Systematic tolerance determination relating to samples of series products

Abstract

To determine a tolerance region within an operational design domain (ODD) around a target parameter sample of a parameterizable simulation model of a product relating to a steer-by-wire steering system and / or a steering system for highly automated driving.SOLUTION: A computer-implemented method comprises a step of determining, based on a maximum permissible distance and a dissimilarity metric, a tolerance region within an ODD around a target parameter sample, the dissimilarity metric defining a distance between a parameterized simulation model assigned to the target parameter sample and each parameterized simulation model.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] Series-produced products (especially large-scale series-produced products), such as steering systems, are subject to variations in product parameters, such as friction, elasticity, and / or inertia, due to manufacturing tolerances and errors. Series-produced products are also subject to additional parameter variations, due to wear and / or aging caused by environmental influences. All value ranges and value combinations of the parameter variations that occur in practice for a series-produced product are the so-called "operational design domain" (ODD) of this series-produced product, e.g., "Betriebsbereich fuer die Auslegung" in German.

[0002] In any simulation model of a (serial) manufactured product, differences between the modeled and real behavior arise based on parameter variations within the entire ODD and due to simplifications made during modeling. Such a simulation model, including model variations and / or model uncertainties within the entire ODD, is the basis for a simulation-based product release. The characterization of the model uncertainties required in this regard is typically performed today on the basis of a few selected product prototypes, since a full characterization is generally too time-consuming. In the following, the target version (also the ideal version) of a prototype is referred to as a product specimen, and its physical realization as a product individual. The selection of a product specimen for characterizing the uncertainty is usually based on expert opinion.

[0003] However, exact realization of the selected (ideal) product sample is practically impossible due to the final manufacturing precision, and as a result, the product sample and the product individual attributed to it have system behaviors that deviate slightly from each other. Typically, acceptable parameter tolerances for the realization of the product sample are defined by expert knowledge. Instead, the aim may be to manufacture and / or finish the product individual as accurately as possible. However, this does not preclude specifying and / or establishing parameter tolerances that are too loose, which may result in significant system behavior differences between the product sample and the product individual. On the other hand, it does not preclude specifying and / or establishing parameter tolerances that are too tight, which may result in unnecessarily high manufacturing costs, even though the system behavior of the product sample and the product individual may be substantially identical.

[0004] The problem to be solved that underlies this disclosure can be found, for example, in providing a method for determining how much a product individual can differ from a predetermined product example in order to fully realize this product example in terms of its system behavior.

[0005] Compared to conventional steering, steer-by-wire (SbW) steering systems and / or steering systems for highly automated driving (HAD) are subject to stricter standard requirements for product release. To ensure that the relatively strict release requirements for (large-scale) serially produced SbW and HAD steering systems do not significantly increase the costs of real-world testing and inspection compared to conventional steering, the field is focusing on simulation-based release processes. For such simulation-based release, a validated and verified simulation model of the steering system with known model uncertainties is essential.

[0006] Within the framework of an in-house and simulation-based release process for SbW and / or HAD steering systems, a characterization of model uncertainty should be performed based on a few selected product individuals.

[0007] The problem before us is therefore to systematically select, using model-based criteria, an attributable product exemplar that is therefore representative of the entire ODD of a (large-scale) serially produced steering system with quantifiable residual uncertainties.

[0008] Currently, there is no systematic method for determining meaningful parameter tolerances for the realization of a product sample, thereby ensuring negligible quantitative system behavior dissimilarity between the product sample and the product individual, thereby avoiding overly tight or loose tolerances.

[0009] In systems theory, various metrics are known to quantify the dissimilarity of two systems (hereafter, systems and products can be considered equivalent) and thereby compare these systems. Below, we will explain the gap metric, the ν-gap metric, and the L2 metric.

