1. A computer-implemented method for automatically generating at least one test plan (100) for measuring at least one measured object (102), wherein the method comprises the steps of:

a) providing a desired data set (104) of the object under test (102);

b) providing a starting pattern (122), wherein the providing comprises generating a partition (124), wherein the generating of the partition (124) comprises applying at least one partition function, wherein the partition (124) has a plurality of partition indices (126);

c) generating a target pattern (130), wherein the generation of the target pattern (130) comprises a comparison between a desired data set (104) and the partition (124), wherein in case the partition (124) deviates from the desired data set (104), at least one partition index (126) is adapted;

d) creating at least one element having at least one piece of pattern information in the test plan (100) according to the target pattern (130).

2. The method according to the preceding claim, wherein the desired data set (104) is generated from and/or comprises at least one model of the object under test (102), wherein the model of the object under test (102) is and/or comprises a CAD model.

3. The method according to any one of the preceding claims, wherein the desired dataset (104) has a plurality of elements (106), wherein the desired dataset (104) has a special geometry (112), a regular geometry (110), or a combination of special geometry (112) and regular geometry (110).

4. The method according to one of the preceding claims, wherein the adapting of the at least one partition index (126) comprises masking the partition index (126).

5. The method according to one of the preceding claims, wherein the desired data set (104) has a pattern (114) comprising missing locations (116), wherein the comparing comprises removing and/or deleting all partition indices (124) corresponding to the missing locations (116) in the desired data set (104).

6. The method according to one of the preceding claims, wherein the desired data set (104) comprises an outer contour (118).

7. The method according to the preceding claim, wherein the comparison between the desired dataset (104) and the partition (124) comprises involving the outer contour (118), wherein the comparison comprises removing and/or deleting all partition indices (126) outside the outer contour (118).

8. The method of one of the preceding claims, wherein the comparison comprises a pattern comparison between the expected data set (104) and the partition (124).

9. The method according to the preceding claim, wherein no adapted partition index (126) is created in the test plan (100).

10. A measurement method for measuring at least one measured object (102), wherein the measurement method comprises generating a test plan (100) according to one of the preceding claims relating to a method for generating a test plan (100), wherein the measurement method has at least one measurement step, wherein the measurement step relates to performing a measurement of the measured object (102) according to the test plan (100).

11. A computer program which, when executed on a computer or computer network, performs one of the following configurations of the method: method for generating a test plan according to one of the preceding claims relating to a method for generating a test plan (100), in particular method steps a) to d); and/or a measuring method according to the preceding claim.

12. A computer program product comprising program code means stored on a machine readable medium for performing one of the following methods when the program is executed on a computer or a computer network: method for generating a test plan (100) according to one of the preceding claims referring to a method for generating a test plan; and/or a measuring method according to the preceding claim.

13. Coordinate measuring machine (136) for measuring at least one measured object (102), wherein the coordinate measuring machine (136) comprises at least one data processing unit (150), wherein the data processing unit (150) is configured to generate at least one test plan (100) for measuring the measured object (102), wherein the data processing unit (150) comprises at least one interface (152) configured to provide at least one desired data set (104) and at least one start pattern (122) of the measured object (102), wherein the providing comprises generating a partition (124), wherein the generating of the partition (124) comprises applying at least one partition function, wherein the partition (124) has a plurality of partition indices (126), wherein the data processing unit (150) is configured to generate a target pattern (130), wherein the data processing unit (150) is configured to compare the desired data set (104) with the partition (124), wherein the data processing unit (150) is configured to adapt at least one partition index (126) if the partition (124) deviates from the desired data set (104), wherein the data processing unit (150) is configured to create at least one element having at least one piece of pattern information in the test plan (100) according to the target pattern (130).

14. The coordinate measuring machine (136) according to the preceding claim, wherein the coordinate measuring machine (136) has at least one controller (154) configured to control at least one component of the coordinate measuring machine (136) to measure the measurand (102) according to the test plan (100).

15. The coordinate measuring machine (136) according to one of the preceding claims directed to a coordinate measuring machine (136), wherein the coordinate measuring machine (136) is configured to perform the method of: method for generating a test plan (100) according to one of the preceding claims referring to a method for generating a test plan (100); and/or a measuring method according to claim 10.

Background

Various methods and devices for measuring test features of a measured object, in particular of a measuring geometry, are known from the prior art. These methods typically follow test plans that need to be generated or written in advance. The test plan determines, among other things, what is to be measured and how. To generate a test plan, a CAD model of the object under test is usually imported into a computer-based development environment provided for this purpose, and the model is converted into a test plan. Measuring software, for example, from ZeissThe CAD data set can be automatically converted into a test plan.

When there are a large number of geometric elements, one option is that these geometric elements are each produced separately and each created as a separate element in the test plan. The measurement and evaluation strategies and the test features must also be selected and assigned individually accordingly.

Alternatively, the geometric elements may be collectively defined using patterns. In particular, the pattern may be advantageous because only one of these geometries needs to be processed and all other geometries are determined using the pattern position. Only one element is created using the pattern information in the test plan. Measurement and evaluation strategies may be selected and assigned according to a pattern. Such patterns are manually produced in known methods and equipment. It is true that the pattern can be generated by a so-called partition function. However, not all the partition positions (also referred to as indices) of the pattern in the measurement sequence have to be considered at all times, for example because the CAD model does not provide features here. Therefore, these unwanted partition locations must be masked or deleted. This action can only be performed manually so far. This can be complicated and also prone to errors. Especially considering the use of a very large number (e.g. thousands) of geometric elements and associated test features, processing the pattern can become very difficult to manage and time consuming.

