2. Method (1000) according to claim 1, characterized in that the following steps are carried out: Provide (100) at least one test object (1); Determine (200) several measurement parameters (2) of the measurement process, on which the result of the measurement process depends in addition to the properties of the test object (1) to be measured, given by a measurement quantity (3) to be considered; Determining a range of variation (21) for each measurement parameter (2); performing (300) several measurements on the test object (1) for different values (210, 211, 212) of each measurement parameter (2) within of the respective range of variation (21), so that several distributions (310, 311, 312) of measured values of the measured quantity (3) are obtained, each with a measure of dispersion (310a, 311a, 312a), in particular a spread; Evaluating the desired repeat dispersion from the measures of dispersion (310a, 311a, 312a) of the distributions (310, 311, 312) of the obtained measured values. Method (1000) according to any one of claims 1 to 2, wherein a standard deviation (41) over the at least one measure of dispersion (310a, 311a, 312a) is determined as the desired repeat dispersion. Method according to any one of claims 1 to 2, wherein the desired repeat dispersion is determined as the difference between the maximum and the minimum of all spreads. Method (1000) according to any one of claims 1 to 4, wherein the range of variation (21) for the measured parameter (2) is determined based on a target value (31) and / or a predetermined fluctuation range (32) for the measured quantity (3) in conjunction with a known relationship (5) between the measured quantity (3) and the measured parameter (2).Method (1000) according to claim 5, wherein the measurement parameter (2) is varied stepwise, and the step size (220, 221) is adjusted in each subsequent step depending on the distance (320) of the distribution (310) of measured values obtained in the previous step, and / or their median or mean, from the predetermined setpoint (31) of the measured quantity (3). Method (1000) according to claim 6, wherein the step size (220, 221) with which the measurement parameter (2) is varied is selected to be smaller in a range around the setpoint (31) of the measured quantity (3) and larger in the vicinity of an upper (34) and lower limit (33) of the fluctuation range (32) for the measured quantity (3). Method (1000) according to any one of claims 1 to 7, wherein the range of variation (21) for the measurement parameter (2) is determined based on a technical specification of the measurement process, a device used for the measurement process, and / or the test object (3). Method (1000) according to any one of claims 5 to 8, wherein the target value (31) of the measurand (3), the range of variation (32) for the measurand (3), the known relationship (5) between the measurand (3) and the measurement parameter (2), and / or the technical specification are obtained from at least one data sheet (6) and / or digitally stored data set of the measurement process, a device used for the measurement process, and / or the test object (1). Method (1000) according to any one of the preceding claims, wherein at least one instance of a component is selected as the test object (1). Method (1000) according to claim 10, wherein a fuel injector for an internal combustion engine is selected as the test object (1).Method (1000) according to any one of claims 10 to 11, wherein, in response to the fact that the determined repeatability meets a predetermined criterion, it is determined that the measurement process is suitable for quality control in series production. Method (1000) according to any one of the preceding claims, wherein the measured variable (3) comprises an injection quantity of a liquid, a supply quantity of a gas, an output power, a current, and / or a torque. Method (1000) according to any one of the preceding claims, wherein the measured parameter (2) comprises a pressure, an injection time, a supply time, an electrical voltage, and / or a temperature. A computer program containing machine-readable instructions which, when executed on one or more computers, cause the computer(s) to perform at least the computational steps of a method according to any one of claims 1 to 14. A machine-readable data carrier containing a computer program according to claim 15. A computer equipped with the computer program according to claim 15, and / or with the machine-readable data carrier according to claim 16.