[0010] The gap metric quantifies the dissimilarity of the uncontrolled (open-loop) input / output behavior of two systems P1 and P2 relative to their stability and performance characteristics under controlled operation (closed-loop) on a scale within the real interval [0,1]. A metric result close to 0 means that both systems are very similar, and any P1-stabilizing controller will also stabilize system P2 with similar controlled performance. A metric result of 0 means that the considered systems P1 and P2 behave exactly identically. On the other hand, a metric result close to or equal to 1 means that systems P1 and P2 are very dissimilar. Furthermore, the gap metric allows for statements about the robust stability of closed control systems with model uncertainty. An explicit controller concept is required for gap metric evaluation. Further details about the definition and properties of the gap metric can be found in Chapter 17 of the book "Essentials of Robust Control," by Kemin Zhou and John C. Doyle, 1st Edition, Pearson, 1997, ISBN: 9780135258332.

[0011] The systems-theoretic propositions and implications of the ν-gap metric are very similar to the gap metric, although both metrics are defined fundamentally differently. Evaluation of the ν-gap metric requires examining the rotational speeds of the systems P1 and P2 being compared, rather than the controller concepts. Further details about the definition and properties of the ν-gap metric can be found in Chapter 17 of the book "Essentials of Robust Control," by Kemin Zhou and John C. Doyle, 1st Edition, Pearson, 1997, ISBN: 9780135258332, or in the publication "Frequency domain uncertainty and the graph topology," by Glenn Vinnicombe, IEEE Transactions on Automatic Control, vol. 38, no. 9, pp. 1371-1383, September 1993, DOI: 10.1109 / 9.237648.

[0012] The definition of the L2 metric is equivalent to the ν-gap metric, which does not examine the number of revolutions. For this reason, the L2 metric has a lower theoretical importance, although the principle propositions and implications of both metrics are similar. More details about the definition and properties of the L2 metric can be found in Chapter 17 of the book "Essentials of Robust Control", by Kemin Zhou and John C. Doyle, 1st Edition, Pearson, 1997, ISBN: 9780135258332.

[0013] All three introduced dissimilarity metrics have the following well-known properties: - These metrics quantify the dissimilarity of two systems based on their uncontrolled input / output behavior in relation to their stability and performance characteristics under controlled operation. The metric results allow determining whether two systems P1 and P2 are sufficiently similar, i.e. whether P1 can be considered representative of P2. - The metric results can also be explained as the (systematic) distance between the systems being compared. - For all systems to be compared, the following holds: L2 result ≦ ν-gap result ≦ gap result. [Prior art documents] [Non-patent literature]

[0014] [Non-Patent Document 1] Chapter 17 of the book "Essentials of Robust Control," by Kemin Zhou and John C. Doyle, 1st Edition, Pearson, 1997, ISBN: 9780135258332 [Non-patent document 2] Publication "Frequency domain uncertainty and the graph topology", Glenn Vinnicombe, IEEE Transactions on Automatic Control, vol. 38, no. 9, pp. 1371-1383, September 1993, DOI: 10.1109 / 9.237648 Summary of the Invention

[0015] A first general aspect of the present disclosure relates to a computer-implemented method for determining tolerance regions within an operational design domain (ODD) around target parameter samples of a parameterizable simulation model for a product, which may be, for example, a steer-by-wire steering system and / or a steering system for highly automated driving.

[0016] The method includes determining a tolerance region within the ODD around the target parameter sample based on a maximum allowable distance and a dissimilarity metric, the dissimilarity metric defining the distance between a parameterized simulation model assigned to the target parameter sample and each one of the parameterized simulation models.

[0017] A second general aspect of the present disclosure relates to a computer system designed to execute a computer-implemented method for determining tolerance regions within an operational design domain (ODD) around target parameter samples of a parameterizable simulation model for a product according to the first general aspect (or an embodiment thereof).

[0018] A third general aspect of the present disclosure relates to a computer program designed to perform a computer-implemented method for determining tolerance regions within an operational design domain (ODD) around target parameter samples of a parameterizable simulation model for a product according to the first general aspect (or an embodiment thereof).

[0019] A fourth general aspect of the present disclosure relates to a computer-readable medium or signal storing and / or embodying a computer program according to the third general aspect (or an embodiment thereof).