Disclosure of Invention

Object of the Invention

It is therefore desirable to provide a computer implemented method, a computer program, a measuring method and a coordinate measuring machine for automatically generating a test plan which at least largely avoid the disadvantages of the known methods and devices. In particular, it is an object to reduce the amount of work and time involved in generating a test plan and to reduce the likelihood of errors in generating a test plan.

Summary of The Invention

This object is achieved by a method and a device having the features of the independent patent claims. Advantageous developments which can be realized individually or in any combination are presented in the dependent claims.

In the following, the terms "exhibit," "have," "include," or any grammatical deviations thereof, are used in a non-exclusive manner. Thus, these terms may refer to the absence of other features than those introduced by these terms, or the presence of one or more other features. For example, the expressions "a exhibits B", "a has B", "a includes B", or "a includes B" may refer to the case where no other elements than B are provided in a (that is, the case where a consists only of B), and the case where one or more other elements are provided in a in addition to B, such as elements C, elements C and D, or even other elements.

Furthermore, it is noted that the terms "at least one" and "one or more" and grammatical modifications thereof, if used in conjunction with one or more elements or features and intended to convey the fact that the elements or features may be provided singly or in multiples, are often used only once, for example, when the feature or element is first introduced. When subsequently referring again to such features or elements, the corresponding terms "at least one" or "one or more" are generally not used anymore, without limiting the possibilities that such features or elements may provide individually or in multiple.

Furthermore, in the following, the terms "preferably", "specifically", "for example" or similar terms are used in combination with optional features, whereby alternative embodiments are not limited. In this regard, the features introduced by these terms are optional features and are not intended to limit the scope of the claims, especially of the independent claims, by these features. In this regard, the invention may also be implemented using other configurations, as will be appreciated by those skilled in the art. Similarly, features introduced by "in one embodiment of the invention" or "in one exemplary embodiment of the invention" are to be understood as optional features, whereby it is not intended to limit the scope of protection of the independent claims or of alternative configurations. Furthermore, all possibilities of combining features introduced by these introductory expressions with other features, whether optional or not, are not intended to be affected by the introductory expressions.

In a first aspect of the invention, a computer-implemented method for automatically generating at least one test plan for measuring at least one measured object is proposed.

The term "computer-implemented" as used herein is a broad term and is intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer, without limitation, to a method involving at least one computer and/or at least one computer network. The computer and/or computer network may comprise at least one processor configured to perform at least one method step of the inventive method. In each case, each of these method steps is preferably performed by a computer and/or a network of computers.

The method may be performed fully automatically, and in particular without user interaction. The term "automatically" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer, without limitation, to processes that are performed entirely by a computer and/or computer network and/or machine, particularly without user interaction and/or human intervention. To initiate the process, user interaction may be required.

The term "test plan" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to the specification of at least one test, particularly to the result of a test plan, without limitation. For example, the test may be a quality test. The test plan may have multiple elements, such as test specifications, test instructions, and a test sequence plan. The test specification may establish test features. The test instructions may include instructions for performing a test. The test sequence plan may establish the order of the tests. Further, the test plan may include information that identifies the test document.

The term "test feature" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly denote, without limitation, a feature of the at least one measurement element to be determined and/or to be examined and/or to be tested. Determining and/or testing the test features may include determining and/or testing dimensional deviations and/or shape deviations and/or positional deviations. The test feature may be a feature selected from the group consisting of: at least one length; at least one angular dimension, at least one surface parameter, shape, orientation.

The term "measurement element" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to a predetermined or predeterminable geometric element without limitation. The measuring element may be, for example, a geometric element selected from the group consisting of a circle, a cylinder, a rectangle, a straight line, or other element having a regular geometric shape. The test features can be determined and/or checked, for example, by measuring points and/or lines and/or areas of the object under test.

The term "measurand" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to an arbitrarily shaped object to be measured without limitation. For example, the object under test may be selected from the group consisting of a test object, a workpiece, and a part to be measured. The term "measurement measurand" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to capturing the geometric dimensions of an object by means of position measurement and/or distance measurement and/or angle measurement without limitation.

The computer-implemented method for automatically generating at least one test plan comprises the following steps, which may be performed, as an example, in the following order. In addition, one or more of these method steps may be performed once or several times in a repeated fashion. In addition, two or more of these method steps can be carried out simultaneously or at least overlapping in time. The method may also include other method steps not listed.

The method comprises the following steps:

a) providing a desired data set of the object under test;

b) providing a starting pattern, wherein the providing comprises generating a partition, wherein the generating of the partition comprises applying at least one partition function to the starting pattern, wherein the partition has a plurality of partition indices;

c) generating a target pattern, wherein the generation of the target pattern comprises a comparison between a desired data set and the partition, wherein at least one partition index is adapted in case the partition deviates from the desired data set;

d) at least one element having at least one piece of pattern information is created in the test plan according to the target pattern.

Steps a) to d) can each be carried out completely automatically. The term "fully automatic" as used herein is a broad term that is intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer without limitation to the fact that each of steps a) to d) is performed entirely by a computer and/or a computer network and/or a machine, especially without user interaction and/or human intervention. User interaction may be required in order to initiate each of the various steps. The user interaction may include selecting at least one data set and/or entering at least one command.

