How to calculate the variation of repetitions

Abstract

A method (1000) for determining the variability of repetitions of a given measurement process to be carried out on a test object (1), comprising the steps of preparing (100) at least one test object (1), determining (200) at least one measurement parameter (2) of the measurement process, the result of which is additionally dependent on the characteristic to be measured of the test object (1) given by the measurand (3) under consideration, determining a variation range (21) for the at least one measurement parameter (2), and determining different values ​​of the at least one measurement parameter (2) within the respective variation range (21). A method (1000) for determining the variability of a repetition of a given measurement process to be performed on a test object (1), comprising: a step (300) of performing a plurality of measurements on a test object (1) relating to (210, 211, 212) and thereby obtaining at least one distribution (310, 311, 312) of measured values ​​of a measurand (3) having at least one degree of variability (310a, 311a, 312a), in particular a range of variability; and a step of evaluating the variability of a desired repetition from the at least one degree of variability (310a, 311a, 312a) of the at least one distribution (310, 311, 312) of the obtained measured values.

Description

[Technical field]

[0001] The present invention relates to a method for determining repetitive variability. The present invention also relates to a computer program for carrying out said method, to a machine-readable recording medium comprising such a computer program, and to one or more computers comprising said computer program. [Background technology]

[0002] As proof of the capability of a measurement process within the framework of a functional test of a series of components, statistical methods based, inter alia, on determining the repeat variance of the test feature are used. In particular, the %GRR method has proven highly successful in this context. Typically, for the assessment of a measurement process, the repeat variance, which is usually included in the measurement of a relatively large number of test objects, is considered. For example, 25 test objects can be measured at least twice each. For the measured values ​​obtained for each test object, the range of the variance is determined. The range of the variance obtained for all test objects forms 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 assessed. A measurement process can be qualified, for example, if the ratio of the repeat variance to the tolerance for the feature being tested is below a predefined threshold value. Often, measurements of this kind are performed by several testers, which further requires the provision of a sufficiently large number of test objects for a statistically usable evaluation. Summary of the Invention

[0003] It has been recognized that evaluation of test processes by conventional methods is not feasible when there are very few test objects. Thus, conventional methods for evaluation of test processes do not allow for consideration of measurement processes that include a small series of test objects or that are in an early stage of development.

[0004] The invention relates to a method for determining the variability of repetitions of a given measurement process to be carried out on at least one test subject, the method then comprising at least the steps described below.

[0005] Repetition variability refers to the variability of measurements when measurements are repeated on the same subject, in the same laboratory, by the same staff (repetition conditions), with other conditions remaining the same, where measurement parameters can be kept constant in a first measurement process and varied in a second measurement process between measurements. Here, "kept constant" or "varied" can relate to the choice of measurement parameters and / or the values ​​of these measurement parameters. In the following, the choice of measurement parameters is kept constant, but the values ​​of the measurement parameters are varied.

[0006] In one method step, at least one test object is provided. In a further step, at least one measurement parameter of a measurement process is determined, the result of which is additionally dependent on the property to be measured of the test object, which can be explained by the measured quantity under consideration. In a further step, a variation range for the at least one measurement parameter is determined, whereby a number of measurements can be performed on the test object, with different values ​​of the at least one measurement parameter within each determined variation range being selected, so that in the course of the measurement, a measured value of each of the measured quantities under consideration is obtained.

[0007] The repeated measurements result in a plurality of measurements each with the selected measurement parameter forming at least one unique distribution having at least one degree of variability, in particular a width of variability, and finally, the degree of variability is used to evaluate the variability of the desired repeats.

[0008] This can be done, for example, by determining the standard deviation for all the obtained degrees of variation by statistical evaluation of the distribution obtained, and subsequently evaluating the desired repetition variation of the measurement process from this standard deviation. The repetition variation can in this case, for example, correspond to the standard deviation for at least one degree of variation, in particular for all the obtained ranges of variation. The repetition variation may, however, for example, be determined as the difference between the maximum value of the obtained range of variation and the minimum value of the obtained range of variation.

[0009] It should be noted that the measurement parameters that are varied in the methods described above and below are not varied, but held constant, in conventional methods of assessing measurement process capability.

