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EP-4735964-A1 - COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM, DATA CARRIER SIGNAL AND MANUFACTURING SYSTEM FOR GENERATING CONTROL DATA RELATING TO AT LEAST ONE MANUFACTURING MACHINE

EP4735964A1EP 4735964 A1EP4735964 A1EP 4735964A1EP-4735964-A1

Abstract

The invention relates to a computer-implemented method (40), to a computer program, to a data carrier signal and to a manufacturing system (201) for generating control data relating to at least one manufacturing machine (1), wherein a plurality of steps (42) to (54) for manufacturing process control are included for this purpose and point cloud data relating to a workpiece (7) are registered (43), wherein the registration (43) of the point cloud data relating to the workpiece (7) is based on a singular value decomposition (SVD) of a difference matrix of the point cloud data with determination of the associated singular vectors and/or on a determination of the eigenvectors of a covariance matrix of the point cloud data that is formed from the difference matrix, wherein the difference matrix is formed from the difference between the point cloud data coordinates and the centre of gravity coordinates of the point cloud.

Inventors

  • Ulmer, Franz-Georg
  • GOERSCH, DANIEL

Assignees

  • Carl Zeiss Digital Innovation GmbH

Dates

Publication Date
20260506
Application Date
20230630

Claims (15)

  1. 1. A method (40) for generating control data for at least one production machine (1) that is computer-implemented and/or runs on at least one processor and/or runs under the control of at least one processor and/or is initiated by at least one processor, wherein the method (40) comprises the following steps and at least one of the following steps of the method (40) is carried out on the at least one and/or another processor: • Obtaining (42) point cloud data of a workpiece (7); • Registering (43) the point cloud data of the workpiece (7); • Fitting (44) geometric surface and/or shape elements to the point cloud data of the workpiece (7); • Obtaining (46) a test plan and/or C AD model of the workpiece (7) and, if applicable, actual control data and/or, if applicable, existing infrastructure or environmental data from at least one production machine (1) for producing the workpiece (7); • Comparing (48) the test plan and/or CAD model of the workpiece (7) with the fitted surfaces and/or form elements and determining deviations; • Evaluating or assessing (50) the deviations determined in the comparison, if necessary on the basis of specified tolerances of the workpiece (7) and, if necessary, the actual control data and/or, if necessary, the infrastructure or environmental data; • generating (52) new target control data and/or new infrastructure or environmental data for the further production of the workpiece (7) or for the production of a new workpiece (7) depending on the evaluation (50); • Transferring the new target control data and/or, if applicable, the new infrastructure or environmental data to the at least one production machine (1) and/or to at least one further production machine and/or to one of the production machines (1) and/or the at least one further Factory control of the production machine depending on the evaluation (50); wherein the registration (43) of the point cloud data of the workpiece (7) is based on a singular value decomposition (SVD) of a difference matrix of the point cloud data with determination of the associated singular vectors and/or on a determination of the eigenvectors of a covariance matrix of the point cloud data formed from the difference matrix, wherein the difference matrix is formed from the difference of the point cloud data coordinates and the center of gravity coordinates of the point cloud.
  2. 2. Method (40) according to claim 1, wherein the registration (43) also comprises a calculation of the scalar products of the point cloud data with at least two determined singular vectors and l or eigenvectors and / or wherein the registration (43) also comprises a determination of at least two coordinates in the X and / or Y and / or Z direction of the coordinates of the point cloud data rotated into the computer or CAD coordinate system.
  3. 3. Method (40) according to claim 2, wherein with the aid of the scalar products and/or the coordinates, the most probable initial value and/or final value of the extension of the workpiece (7) in the direction of a singular vector and/or an eigenvector and/or in a coordinate direction is determined by considering a density or frequency distribution of the scalar products in the direction of the corresponding singular vector and/or eigenvector and/or by considering a density or frequency distribution of the coordinates.
  4. 4. Method (40) according to claim 3, wherein when considering the density or frequency distribution of the scalar products and/or the coordinates, the evaluation is carried out on the basis of mean values, threshold values, half-widths, threshold widths and/or gradients of the local density or frequency distributions.
  5. 5. Method (40) according to claim 3, wherein the most probable initial value and/or final value of the extension of the workpiece (7) is determined by determining the local maximum and/or the local minimum of the first partial derivative of the density or frequency distribution in the direction of the corresponding singular vector and/or eigenvector and/or in the coordinate direction, whereby in case of ambiguities the first extremum in the direction considered is used.
  6. 6. Method (40) according to claim 3 or 4, wherein the most probable initial value and/or final value of the extension of the workpiece (7) in the direction of a singular vector and/or eigenvector and/or in a coordinate direction is determined by an evaluation of the density or frequency distribution of the largest or smallest scalar products or coordinates in terms of magnitude, and wherein scalar products or coordinates which deviate from the largest or smallest scalar products or coordinates in terms of magnitude by more than 5%, in particular by more than 1%, are disregarded for this evaluation.
  7. 7. Method (40) according to one of the preceding claims, wherein the registration (43) of point cloud data of the workpiece (7) is carried out on the basis of a parallelization by or with the involvement of at least one graphics processor.
  8. 8. Method (40) according to one of the preceding claims, wherein the point cloud data of the workpiece (7) are measured by at least one sensor as coordinates of points of the workpiece (7) and further processed by at least one processor to form point cloud data of the workpiece (7), wherein the point cloud data represent at least a partial area of the workpiece (7) to be manufactured and wherein the point cloud data are further processed by this and/or at least one further processor in accordance with the further method steps and/or are passed on to other processors for further processing.
  9. 9. Method (40) according to one of the preceding claims, wherein the test plan and/or the CAD model of the workpiece (7) and, if applicable, the actual control data and/or, if applicable, the existing infrastructure or environmental data of the at least one production machine (1) are processed by at least one processor of the at least one production machine (1) and/or by at least one processor of the factory control system superordinate to the at least one production machine (1). preprocessed and handed over.
  10. 10. Method (40) according to one of the preceding claims, wherein the new DESIRED control data and/or optionally the new infrastructure or environmental data are further processed by at least one processor of the at least one production machine (1) and/or the at least one further production machine and/or by at least one processor of the factory control system superordinate to the at least one production machine (1) and/or the at least one further production machine for the production of the new workpiece (7).
  11. 11. Computer program comprising instructions which, when the program is executed by at least one processor, cause this at least one processor and/or further processors to carry out the method according to one of claims 1 to 10.
  12. 12. Data carrier signal that transmits the computer program in whole or in part.
  13. 13. Manufacturing system (210) comprising a computer program according to claim 11 and at least one processor.
  14. 14. Manufacturing system (210) comprising at least one processor and at least one memory, wherein the at least one processor exchanges data with the at least one memory and the manufacturing system (210) is configured to: • Obtaining (42) point cloud data of a workpiece (7); • Registering (43) the point cloud data of the workpiece (7); • Fitting (44) geometric surface and/or shape elements to the point cloud data of the workpiece (7); • Obtaining (46) a test plan and/or CAD model of the workpiece (7) and, if applicable, actual control data and/or, if applicable, available infrastructure or environmental data of at least one production machine (1) for producing the workpiece (7); • Comparing (48) the test plan and/or CAD model of the workpiece (7) with the fitted surfaces and/or form elements and determining deviations; • Evaluating or assessing (50) the deviations determined in the comparison, if necessary on the basis of specified tolerances of the workpiece (7) and, if necessary, the actual control data and/or, if necessary, the infrastructure or environmental data; • generating (52) new target control data and/or new infrastructure or environmental data for the further production of the workpiece (7) or for the production of a new workpiece (7) depending on the evaluation (50); • Transferring the new target control data and/or, if applicable, the new infrastructure or environmental data to the at least one production machine (1) and/or to at least one further production machine and/or to a factory control system higher up the at least one production machine (1) and/or the at least one further production machine depending on the evaluation (50); wherein the registration (43) of the point cloud data of the workpiece (7) is based on a singular value decomposition (SVD) of a difference matrix of the point cloud data with determination of the associated singular vectors and/or on a determination of the eigenvectors of a covariance matrix of the point cloud data formed from the difference matrix, wherein the difference matrix is formed from the difference between the point cloud data coordinates and the center of gravity coordinates of the point cloud.
  15. 15. Manufacturing system (210) according to claim 14, wherein the registration (43) also comprises a calculation of the scalar products of the point cloud data with at least two determined singular vectors and/or eigenvectors and/or wherein the registration (43) also comprises a determination of at least two coordinates in the X and/or Y and/or Z direction of the coordinates of the data entered into the computer or CAD system. coordinate system rotated point cloud data.