The term "desired dataset" (also referred to as a nominal dataset) as used herein is a broad term that is intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer without limitation to a data set that represents as accurately as possible the features of a real measurand. The desired data set may be generated from and/or may include at least one model of the object under test. For example, the model of the measurand may be and/or include a CAD model. The CAD model may be three-dimensional or two-dimensional. The CAD model may include product and manufacturing information for the object under test. Measurement data obtained, for example, by testing and/or capturing at least one feature of the measurand using a coordinate measuring machine, and/or additional information relating to the configuration of the measurand may alternatively or additionally be considered when generating the desired data set.

The desired dataset may have a plurality of elements, in particular a plurality of geometric elements. The term "geometric element" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to an element of a desired data set that is a feature to be inspected, without limitation. For example, the geometric elements may each be a regular geometric shape, such as a circle or a rectangle. Alternatively or additionally, the desired data set may have at least one special geometry, for example at least one curve, in particular a plurality of curves. The desired dataset may have a special geometry, a regular geometry, or a combination of special and regular geometries.

For example, the desired data set may have a pattern. The elements of the desired data set may form a pattern. For example, the pattern may be a pattern comprising a plurality of rectangles, triangles, hexagons or a plurality of circles. The term "pattern" as used herein is a broad term intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer, without limitation, to a plurality of elements that are substantially regularly and/or periodically and/or constantly arranged with respect to each other. "substantially regularly and/or periodically and/or constantly arranged with respect to each other" may be understood to mean completely regular and/or periodic and/or constant arrangements and arrangements in which the areas of the pattern deviate from regular and/or periodic and/or constant arrangements are conceivable. For example, the pattern may have missing positions.

The desired data set may additionally comprise an outer contour, which is also referred to as an edge boundary. For example, the outer contour may be defined by capturing the geometry on a coordinate measuring machine. The outer contour may be a rotationally symmetric edge boundary. The outer contour may in particular be spherical or, from a 2D point of view, circular. For example, the outer contour may be an outer circle. Other outer contours, in particular non-rotationally symmetrical contours or linear contours, are also envisaged.

The term "providing a desired data set" as used herein is a broad term intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to, without limitation, loading, particularly importing and/or generating a desired data set.

The term "starting pattern" as used herein is a broad term intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer without limitation to a pattern, for example generated by a data processing unit, which pattern is to be adapted to a desired data set. The starting pattern may comprise a plurality of geometric elements. For example, the geometric elements of the starting pattern may each be a regular geometric shape, such as a circle or a rectangle. The providing of the starting pattern comprises generating at least one partition, wherein the generating of the partition comprises applying at least one partition function. The partition has a plurality of partition indices. Methods for generating partitions are known to those skilled in the art. The partitions may be and/or include rings by means of geometric elements. For example, in this case, the partition or ring may be a one-dimensional ring or a multi-dimensional ring. These may correspond to linear patterns, rotationally symmetric patterns, or other patterns. Colloquially, if a partition replicates the original geometry according to a pattern, the partition may be identical to a ring by means of a geometric element.

The term "partition" as used herein is a broad term that is intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to, without limitation, a plurality of geometric elements, such as a plurality of circular elements, for example, generated by a data processing unit. The position of each of the geometric elements in the partition may be defined by a partition function. The partitions may be one-dimensional partitions, two-dimensional partitions, or three-dimensional partitions. The partitions may be linear partitions, rotational partitions, or offset polar partitions. The generation of the linear partition may include defining a first partition index, where the defining includes determining a location and a type of the partition index (e.g., a circle). The generation of linear partitions may include determining a total number of partition indices in the x-direction and/or the y-direction. The generation of the linear partition may include determining an offset in at least one direction by a constant value. The partitions may be generated on the basis of the first partition index by using the total number and an offset from the partition function. The partition function of the linear partition may be a linear function.

The term "partition index" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to individual elements of a partition without limitation. The partition index may also be referred to as a partition element. Elements may be created from partition indexes in a test plan. For example, the partition index may represent measured elements that are intended to be measured in a measurement method.

The method according to the invention proposes not to delete manually the missing elements in the real measurand, but to use a software algorithm to perform a matching between the desired dataset and the starting pattern to automatically delete the missing elements from the starting pattern and thus to generate a target pattern on the basis of which the elements of the test plan are created. In this case, it is desirable that the matching of the data set and the starting pattern is not effected in particular manually, but rather fully automatically. The term "target pattern" as used herein is a broad term intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to a pattern adapted to a desired data set without limitation.

The adapting of the at least one partition index may comprise masking the partition index. The term "adapted" as used herein is a broad term that is intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to, without limitation, changing, particularly removing and/or deleting, a partition index, also referred to as masking. When generating the test plan, the masked partition index is ignored.

The desired data set may have a pattern containing missing locations. The comparison may include removing and/or deleting all partition indexes corresponding to the missing locations in the desired dataset.

The comparison between the desired data set and the partition may include relating to an outer contour of the desired data set. The comparison may include removing and/or deleting all partition indices outside or inside the outer contour. For example, the outer contour may be an outer circle, and all partition indices outside the outer circle may be deleted and/or removed. Adding partition indexes is also contemplated.