[0010] A number of measurement parameters of the measurement process may be determined, the result of which depends additionally on the property to be measured of the test object given by the measurand under consideration. A variation range for each measurement parameter may then be determined. Furthermore, a number of measurements on the test object for different values ​​of each measurement parameter within the respective variation ranges may then be carried out, resulting in a number of distributions of the measured values ​​of the measurand under consideration, each with one degree of variation, in particular a range of variation. Then again, the variation of the repetitions of interest can be evaluated from the degree of variation of the distribution of the obtained measured values.

[0011] The method described above and below allows a measurement process to be assessed, i.e. to determine its measurement process capability, when only a small number of test objects are present. The method thus makes it possible to determine whether a measurement process is sufficiently suitable even for a small number of test objects, even down to a single test object. The method is thus suitable for assessing a measurement process when only one test object is present or when only a small number of test objects are present. The measurement uncertainty of the considered measurement method can be investigated, whereby resources, for example in the form of stable components that must be prepared for the capability study and possibly also selected, can be reduced or saved. This can lead to significant savings in terms of costs for series components and set-up times in the corresponding laboratory equipment.

[0012] A further advantage of the method described above and below is provided by the fact that a capability study for a measurement process with one test object or a small number of test objects can be carried out already in an early phase of the corresponding development process, when only a small number of sample test objects are available. This allows the proof of capability of the measurement process already in an early project phase. In particular, at such an early time point, possible improvement possibilities can be recognized and realized. This, in turn, can lead to significant savings compared to later proof of capability, where the need for substantial changes or improvements of the measurement process can only be recognized after high costs have already been incurred.

[0013] According to one embodiment, the variation range for the measured parameter is determined on the basis of the target value. Additionally or alternatively, the variation range for the measured parameter may be determined on the basis of a predefined deviation range for the measurand in conjunction with a known relationship between the measurand and the measured parameter.

[0014] The known relationship between the measured quantity and the measured parameter can be, for example, a characteristic line which represents the dependence of the measured quantity on the measured parameter. For example, at least the functional relationship between the measured quantity and the measured parameter may be known in the form of a formula from which the characteristic line can be obtained by substituting corresponding values ​​for the measured parameter and, if appropriate, further parameters which characterize the measurement process and / or the test object. The functional relationship can also be additionally or exclusively experimentally determined and stored, for example, in the form of a value table, from which, for example, corresponding representative values ​​for the measured parameter and the measured quantity can be taken. The value table or also a corresponding graphical indication of the relationship between the measured quantity and the measured parameter, if appropriate with suitable interpolation, can be stored in digital form and / or in the form of a data sheet. The setpoint values ​​of the measured quantity and the variation ranges of the measured parameters can also be stored in the data sheet in digital or analog form.

[0015] For example, the measurement parameters may be gradually varied within the scope of the method, the step width of the variation in subsequent steps being adapted, in each case, in accordance with the interval of the measured values, of the distribution obtained in the preceding step and / or of the median or mean of this distribution, relative to a predefined target value of the measured quantity.

[0016] This can be achieved, for example, by considering the amount of difference between the distribution or median or mean of the measured values, on the one hand, and the target value of the measurand, on the other hand. For measured values ​​in the vicinity of the target value, for example, a smaller step size can be selected when varying the measurement parameter, so that a more accurate consideration of this range of the measurand can be achieved. This allows for an additional consideration of the range around the target value that is particularly important for the measurement process.

[0017] Furthermore, the step size for varying the measurement parameter may be selected to be smaller within a range around the target value of the measurand and larger near the upper and lower limits of the excursion range for the measurand, such that ranges of measurements that are more spaced apart from the target value of the measurand can receive a relatively weaker weighting in the investigation of the qualification of the measurement process, and ranges that are in the vicinity of the target value of the measurand can receive a relatively stronger weighting in the qualification.

[0018] According to another embodiment, the variation range for the measurement parameter is determined based on the technical specifications of the measurement process, the equipment used for the measurement process and / or the test object, so that corresponding prior knowledge can be made available for determining the repeat variability.

[0019] According to another embodiment, the target value of the measurand, the run-out range of the measurand, the known relationship between the measurand and the measurement parameters, and / or the technical specifications are obtained from at least one data sheet. Additionally or alternatively, the aforementioned variables may be obtained from digitally stored data sets of the measurement process, the equipment used for the measurement process, and / or the test object. In this case, these sources of prior knowledge may also be utilized for the method of determining the repeatability variation.