[0020] The method proposed herein according to the first general aspect (or an embodiment thereof) allows systematically determining tolerance zones based on model-based criteria for each realization (i.e., physical production) of a product sample, so that the system behavior of the product sample and the product individual match up to an acceptable quantitative dissimilarity. In particular, parameter tolerances for the product sample can be determined. The product sample to be manufactured is assigned to a target parameter sample and is therefore a target version of the prototype (also an ideal version). Thus, in the realization of the product sample, a product individual can be created as a prototype whose parameter sample lies within the tolerance zone around the target parameter sample.

[0021] Among other advantages over the state of the art, the following may be achieved by the method proposed herein according to the first general aspect (or an embodiment thereof): - A systematic procedure for establishing individual parameter tolerances (i.e., the range of permissible values ​​of one or more parameters) for the realization of a product sample; - the calculable individual tolerance zones of the product parameters (i.e. the allowable zones for all parameters) for every product sample, - Quantifiable system behavior dissimilarity between product samples and product individuals based on mismatched parameters within the entire ODD; - possibility of identifying local product parameters or combinations thereof in the vicinity of a product sample which have a particularly strong influence on the system behavior of this product sample.

[0022] The method proposed herein according to the first general aspect (or an embodiment thereof) may be used in the design stage and / or system development (i.e. after the design stage) when developing a real (i.e. physical) product (e.g. in the case of a steer-by-wire (SbW) steering system).

[0023] For example, in the design phase or system development, the present invention can be used to determine individual tolerance regions (e.g., to characterize model uncertainty) for (product) parameters for all product sample realizations, while respecting acceptable system behavior dissimilarities between the product samples and the product individuals. In particular, individual parameter tolerances for all product samples to be realized can be calculated, within which acceptable system behavior dissimilarities are respectively respected. This is important because prototypes whose parameters exactly correspond to target parameter samples are difficult to manufacture and / or require considerable effort for subsequent adjustment (e.g., shimming).

[0024] Moreover, for example, during the design phase, intermediate results of the method based on the first general aspect (or an embodiment thereof) and additional mathematical considerations can identify local product parameters and / or combinations thereof near a product sample that have a particularly strong influence on the system behavior of this product sample.

[0025] The target parameter sample may be one of a number of representative parameter samples in the ODD, i.e., one of a number of parameter samples representative of the entire ODD of a (series) manufactured product with quantifiable residual uncertainty. The realized product specimen (more precisely, the product individual) is in this case the representative product individual. On the other hand, the target parameter sample may not be a representative parameter sample, i.e., may be predetermined as a target for realization, arbitrarily or for other reasons (e.g., as a limit specimen).

[0026] Based on these representative product individuals, further system development can systematically characterize the uncertainty between the real product behavior and its modeled behavior within the entire ODD, due to parameter variations and modeling simplifications. The representative product individuals with characterized model uncertainty can then be used for product control development and / or product release.

[0027] The method proposed here according to the first general aspect (or an embodiment thereof) can be applied, inter alia, within the framework of a process for the release of SbW and HAD steering systems, in which the realization of a representative product specimen of the entire ODD can be utilized in the validation and / or verification of the steering system.

[0028] The method proposed herein based on the first general aspect (or an embodiment thereof) may be implemented analytically in whole or in part, which may avoid sampling a large number of parameter samples around a target parameter sample. In an embodiment, the tolerance region may sometimes be precisely determined. [Brief explanation of the drawings]

[0029] [Figure 1] 1 is a schematic diagram of an exemplary embodiment of a computer-implemented method for determining tolerance regions within an operational design domain (ODD) around target parameter samples of a parameterizable simulation model for a product, particularly a steer-by-wire steering system and / or a steering system for highly automated driving. [Figure 2] FIG. 1 illustrates an exemplary embodiment of the method. [Figure 3a] FIG. 3b illustrates the example ODD of FIG. 3b together with an example arbitrary number of parameterized simulation models. [Figure 3b] FIG. 1 illustrates an example ODD with target parameter samples surrounded by a tolerance region. DETAILED DESCRIPTION OF THE INVENTION

[0030] The method 100 proposed in this disclosure focuses on determining a tolerance region 30 within an operational design domain (ODD) around a target parameter sample 10 of a parameterizable simulation model for a product, particularly a steer-by-wire steering system and / or a steering system for highly automated driving. Alternatively or additionally, the method 100 may also focus on manufacturing a product specimen in the form of a product solid that realizes the product specimen.