The comparison between the desired data set and the partition may specifically comprise a pattern comparison between the desired data set and the partition. The pattern comparison may include identifying and/or selecting a pattern location in the desired data set. The pattern comparison may include identifying pattern locations in the partitions that correspond to the identified and/or selected pattern locations in the desired data set. The pattern comparison may include determining whether elements of the desired data set are present at the identified and/or selected pattern locations in the desired data set. Pattern matching may include determining whether a partition index exists at a pattern location corresponding to the identified and/or selected pattern location in the desired data set. In case a lack of correspondence of pattern positions in the data set and the presence or absence of corresponding pattern positions in the partitions is desired, the pattern comparison may specifically adapt the partitions by masking the partition index.

The term "pattern information" as used herein is a broad term intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to information describing a pattern, particularly a target pattern, without limitation. The pattern information may include information about the distribution of pattern elements, the positions of elements in the pattern, the types of elements, or the types of patterns. The type of pattern may be e.g. a partition with a polar offset, and/or a 1d linear partition or a 2d linear partition or a rotational partition or a partition with a list of positions.

The term "create" element in a test plan as used herein is a broad term intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to adding and/or referring to elements in a test plan without limitation. No adapted partition index is created in the test plan.

In another aspect, a measurement method for measuring at least one measured object is proposed. The measurement method comprises generating a test plan using one of said configurations of the method for generating a test plan according to the invention. With regard to the definition and configuration of the measurement method, reference is made to the definition and configuration with regard to the method for generating the test plan. The measuring method has at least one measuring step. The measuring step involves performing measurements of the object under test according to a test plan. In particular, a coordinate measuring machine may be used in the measuring method. The coordinate measuring machine may be a coordinate measuring machine selected from the group consisting of: tactile coordinate measuring machines, optical coordinate measuring machines, laser-based coordinate measuring machines. Alternatively, the measurement method may be used in a CT scanner system.

Furthermore, in the context of the present invention, a computer program is proposed which, when executed on a computer or a computer network, performs one of the configurations of at least one of the methods according to the invention, in particular method steps a) to d).

Furthermore, in the context of the present invention, a computer program is proposed, which comprises program code means for performing one of the configurations of the method according to the invention when the program is executed on a computer or a computer network. In particular, the program code means may be stored on a computer readable data medium.

The terms "computer-readable data medium," "data storage device," and "computer-readable storage medium" as used herein may particularly refer to a non-transitory data storage device, such as a hardware data storage medium having computer-executable instructions stored thereon. The computer-readable data medium or computer-readable storage medium may be or specifically include a storage medium such as a Random Access Memory (RAM) and/or a Read Only Memory (ROM).

Furthermore, in the context of the present invention, a data medium is proposed which stores a data structure which, after being loaded into the underlying memory and/or main memory of a computer or computer network, can carry out one of the configurations of the method according to the invention.

In the context of the present invention, there is also proposed a computer program product comprising program code means stored on a machine-readable medium for performing one of the configurations of the method according to the invention when the program is executed on a computer or a computer network.

In this context, a computer program product is understood to mean a product which is a commercially available product. In principle, it may be obtained in any form, for example on paper or on a computer-readable data medium, and in particular it may be distributed via a data transmission network.

Finally, in the context of the present invention a modulated data signal is proposed, which comprises instructions executable by a computer system or a computer network for performing a method according to one of the described embodiments.

With regard to the computer-implemented aspects of the invention, one or more or even all method steps of a method according to one or more of the configurations proposed herein may be performed by means of a computer or a network of computers. Thus, in general, any of these method steps (including the provision and/or manipulation of data) may be performed by means of a computer or a computer network. In general, these steps may include any of the method steps, not including steps that require manual operations, such as user selection of a data set.

In another aspect, in the context of the present invention, a coordinate measuring machine is proposed for measuring at least one measured object with an arbitrary sensor or CT scanner.

The term "coordinate measuring machine" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to, without limitation, a device for determining at least one coordinate of a measured object. The coordinate measuring machine may be a gantry measuring machine or a bridge measuring machine. The coordinate measuring machine may have a measuring table for placing at least one workpiece to be measured. The coordinate measuring machine may include at least one gantry including at least one first column, at least one second column, and a beam connecting the first column and the second column. At least one column selected from the first column and the second column may be mounted to be movable on the measurement table. The horizontal direction may be a direction along the y-axis. The coordinate measuring machine may have a coordinate system, for example a cartesian coordinate system or a spherical coordinate system. Other coordinate systems are also contemplated. The x-axis may extend perpendicular to the y-axis in the plane of the support surface of the measurement table. The z-axis (also referred to as the longitudinal axis) may extend in a vertical direction perpendicular to the plane of the support surface. The post may extend along the z-axis. The beam may extend along the x-axis. For example, the coordinate measuring machine may be a tactile coordinate measuring machine. The term "tactile" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to touch properties without limitation. The tactile coordinate measuring machine may scan the measurand to make measurements. The haptic coordinate measuring machine can in particular determine whether a defined tolerance of the measured object is adhered to.

The coordinate measuring machine comprises at least one data processing unit. The term "data processing unit" as used herein is a broad term intended to be given its customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may refer specifically, without limitation, to any logic circuitry for performing the basic operations of a computer or system, and/or to generally a device configured to perform calculations or logic operations. The data processing unit may have a processor or a processor unit. The data processing unit may have, for example, an Arithmetic and Logic Unit (ALU), a Floating Point Unit (FPU), such as a math coprocessor or a numerical coprocessor, a plurality of registers, and a main memory, such as a cache main memory. The data processing unit may have a multicore processor. The data processing unit may have a Central Processing Unit (CPU). Alternatively or additionally, the data processing unit may have one or more application specific integrated circuits and/or one or more Field Programmable Gate Arrays (FPGAs) or the like.