[0020] According to an embodiment, at least one sample of a component, which is produced either individually or in series, may be selected as the test object. Advantageously, this allows a small number of test objects produced, for example only one or two test objects, to be used in the course of the method. This allows the costs associated with the preparation of the test objects and the implementation of the measurement process to be reduced. Furthermore, the method can also be used in the case of the evaluation of measurement processes in which there are sub-series with a small number of series of components. At the very least, a part of such a sub-series can thus be used as the test object within the framework of the method, avoiding the corresponding preparation of a larger number of test objects of the sub-series.

[0021] According to an embodiment of the invention, a fuel injector for an internal combustion engine can be selected as a test object, and it is no longer necessary in the above-mentioned and below-mentioned method to provide a larger number of fuel injectors in order to determine the variability of the corresponding repetitions of the measurement process within the scope of the evaluation of the test process.

[0022] According to one embodiment, it can be determined whether the variation of the repetitions determined within the framework of the method satisfies a predefined criterion, in which case the measurement process can be declared suitable for the quality control of serial production.

[0023] For example, the predetermined criterion may be a condition that the ratio of the determined repeat variation to a predetermined tolerance is not to exceed or, depending on the set of conditions, not to fall below a predetermined value, in which case an adapted version of the %GRR Proof of Capability method described herein may be used.

[0024] According to one embodiment, the measured quantities may include liquid injection volume, gas delivery volume, power, current and / or torque.

[0025] According to another embodiment, the measured parameters may include pressure, injection time, voltage and / or temperature.

[0026] The invention further relates to a computer program comprising machine-readable instructions which, when the computer program is executed on one or more computers, cause the one or more computers to carry out the method according to the invention. The invention also encompasses a machine-readable recording medium having said computer program stored thereon, and a computer implementing said computer program or said machine-readable recording medium.

[0027] Further measures for improving the invention will be explained in more detail below with reference to the drawings together with a description of preferred embodiments of the invention. [Brief description of the drawings]

[0028] [Figure 1] FIG. 10 illustrates an example embodiment of a method 1000 for determining the variability of repetitions of a given measurement process to be performed on a test subject 1. [Diagram 2] FIG. 10 illustrates another embodiment of the method 1000. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] According to FIG. 1, the method comprises at least the steps described below. In step 100, at least one test object 1 is provided. In this case, the test object 1 can be, for example, a fuel injector. In step 200, at least one measurement parameter 2 of a measurement process is determined, the result of which is additionally dependent on the characteristic to be measured of the test object 1. The characteristic to be measured of the test object 1 in this case relates to a measured quantity 3 to be considered within the scope of the measurement process. It should be noted that in conventional methods for determining the variation of repetitions of a measurement process, the aforementioned measurement parameter is not subject to fluctuations and is selected constant. In a further step 300, a variation range 21 for the measurement parameter 2 is determined. Thereafter, in step 400, a number of measurements are performed on the test object 1, whereby the measurement parameter 2 is changed within the determined variation range 21 and a number of (at least two) measured values ​​of the measured quantity to be considered are obtained for the corresponding values ​​of the measurement parameter. In step 500, the variability ranges 310a, 311a, 312a are calculated from the distributions 310, 311, 312 of the obtained measurements of the measurement quantities for each measurement parameter variation carried out. In step 600, the variability of the desired repetitions of the measurement process is finally determined from at least the standard deviation 41 of the individual variability ranges 310a, 311a, 312a. The relationships between all the variables mentioned are explained in more detail below with the aid of FIG. 2.

[0030] 2 shows an exemplary implementation of the method 1000 in serial production of fuel injectors. A fuel injector is provided as a test object 1 for which a measurement process is to be carried out and within which it is desired to determine the repeat variance of the measurement process. The aim of the method 1000 is to determine whether the repeat variance satisfies a given criterion, in which case the measurement process can be declared suitable for the quality control of serial production.

[0031] A measurement parameter 2 of the measurement process is then determined. The measurement parameter 2 is a parameter that is not varied in the conventional method for determining the variation of repetitions within the framework of the corresponding measurement. The difference between the method described here and the conventional method for the evaluation of a measurement process is, inter alia, that in the former case the parameter variation is carried out within the framework of the generation of the measured values, whereas in the latter case a number of test objects are used in the course of generating the corresponding measurement data, the measurement parameter however taking on a predefined, fixed value in this case. The result of the measurement process obviously depends, inter alia, on the selection of the measurement parameter 2. In the example shown in FIG. 2, the actuation time t of the fuel injector is selected as the measurement parameter 2.