[0031] The method 100 is wholly or partially analytical and assumes that a parameterizable simulation model exists in analytical form. First, a computer-implemented method 100 for determining a tolerance region 30 within an operational design domain (ODD) around a target parameter sample 10 of a parameterizable simulation model for a product is disclosed. The product may be, inter alia, a serially manufactured product, i.e., produced continuously. This method 100 (although it may also be applied to non-serial or small-batch production) is particularly useful when a large number of similar products are to be manufactured, although the large number of similar products may differ (e.g., due to production and / or materials). The large number of similar products may include, for example, more than 1e5 products per year, more than 5e5 products per year, or more than 1e6 products per year.

[0032] The product may for example be a steer-by-wire steering system. Alternatively or additionally, the product may be a steering system for automated driving, in particular highly automated driving.

[0033] For example, as shown schematically in FIG. 1, the method 100 includes determining 140 a tolerance region 30 within the ODD around the target parameter sample 10 based on a maximum allowable distance and a dissimilarity metric, where the dissimilarity metric defines the distance between a parameterized simulation model (always the same for each parameterized simulation model, denoted as Sample PUM in FIG. 2) assigned to the target parameter sample 10 and each one of the parameterized simulation models.

[0034] The dissimilarity metric may be based on a gap metric, a v-gap metric, and / or an L2 metric. In particular, the dissimilarity metric may be a gap metric, a v-gap metric, or an L2 metric. Alternatively, the dissimilarity metric may be based on a combination of the gap metric, the v-gap metric, and / or the L2 metric. The dissimilarity metric may output a quantitative measure of the dissimilarity of a pair of parameterized simulation models, i.e., a quantitative measure of how similar or dissimilar both parameterized simulation models of the pair are. In this respect, the dissimilarity metric may also be referred to as a similarity metric or a comparison metric. The quantitative measure output by the dissimilarity metric may be called a distance. If both parameterized simulation models of a pair are similar, the distance may be small. In particular, if both parameterized simulation models of a pair are identical (i.e., maximally similar), the distance may be zero. In contrast, if both parameterized simulation models of a pair are dissimilar, the distance may be large.

[0035] Exemplary results of the determination of the tolerance region 30 around the target parameter sample 10 are shown in Figures 3a-b. The target parameter sample 10 and tolerance region 30 may define a product specimen to be manufactured for this product, hereinafter also referred to as the target product specimen.

[0036] 1, the method 100 may further include a step 150 of outputting requirements for the production of a product specimen to be manufactured based on the target parameter sample 10 and the tolerance zones 30, the requirements being met if (e.g., all) parameters of the product specimen to be manufactured are within the tolerance zones 30. Step 150 is labeled "Convert tolerance zones to individual parameter tolerances" in the exemplary embodiment in FIG.

[0037] Based on this requirement, production (e.g., prototyping) can produce a product individual. This requirement may include or be an algorithm that can check whether one or more parameters of a product specimen (i.e., product individual) to be produced are within a tolerance region 30 around the target parameter sample 10. Such an algorithm can be useful, for example, when the tolerance region is higher dimensional and deviates significantly from a product space (i.e., a Cartesian product of intervals).