The data processing unit is configured to generate at least one test plan for measuring the object under test.

The data processing unit comprises at least one interface configured to provide at least one desired data set and at least one starting pattern of the object under test. The providing includes generating the partition. The generation of the partition includes applying at least one partition function. The partition has a plurality of partition indices.

The term "interface" as used herein is a broad term that is intended to be given a customary and customary meaning as understood by those skilled in the art. The terms are not limited to a specific or suitable meaning. The term may particularly refer to, without limitation, an element or part of a data processing unit that is configured to transmit information. The interface may be a communication interface, in particular a data interface, configured to receive data from another device and/or from a user and/or to transmit data from the interface to other components of the data processing unit and/or to an external device. The interface may comprise at least one electronic interface and/or a human-machine interface, e.g. an input/output device, such as a display and/or a keyboard. The interface may have at least one data connection, for example a bluetooth connection, an NFC connection or another connection. The interface may have at least one network or be part of a network. The interface may have at least one internet port, at least one USB port, at least one drive, or a web interface.

The data processing unit is configured to generate a target pattern. The data processing unit is configured to compare the desired data set with the partition. The data processing unit is configured to adapt the at least one partition index in case the partition deviates from the desired data set. The data processing unit is configured to create at least one element having at least one piece of pattern information in the test plan according to the target pattern.

The coordinate measuring machine has at least one controller configured to control at least one component of the coordinate measuring machine to measure a measurand according to the test plan.

The coordinate measuring machine may be configured to perform one of the configurations of the method for generating a test plan according to the present invention and/or one of the configurations of the measuring method according to the present invention. With regard to the definition and configuration of the measurement method, reference is made to the definition and configuration with regard to the method for generating the test plan.

The proposed device and method have a number of advantages compared to known devices and methods. Thus, the increased degree of production automation of the test plan can reduce time involvement and manpower. Furthermore, the possibility of error can also be reduced compared to manual processing.

In summary, without limiting further possible configurations, the following embodiments are proposed:

example 1: a computer-implemented method for automatically generating at least one test plan for measuring at least one measured object, wherein the method comprises the steps of:

a) providing a desired data set of the object under test;

b) providing a starting pattern, wherein the providing comprises generating a partition, wherein the generating of the partition comprises applying at least one partition function, wherein the partition has a plurality of partition indices;

c) generating a target pattern, wherein the generation of the target pattern comprises a comparison between a desired data set and the partition, wherein at least one partition index is adapted in case the partition deviates from the desired data set;

d) at least one element having at least one piece of pattern information is created in the test plan according to the target pattern.

Example 2: the method according to the previous embodiment, wherein the desired data set is generated from and/or comprises at least one model of the measurand, wherein the model of the measurand is and/or comprises a CAD model.

Example 3: the method of any of the preceding embodiments, wherein the desired dataset has a plurality of elements, wherein the desired dataset has a special geometry, a regular geometry, or a combination of special and regular geometries.

Example 4: the method according to one of the preceding embodiments, wherein the adapting of the at least one partition index comprises masking the partition index.

Example 5: the method according to one of the preceding embodiments, wherein the desired dataset has a pattern comprising missing locations, wherein the comparing comprises removing and/or deleting all partition indices corresponding to the missing locations in the desired dataset.

Example 6: the method according to one of the preceding embodiments, wherein the desired dataset comprises an outer contour.

Example 7: the method according to the previous embodiment, wherein the comparison between the desired dataset and the partition comprises involving the outer contour, wherein the comparison comprises removing and/or deleting all partition indices outside or inside the outer contour.

Example 8: the method according to one of the preceding embodiments, wherein the comparison comprises a pattern comparison between the expected data set and the partition.

Example 9: the method according to the previous embodiment, wherein no adapted partition index is created in the test plan.

Example 10: a measuring method for measuring at least one measured object, wherein the measuring method comprises generating a test plan according to one of the preceding embodiments relating to a method for generating a test plan, wherein the measuring method has at least one measuring step, wherein the measuring step involves performing a measurement of the measured object according to the test plan.

Example 11: a computer program which, when executed on a computer or computer network, performs one of the following configurations of the method: method for generating a test plan according to one of the preceding embodiments relating to a method for generating a test plan, in particular method steps a) to d); and/or a method of measurement according to the previous embodiment.

Example 12: a computer program product comprising program code means stored on a machine readable medium for performing one of the following methods when the program is executed on a computer or a computer network: a method for generating a test plan according to one of the preceding embodiments relating to a method for generating a test plan; and/or a method of measurement according to the previous embodiment.

Example 13: coordinate measuring machine for measuring at least one measured object, wherein the coordinate measuring machine comprises at least one data processing unit, wherein the data processing unit is configured to generate at least one test plan for measuring the measured object, wherein the data processing unit comprises at least one interface configured to provide at least one desired dataset and at least one starting pattern of the measured object, wherein the providing comprises generating a partition, wherein the generating of the partition comprises applying at least one partition function, wherein the partition has a plurality of partition indices, wherein the data processing unit is configured to generate a target pattern, wherein the data processing unit is configured to compare the desired dataset with the partition, wherein the data processing unit is configured to adapt at least one partition index in case the partition deviates from the desired dataset, wherein the data processing unit is configured to create at least one element having at least one piece of pattern information in the test plan according to the target pattern.

Example 14: the coordinate measuring machine according to the previous embodiment, wherein the coordinate measuring machine has at least one controller configured to control at least one component of the coordinate measuring machine to measure the measurand according to the test plan.