[0032] In the method described here, therefore, a variation range 21 for the measurement parameter 2 is determined and a number of measurements are carried out on the test object 1 with different values ​​210, 211, 212 of the measurement parameter 2 within the variation range 21, where the control time t is a minimum time t min and the maximum time t as the upper limit 23 of the fluctuation range 21 max The value fluctuates between .

[0033] As shown in FIG. 2, a variation range 21 of the measurement parameter 2, which may be represented in particular by an upper limit 23 and a lower limit 22, can be determined on the basis of a target value 31 of the measurand 3 and a corresponding deflection range 32 of the measurand 3. The upper limit 23 of the variation range 21 of the measurement parameter 2, here the maximum drive control time t maxis, for example, the upper limit 34 of the deflection range 32 of the measured quantity 3, here the maximum injection quantity Q max Similarly, the lower limit 22 of the variation range 21 of the measurement parameter 2, here the minimum drive control time t min is the lower limit 33 of the deviation range 32 of the measured quantity 3, here the minimum injection quantity Q min 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 can be, for example, 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 the form of a graph. Corresponding tables of the relationship between the measured quantity 3 and the measured parameter 2, as well as graphical representations, can be obtained, for example by interpolation, using previous measurements on one or more reference test objects, and can be used within the framework of the method described here. Corresponding information on the characteristic line 5 can be stored in a database or data sheet 6 in digital or analog form and is available in the corresponding method steps.

[0034] Thus, a number of measurements are carried out on the test object 1, where different values ​​210, 211, 212 of the measurement parameter 2 within the variation range 21 are considered. For each value 210, 211, 212 of the measurement parameter 2, a distribution 310, 311, 312 of measurement values ​​is obtained with a width of variation 310a, 311a, 312a. The dots (symbol "·") plotted in Fig. 2 respectively represent the mean values ​​of the distributions 310, 311, 312 and further similar distributions. These mean values ​​are scattered around the characteristic line 5.

[0035] Within the scope of the measurement process, the measurement parameter 2 can be varied in each case incrementally. In so doing, the step width 220, 221 between successive measurements can be adapted in each case depending on the distance that the distribution 310 (or also, for example, the median or mean value for this distribution 310) has from the target value 31 of the measurand 3. For example, if the distribution 310 or its mean or median in a preceding step has a small distance 320 from a predefined target value 31 of the measurand 3, then the step width 221 in the following step can be selected to be smaller than the step width 220 in the preceding step. A small distance can be understood, for example, as a quantitative deviation within predefined limits. As a result, within the scope of the presented method, a larger number of measured values ​​can be generated in the vicinity around the target value 31 of the measurand 3 in the range in which the measured values ​​have a larger distance 320 from the target value 31. Additionally and in a similar manner, the step size 220 of a subsequent step may be selected to be larger compared to the step size 221 of the previous step if the distribution 310 or its mean or median in the previous step has a larger distance 320 from the predetermined target value 31 of the measurand 3.

[0036] Furthermore, the variation range 21 of the measurement parameter 2, and possibly also 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 object 3. Corresponding data and specifications can be taken in particular from analog or digital data sets or data sheets 6. The same applies to the setpoint value 31, the run-out range 32 and the known relationship 5 of the measurement quantity 3.

[0037] Finally, the standard deviations 41, σ of the distributions 310, 311, 312 of the measured values ​​obtained, which respectively represent the injection amount Q, are calculated as follows: Qis determined as the desired repeat variability. Alternatively, the desired repeat variability may be determined from the difference between the maximum and minimum values ​​of all variability ranges. Finally, with the aid of standardized criteria, it can be checked whether this measurement process can be considered suitable for quality control of serial production. [Explanation of symbols]