[0038] The determination 140 of the tolerance region 30 within the ODD may be performed, for example, such that within the tolerance region 30, each distance to the parameterized simulation model assigned to the target parameter sample 10 is less than the maximum allowable distance. Alternatively, the determination 140 of the tolerance region 30 within the ODD may be performed, for example, such that within the tolerance region 30, each distance to the parameterized simulation model assigned to the target parameter sample 10 is less than or equal to the maximum allowable distance. For example, it is also conceivable that the determination 140 of the tolerance region 30 within the ODD may be performed such that within the tolerance region 30, each distance to the parameterized simulation model assigned to the target parameter sample 10 is, at most, approximately the maximum allowable distance.

[0039] Furthermore, the determination 140 of the tolerance zone 30 within the ODD can be performed so that the tolerance zone 30 is exactly or approximately maximal. The tolerance zone 30 can be maximized, for example, based on the Lebesgue measure. The tolerance zone may, but need not, be a zone in the mathematical sense.

[0040] Furthermore, the determination 140 of the tolerance region 30 within the ODD may be performed such that the tolerance region 30 is a connected set within the ODD. Alternatively or additionally, the determination 140 may be performed such that the tolerance region 30 is a convex set within the ODD. In particular, the tolerance region may be a convex connected set within the ODD.

[0041] The tolerance region 30 can be, for example, a manifold, a polytope, in particular a convex polytope, a hyperellipsoid, or a hypercube. A hypercube is particularly advantageous since the parameters and their tolerances are independent of each other. If this is not given, the tolerance region 30 can be defined as an (arbitrary) manifold. To reduce the memory requirements of such a manifold, it may be useful to approximate the manifold by a polytope or a hyperellipsoid.

[0042] The ODD may be defined by a numerical and / or analytical description. A parameterized simulation model assigned to the target parameter samples 10 may be defined by evaluating a parameterizable analytical simulation model with the target parameter samples 10 in the ODD. For example, as shown schematically and optionally in FIG. 1 , the method 100 may further include generating 120 a parameterized simulation model assigned to the target parameter samples 10, where the parameterizable analytical simulation model is evaluated with the target parameter samples 10 in the ODD.

[0043] Each parameterized simulation model may be defined by evaluating a parameterizable analytical simulation model at one parameter sample in the ODD for the product. For example, as shown schematically and optionally in FIG. 1, method 100 may further include generating 121 each parameterized simulation model, where the parameterizable analytical simulation model is evaluated at one (any) parameter sample in the ODD.

[0044] Method 100 or step 140 may further include a step of converting the parameterizable analytical simulation model to obtain a parameterized simulation model attributed to any ODD sample (e.g., target parameter sample 10, or a parameter sample of a respective parameterized simulation model). Such a step is labeled "Convert PAM to PUM at each ODD point" in the exemplary embodiment in FIG. 2, where PAM refers to the parameterizable analytical simulation model, PUM refers to the parameterized simulation model of the parameterizable analytical simulation model, and ODD point refers to any respective parameter sample in the ODD.

[0045] Method 100 or step 140 may further include creating a dissimilarity metric function for any pair of a parameterized simulation model assigned to the target parameter sample 10 and each one of the parameterized simulation models in the entire ODD (or a portion thereof). Such a function can combine dissimilarity metrics for two transformed parameterizable analytical simulation models. Such a step is labeled "Create Metric Functions Between All PUMs and Exemplar PUMs" in the exemplary embodiment in FIG. 2, and each of these metric functions is a true dissimilarity metric function.

[0046] The original determination 140 of the tolerance region in the ODD around the target parameter sample 10 based on the maximum allowable distance and dissimilarity metric is labeled "Calculate Tolerance Region" in the exemplary embodiment in FIG. 2.

[0047] The target parameter sample 10 may be one of many target parameter samples representative of the ODD, in which case the parameter samples 20 of each parameterized simulation model may be within a representative range of the target parameter sample 10, i.e., may be limited to a portion of the ODD.

[0048] As shown schematically and optionally in FIG. 1 , for example, the method 100 may further include identifying 160 one or more parameters within the tolerance zone 30 that have a relatively large effect on the product. This may be done, for example, by sensitivity analysis. This may improve understanding of the product and its behavior. This may be beneficial, especially in the case of higher-dimensional ODDs, where the tolerance zone 30 is no longer directly observable. This may improve system understanding within the tolerance zone. The results may be taken into account in the realization of the target product sample.