Example 15: the coordinate measuring machine according to one of the preceding embodiments relating to a coordinate measuring machine, wherein the coordinate measuring machine is configured to perform the method of: a method for generating a test plan according to one of the preceding embodiments relating to a method for generating a test plan; and/or the measurement method according to embodiment 10.

Drawings

Further details and features of the invention will become apparent from the following description of exemplary embodiments, particularly in conjunction with the dependent claims. In this case, the respective features may be implemented by themselves only or as a plurality in combination with each other. The invention is not limited to these exemplary embodiments. These exemplary embodiments are schematically illustrated in the drawings. In this case, identical reference numbers in the individual figures denote identical or functionally identical elements or elements which correspond to one another in terms of their function.

In detail:

fig. 1 shows an exemplary embodiment of a method according to the present invention;

FIGS. 2A and 2B illustrate two exemplary embodiments of a desired data set;

FIG. 3 illustrates an exemplary embodiment of a partition;

fig. 4A and 4B show two exemplary embodiments of the adaptation of the starting pattern;

FIG. 5 shows a schematic illustration of an exemplary embodiment of a coordinate measuring machine according to the present invention; and is

Fig. 6A to 6E show further exemplary embodiments of the partitioning.

Detailed Description

Fig. 1 shows a flow chart of an exemplary embodiment of a computer-implemented method according to the present invention for automatically generating at least one test plan 100 for measuring at least one measured object 102, which is not depicted in the figure. The method may involve at least one computer and/or at least one computer network. The computer and/or computer network may comprise at least one processor configured to perform at least one method step of the inventive method. In each case, each of these method steps is preferably performed by a computer and/or a network of computers. The method may be performed fully automatically, and in particular without user interaction.

The test plan 100 may be a specification of at least one test, and specifically a result of the test plan. For example, the test may be a quality test. The test plan 100 may have multiple elements, such as test specifications, test instructions, and a test sequence plan. The test specification may establish test features. The test feature can be a feature of the at least one measurement element 103 to be determined and/or to be checked and/or to be tested. Determining and/or testing the test features may include determining and/or testing dimensional deviations and/or shape deviations and/or positional deviations. The test feature may be a feature selected from the group consisting of: at least one length; at least one angular dimension, at least one surface parameter, shape, orientation. The measurement elements 103 may be predetermined or predeterminable geometric elements. The measurement element 103 may be, for example, a geometric element selected from the group consisting of a circle, a cylinder, a rectangle, a straight line, or other element having a regular geometric shape. The test features may be determined and/or examined, for example, by measuring points and/or lines and/or areas of the object 102. The test instructions of the test plan may include instructions for performing a test. The test sequence plan may establish the order of the tests. Further, the test plan 100 may include information that identifies test documents.

The object under test 102 may be an object of any shape to be measured. For example, the measurand 102 may be selected from the group consisting of a test object, a workpiece, and a part to be measured. The measurement of the measurand 102 may include capturing the geometry of the measurand 102 by means of a position measurement and/or a distance measurement and/or an angle measurement.

The method includes providing a desired data set 104 of the object under test 102. The providing of the desired data set 104 may include loading, specifically importing and/or generating the desired data set 104. It is desirable that the data set 104 be and/or include a data set that represents as accurately as possible the characteristics of the real measurand 102. The desired data set 104 may be generated from at least one model of the object under test 102 and/or may include at least one model of the object under test 102. For example, the model of the object under test 102 may be and/or include a CAD model. The CAD model may be three-dimensional or two-dimensional. The CAD model may include product and manufacturing information for the object 102. Measurement data obtained, for example, by testing and/or capturing at least one feature of the measurand 102 using the coordinate measuring machine 136, and/or additional information related to the configuration of the measurand 102 may alternatively or additionally be considered when generating the desired data set 104.

The desired data set 104 may have a plurality of elements 106, specifically a plurality of geometric elements 108. The geometric element 108 may include a feature to be inspected. For example, the geometric elements 108 may each be a regular geometric shape 110, such as a circle or a rectangle. Alternatively or additionally, the desired dataset 104 may have at least one particular geometry 112, such as at least one curve, in particular a plurality of curves. The desired dataset 104 may have a special geometry 112, a regular geometry 110, or a combination of special geometry 112 and regular geometry 110.

For example, the desired data set 104 may have a pattern 114. The elements 106 of the desired data set 104 may form a pattern 114. For example, the pattern 114 may be a pattern 114 that includes a plurality of rectangles, triangles, hexagons, or a plurality of circles. The pattern 114 may have a plurality of elements 106 that are substantially regularly and/or periodically and/or constantly arranged with respect to each other. Completely regular and/or periodic and/or constant arrangements are contemplated as well as arrangements in which regions of the pattern 114 deviate from regular and/or periodic and/or constant arrangements. For example, the pattern 114 may have missing locations 116.

The desired data set 104 may additionally include an outer contour 118, which is also referred to as an edge boundary. For example, outer contour 118 may be defined by capturing a geometric shape on coordinate measuring machine 136. Outer contour 118 may be a rotationally symmetric edge boundary. Outer contour 118 may specifically be spherical or, from a 2D perspective, circular. For example, outer contour 118 may be outer circle 120. Other outer contours 118 are also contemplated, specifically non-rotationally symmetric contours or linear contours.