[0038] 1. Test subjects 2. Measurement parameters 3. Measurement quantities 5 Relationships and characteristic lines 6 Datasheets 21 Range of variation 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 Variation range 311 Distribution 311a Variation range 312 Distribution 312a Range of variation 320 interval 400 steps 500 steps 600 steps 1000 ways Q injection amount Q max Maximum injection volume Q min Minimum injection volume 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 variability for repetitions of a given measurement process to be performed on a test object (1), comprising: Providing (100) at least one test subject (1); - determining (200) at least one measurement parameter (2) of the measurement process, the result of which depends additionally to the characteristic to be measured of the test object (1) given by the measurand (3) under consideration; determining a variation range (21) for at least one of said measured parameters (2); - a step (300) of performing a number of measurements on the test object (1) for different values ​​(210, 211, 212) of at least one of the measurement parameters (2) within the respective variation ranges (21) to obtain at least one distribution (310, 311, 312) of the measured values ​​of the measurand (3) having at least one degree of variability (310a, 311a, 312a), in particular a range of variability; - evaluating the variability of said iterations of interest from at least one of said measures of variability (310a, 311a, 312a) of at least one of said distributions (310, 311, 312) of said measurements obtained; Including, A method (1000) for determining variability for repetitions of a given measurement process to be performed on a test object (1).

2. Providing (100) at least one test subject (1); - determining (200) a number of measurement parameters (2) of the measurement process, the result of which depends additionally to the characteristic to be measured of the test object (1) given by the measurand (3) under consideration; determining a variation range (21) for each measurement parameter (2); - a step (300) of carrying out a number of measurements on the test object (1) for different values ​​(210, 211, 212) of each measurement parameter (2) within the respective variation range (21), resulting in a number of distributions (310, 311, 312) of measurements of the measurand (3) each having one degree of variation (310a, 311a, 312a), in particular a range of variation; Evaluating the variability of the repetition of interest from the degree of variability (310a, 311a, 312a) of the distribution (310, 311, 312) of the obtained measurement values; To carry out 2. The method (1000) of claim 1.

3. The method (1000) according to any one of claims 1 to 2, further comprising determining a standard deviation (41) for at least one of said measures of variability (310a, 311a, 312a) as the variability of the repetitions of interest.

4. 3. The method of claim 1, wherein the variability of the iteration of interest is determined as the difference between the maximum and minimum values ​​of all variability ranges.

5. 5. The method (1000) according to claim 1, wherein the fluctuation range (21) for the measurement parameter (2) is determined on the basis of a target value (31) for the measurement quantity (3) and / or a predetermined runout range (32) in relation to a known relationship (5) between the measurement quantity (3) and the measurement parameter (2).

6. 6. The method (1000) according to claim 5, wherein the measurement parameter (2) is varied gradually and the step width (220, 221) in subsequent steps is adapted depending on the distance (320) of the measured value, of the distribution (310) obtained in the preceding step and / or of its median or mean value to the predetermined target value (31) of the measured quantity (3).

7. 7. The method (1000) of claim 6, wherein the step width (220, 221) for varying the measurement parameter (2) is selected to be smaller within a range around the target value (31) of the measured quantity (3) and larger near the upper limit (34) and the lower limit (33) of the runout range (32) for the measured quantity (3).

8. 8. The method (1000) according to any one of claims 1 to 7, wherein the variation range (21) for the measurement parameter (2) is determined on the basis of technical specifications of the measurement process, of equipment used for the measurement process and / or of the test object (3).

9. 9. The method (1000) according to any one of claims 5 to 8, wherein the target value (31) of the measured quantity (3), the run-out range (32) for the measured quantity (3), the known relationship (5) between the measured quantity (3) and the measurement parameter (2) and / or the technical specifications are obtained from at least one data sheet (6) and / or from digitally stored data sets of the measurement process, of equipment used for the measurement process and / or of the test object (1).

10. The method (1000) according to any one of claims 1 to 9, further comprising selecting at least one sample of any component as the test object (1).

11. The method (1000) of claim 10, comprising selecting as the test object (1) a fuel injector for an internal combustion engine.

12. 12. The method (1000) of claim 10, further comprising determining that the measurement process is suitable for serial production quality control in response to the determined repetitive variation satisfying a predetermined criterion.

13. The method (1000) according to any one of claims 1 to 12, wherein the measured quantity (3) comprises a liquid injection quantity, a gas supply quantity, a power output, a current and / or a torque.

14. The method (1000) according to any one of claims 1 to 13, wherein the measured parameters (2) comprise pressure, injection time, delivery time, voltage and / or temperature.

15. 15. A computer program comprising machine-readable instructions which, when the computer program is executed on one or more computers, cause the one or more computers to perform at least the calculation steps of the method of any one of claims 1 to 14.

16. 16. A machine-readable recording medium containing the computer program of claim 15.

17. A computer having implemented thereon the computer program according to claim 15 and / or the machine-readable recording medium according to claim 16.

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