[0049] The method 100 may include a step of determining 140 respective tolerance regions in the ODD around the multiple target parameter samples 10. In this case, therefore, around each target parameter sample, an attributed tolerance region in the ODD may be determined. The method 100 may include a step of defining a maximum allowable distance, which is labeled "Define Maximum Allowable Distance" in the exemplary embodiment in FIG. 2. In the case of multiple target parameter samples, this definition may be made globally, i.e., for all target parameters, or may be defined individually for each target parameter sample of the multiple target parameter samples.

[0050] As already discussed, the tolerance zone 30 can be used to determine one or more product individuals whose parameters lie within the tolerance zone around the target parameter sample. Alternatively or in addition to the requirement output 150 possibility discussed above, the method 100 can include a step of matching parameter samples of real product individuals stored in a database with the tolerance zone, particularly based on similarity relationships. In the event of an unsatisfactory degree of similarity, one or more instructions can be output on how a sufficiently satisfactory product sample can be manufactured from the real product sample (e.g., by adjustment, shimming, etc.). This product sample may, but need not, be a representative product sample.

[0051] The product individuals that realize the product samples to be manufactured can be used to design the control of the product. The design of the control of the product can be based, among other things, on a number of product individuals, each realizing a product sample to be manufactured. In particular, if the product samples to be manufactured represent ODDs, the conception and / or design of the robust control of the product can be improved. In particular, this can ensure that the control of the product is designed to be particularly robust. In particular, this can increase the safety of the product.

[0052] Furthermore, one or more requirements for a product can be checked based on individual products that realize a product sample to be manufactured. The check of one or more requirements for a product can be based, among other things, on multiple individual products that each realize a product sample to be manufactured. This ensures that the product is designed robustly. This can also, among other things, increase the safety of the product.

[0053] 2 illustrates one exemplary embodiment of the method 100. In this regard, for example, the following steps may be performed in sequence: - "Convert PAM to PUM at each ODD point" - "Create a metric function between all PUMs and sample PUMs" - "Maximum allowable distance specification" - "Calculating Tolerance Zones", 140 - "Convert Tolerance Zones to Individual Parameter Tolerances", 150 Figure 3a illustrates the exemplary ODD of Figure 3b together with an exemplary arbitrary multi-parameterized simulation model (more precisely, its parameter samples 20, shown as points), which is shown for illustrative purposes only.

[0054] FIG. 3 b illustrates an exemplary ODD having target parameter samples 10 (shown as crosses) surrounded by a tolerance region 30 . The ODD is a two-dimensional product space in this example, i.e., it is represented by two parameters (one parameter in the x-direction and the other parameter in the y-direction) that can each take on values ​​within an interval. In general, the ODD does not have to be a product space, but can be an (arbitrary) manifold. The ODD can have any dimension. The tolerance region 30 here is an exemplary rectangle.

[0055] For this example, in 3D, i.e., in the z direction perpendicular to the plane of the paper, the distance (based on the dissimilarity metric) of each parameter sample 20 in the ODD (or, more precisely, of that parameterized simulation model, some of which are shown as points in FIG. 3a) to the parameterized simulation model assigned to the target parameter sample 10 could be shown. In 2D, instead, four contour lines (e.g., 40) of this magnitude in the z direction are shown. Each of the four dashed lines is a contour line, i.e., has the same z value. In addition to these contour lines, the boundaries (dashed lines) of the range of influence of each representative parameterized simulation model are drawn.

[0056] In FIGS. 3a-b, for example, to the left of the boundary line (dashed line) of the range of influence, there may be a further target parameter sample, around which a tolerance region may also be determined according to the method 100.

[0057] Additionally, a computer system designed to execute a computer-implemented method 100 for determining a tolerance region 30 within an operational design domain (ODD) around a target parameter sample 10 of a parameterizable simulation model for a product is disclosed. The computer system may include a processor and / or a main memory.