The method includes providing a starting pattern 122. The providing includes generating the partition 124. The generation of partition 124 includesAt least one partition function is applied. Partition 124 has a plurality of partition indices 126. The starting pattern 122 may be, for example, a pattern 114 generated by the data processing unit 150, which pattern is to be adapted to the desired data set 104. The starting pattern 122 may include a plurality of geometric elements 108. For example, the geometric elements 108 of the starting pattern 122 may each be a regular geometric shape, such as a circle or a rectangle. The providing of the starting pattern 122 comprises generating at least one partition 124. Partition 124 has a plurality of partition indices 126. Methods for generating partitions 124 are known to those skilled in the art, for example from a software (e.g. from zeiss corporation) Or from applicable training files. The partition 124 may have a plurality of geometric elements 108, e.g. a plurality of circular elements, e.g. generated by the data processing unit 150. The position of each of the geometric elements 108 in the partition 124 may be defined by a partition function. The partition index 124 may be an element 106 of the partition 124. The elements 106 may be created from the partition index 126 in the test plan 100. For example, the partition index 126 may represent the measured element 103 that is intended to be measured in a measurement method.

The method includes generating a target pattern 130. The generation of the target pattern 130 includes a comparison between the desired data set 104 and the partitions 124. In case a partition 124 deviates from the desired data set 104, at least one partition index 126 is adapted. The method according to the invention proposes not to manually delete the missing elements 106 in the real measurand 102, but to use a software algorithm to perform a matching between the desired dataset 104 and the starting pattern 122 to automatically delete the missing elements 106 from the starting pattern 122 and thus to generate a target pattern 130 on the basis of which the elements 106 of the test plan 100 are created. In this case, it is desirable that the matching of the data set 104 and the starting pattern 122 is not specifically effected manually, but rather fully automatically. The target pattern 130 may be the pattern 114 adapted to the desired data set 104.

The adaptation may include changing the partition index 126. In particular, the adaptation may comprise removing and/or deleting thereof, also referred to as masking. Masked partition index 132 is ignored when generating test plan 100.

The desired data set 104 may have a pattern 114 with missing locations 116. The comparison may include removing and/or deleting all partition indexes 126 corresponding to the missing locations 116 in the desired data set 104.

The comparison between the desired data set 104 and the partition 124 may include referencing the outer contour 118 of the desired data set 104. The comparison may include removing and/or deleting all partition indices 126 outside or inside outer contour 118. For example, outer contour 118 may be outer circle 120, and all partition indices 126 outside of outer circle 120 may be deleted and/or removed. The addition of partition index 126 is also contemplated.

The comparison between the desired data set 104 and the partitions 124 may specifically include a pattern comparison between the desired data set 104 and the partitions 124. The pattern comparison may include identifying and/or selecting a pattern location in the desired data set 104, for example, by using an image processing algorithm. The pattern comparison may include identifying pattern locations in the partition 124 that correspond to the identified and/or selected pattern locations in the desired data set. The pattern comparison may include determining whether an element 106 of the desired data set 104 is present at the identified and/or selected pattern location 134 in the desired data set 104. Pattern matching may include determining whether a partition index 126 exists at a pattern location 134 corresponding to the identified and/or selected pattern location 134 in the desired data set 104. In the event of a lack of correspondence in the presence or absence of a pattern location 134 in the expected data set 104 and a corresponding pattern location 134 in the partition 124, the pattern comparison may specifically adapt the partition 124 by masking the partition index 126.

The method further includes creating at least one element having at least one piece of pattern information in the test plan 100 according to the target pattern 130. The pattern information may have information describing the target pattern 130. The pattern information may include information about the distribution of the elements 106 of the target pattern 130, the location of the elements 106 in the target pattern 130, the type of the elements 106 of the target pattern 130, or the type of the pattern. The type of pattern may be e.g. a partition with polar offset, or a 1d linear partition or a 2d linear partition or a rotational partition or a partition with a list of positions. Creating an element in the test plan may include adding and/or referring to the element in the test plan 100. No adapted partition index 126 is created in test plan 100.

Fig. 2A and 2B illustrate two exemplary embodiments of the desired data set 104. In the exemplary embodiment of fig. 2A, it is desirable that the data set 104 include a pattern 114 that includes a plurality of circles. The desired data set 104 in fig. 2A has an outer contour 118 in the form of a rotationally symmetric edge boundary, in particular an outer circle 120. In the exemplary embodiment of fig. 2B, it is desirable that the data set 104 include a pattern 114 that includes a plurality of rectangles. The desired data set 104 in fig. 2B has an outer contour 118 in the form of a non-rotationally symmetric or non-linear curve.

Fig. 3 illustrates an exemplary embodiment of partition 124. The partition 124 includes a plurality of circular partition indices 126. Furthermore, an outer contour 118 in the form of an outer circle 120 is shown.

Fig. 4A and 4B illustrate an exemplary embodiment of the adaptation of the start pattern 122. Fig. 4A illustrates the adaptation of the starting pattern 122 of the desired data set 104 from fig. 2A. Fig. 4B illustrates the adaptation of the starting pattern 122 of the desired data set 104 from fig. 2B. The presence or absence of the element 106 at the corresponding pattern position in the starting pattern 122 and the expected data set 104 is compared. In this case, the missing locations 116 at the pattern locations 134 are masked, which is depicted in fig. 4A and 4B in the form of an "X".