[0058] Additionally, a computer program designed to perform the computer-implemented method 100 for determining a tolerance region 30 within an operational design domain (ODD) around a target parameter sample 10 of a parameterizable simulation model for a product is disclosed. The computer program may exist, for example, in an interpretable or compiled form. The computer program (even in part) may be downloaded into RAM of a computer (e.g., a computer system) for execution, for example, as a bit or byte sequence.

[0059] Further disclosed is a computer readable medium or signal storing and / or embodying a computer program. The medium may include, for example, one of RAM, ROM, EPROM, HDD, SSD, ... and on / in which the signal is stored. [Explanation of symbols]

[0060] 10 Target Parameter Samples 20 Parameter Samples 30 Tolerance area 40 Contour Lines

Claims

1. 1. A computer-implemented method (100) for determining a tolerance region (30) within an operational design domain (ODD) around a target parameter sample (10) of a parameterizable simulation model for a product, particularly a steer-by-wire steering system and / or a steering system for highly automated driving, comprising: - determining (140) the tolerance region (30) within the ODD around the target parameter sample (10) based on a maximum allowable distance and a dissimilarity metric, the dissimilarity metric defining the distance between a parameterized simulation model assigned to the target parameter sample (10) and each one of the parameterized simulation models.

2. The method (100) of claim 1, wherein the target parameter sample (10) and the tolerance zone (30) define a product specimen to be manufactured for the product.

3. - outputting (150) requirements for the production of the product specimen to be manufactured based on the target parameter sample (10) and the tolerance zone (30), said requirements being met if the parameters of the product specimen to be manufactured are within the tolerance zone (30); 3. The method (100) of claim 2, wherein optionally, said requirements include an algorithm capable of checking whether said parameters of said product specimen to be manufactured are within said tolerance zone (30).

4. The method (100) of any one of claims 1 to 3, wherein the dissimilarity metric is based on a gap metric, a v-gap metric, and / or an L2 metric.

5. 5. The method (100) according to claim 1, wherein the determination (140) of the tolerance region (30) within the ODD is performed such that within the tolerance region (30), each distance to the parameterized simulation model assigned to the target parameter sample (10) is less than the maximum allowable distance.

6. 6. The method of claim 5, wherein the determination of the tolerance region within the ODD is performed such that the tolerance region is strictly or approximately maximal, and optionally the tolerance region is maximized based on a Lebesgue measure.

7. The method (100) according to any one of claims 1 to 6, wherein the tolerance region (30) is a polytope, in particular a convex polytope, a hyperellipsoid or a hypercube.

8. 8. The method (100) of claim 1, wherein the parameterized simulation model assigned to the target parameter sample (10) is defined by evaluating the parameterizable analytical simulation model with the target parameter sample (10) in the ODD.

9. 9. The method (100) of claim 1, wherein each parameterized simulation model is defined by evaluating the parameterizable analytical simulation model at one parameter sample in the ODD for the product.

10. The method (100) of any one of claims 1 to 9, wherein the target parameter sample (10) is one from a number of target parameter samples representative of the ODD.

11. 11. The method (100) of claim 10, wherein the parameter samples (20) of each parameterized simulation model are within a representative range of the target parameter samples (10).

12. A method (100) according to any one of claims 1 to 11, comprising the step of identifying (160) one or more parameters within said tolerance zone (30) that have a relatively large influence on said product.

13. 13. A computer system designed to execute a computer-implemented method (100) for determining a tolerance region (30) within an operational design domain (ODD) around a target parameter sample (10) of a parameterizable simulation model for a product according to any one of claims 1 to 12.

14. 13. A computer program designed to perform a computer-implemented method (100) for determining a tolerance region (30) within an operational design domain (ODD) around a target parameter sample (10) of a parameterizable simulation model for a product according to any one of claims 1 to 12.

15. A computer readable medium or signal storing and / or embodying a computer program according to claim 14.

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