Fig. 5 shows a schematic illustration of an exemplary embodiment of a coordinate measuring machine 136 for measuring at least one object under test 102 according to the present invention. The coordinate measuring machine 136 may be a device for determining at least one coordinate of the object under test 102. The coordinate measuring machine may be a gantry measuring machine or a bridge measuring machine. The coordinate measuring machine 136 may have a measuring station 138 for placing at least one workpiece to be measured. The coordinate measuring machine may have at least one gantry 140 including at least one first upright 142, at least one second upright 144, and a cross beam 146 connecting the first upright 142 and the second upright 144. At least one column selected from the first column 142 and the second column 144 may be mounted for movement on the measurement table 138. The horizontal direction may be a direction along the y-axis. Coordinate measuring machine 136 may have a coordinate system 148, such as a cartesian coordinate system or a spherical coordinate system. Other coordinate systems are also contemplated. The x-axis may extend perpendicular to the y-axis in the plane of the support surface of the measurement table 138. The z-axis (also referred to as the longitudinal axis) may extend in a vertical direction perpendicular to the plane of the support surface. The posts 142 and 144 may extend along the z-axis. The beam 146 may extend along the x-axis.

Coordinate measuring machine 136 may be a tactile coordinate measuring machine, as shown in FIG. 5. The tactile coordinate measuring machine may scan the measurand 102 for measurements. The haptic coordinate measuring machine can in particular determine whether a defined tolerance of the measured object is adhered to. However, coordinate measuring machines 136 with other sensors and based on other sensor principles or CT scanners are also contemplated.

The coordinate measuring machine comprises at least one data processing unit 150. The data processing unit 150 may have any logic circuitry for performing the basic operations of a computer or system, and/or generally may be configured as a device that performs calculations or logical operations. The data processing unit may have a processor or a processor unit. The data processing unit may have, for example, an Arithmetic and Logic Unit (ALU), a Floating Point Unit (FPU), such as a math coprocessor or a numerical coprocessor, a plurality of registers, and a main memory, such as a cache main memory. The data processing unit 150 may have a multicore processor. The data processing unit 150 may have a Central Processing Unit (CPU). Alternatively or additionally, the data processing unit 150 may have one or more application specific integrated circuits and/or one or more Field Programmable Gate Arrays (FPGAs), etc.

The data processing unit 150 is configured to generate at least one test plan 100 for measuring the object under test 102.

The data processing unit 150 comprises at least one interface 152 configured to provide at least one desired data set 104 and at least one starting pattern 122 of the object under test 102. The providing includes generating the partition 124. The generation of partition 124 includes applying at least one partition function. Partition 124 has a plurality of partition indices 126.

The interface 152 may be a communication interface, in particular a data interface, configured to receive data from another device and/or from a user, and/or to transmit data from the interface 152 to other components of the data processing unit and/or to an external device. The interface 152 may include at least one electronic interface and/or human-machine interface, for example, an input/output device such as a display and/or a keyboard. The interface 152 may have at least one data connection, such as a bluetooth connection, an NFC connection, or another connection. The interface 152 may have at least one network or be part of a network. The interface 152 may have at least one internet port, at least one USB port, at least one drive, or a web interface.

The data processing unit 150 is configured to generate the target pattern 130. The data processing unit 150 is configured to compare the desired data set 104 with the partitions 124. The data processing unit 150 is configured to adapt at least one partition index 126 in case a partition 124 deviates from a desired data set. The data processing unit 150 is configured to create at least one element 106 in the test plan 100 having at least one piece of pattern information according to the target pattern 130.

The coordinate measuring machine 136 may have at least one controller 154 configured to control at least one component of the coordinate measuring machine 136 to measure the measurand 102 according to the test plan 100.

Fig. 6A-6E illustrate further exemplary embodiments of adapting partition 124. The partitions 124 may be one-dimensional partitions, two-dimensional partitions, or three-dimensional partitions 124. The partitions 124 may be linear partitions, rotational partitions, or offset polar partitions. The generation of the linear partition 124 may include defining a first partition index 126, where the defining includes determining a location and a type of the partition index (e.g., a circle). The generation of the linear partition 124 may include determining a total number of partition indices 126 in the x-direction and/or the y-direction. The generation of linear partition 124 may include determining an offset in at least one direction by a constant value. The partition 124 may be generated on the basis of the first partition index 126 by using the total number and an offset from the partition function. The partition function of the linear partition may be a linear function. Fig. 6A shows an example of a 1D linear partition 124 in the x-direction, where partition indices 126 are missing at some locations, which are removed during adaptation of the partition. Fig. 6B shows an example of 2D linear partitions 124 in the x and y directions, where partition indices 126 are missing at some locations, which are removed during adaptation of the partitions. FIG. 6C shows an example of rotating partitions, where the partition index 126 is missing at some locations. Fig. 6D shows an example of offset polar partitioning, where the partition index 126 is missing at some locations, which is removed during adaptation of the partition. Fig. 6E shows an example of a rotated partition with an offset in the z-direction, where partition indices 126 are missing at some locations, which are removed during adaptation of the partition.

List of reference numerals

100 test plan

102 measurand

103 measurement element

104 expected data set

106 element

108 geometric elements

110 regular geometric shape

112 special geometry

114 pattern

116 missing position

118 edge boundary or outer contour

120 outer circle

122 starting pattern

124 partition

126 partition index

128 adapted elements

130 target pattern

132 masked partition index

134 pattern position

136 coordinate measuring machine

138 measuring table

140 door frame

142 first upright post

144 second upright post

146 Cross Member

148 coordinate system

150 data processing unit

152 interface

154 controller.