Description

Description Computer-implemented method, computer program, data carrier signal and Manufacturing system for generating control data of at least one production machine The invention relates to a computer-implemented method, a computer program, a data carrier signal and a manufacturing system for generating control data for at least one manufacturing machine. The present computer-implemented method is not only an invention according to the classic definition, which comprises a computer, a computer network or another programmable device in which at least one feature is implemented in whole or in part with a computer program, but also, in the age of cloud computing, an invention which additionally and/or alternatively comprises a method for generating control data for at least one manufacturing machine, which method runs on at least one processor and/or is controlled by at least one processor and/or is initiated by at least one processor, the method having steps which are carried out on the at least one and/or another processor. In many industries, efforts are being made to integrate the measurement technology required for quality assurance in production into the production line or even into the actual production machines. For example, US 6,969,821 B2 discloses a method or a production machine for the production of turbine blades in which the measurement technology for quality assurance is already integrated into the production machine. Furthermore, US 10,220,566 B2, US 10,532,513 B2, US 11,104,064 B2 and US 2021/0379823 Al disclose a measurement technology for an additive manufacturing process (AM) that is integrated into the production machine. There are several reasons for this approach. Firstly, the measuring technology that is currently largely based on measuring room infrastructure (controlled temperature, clean/grey room, low vibration, etc.) is expensive. Secondly, the measuring rooms are separated from the production and thus complicate workpiece logistics. In addition, the remote measuring rooms generate large latencies, so that the measurement results obtained there at a later point in time cannot be can be used for effective control of production. Furthermore, the information from the measuring rooms is not prepared in such a way that it can be used by production in an automated form for process control. Therefore, remote measuring rooms are unsuitable for online recording and online control of process states, especially in potentially global manufacturing networks, and cannot support process development or process start-ups for the increasing demand for small series and unique production. Furthermore, the measuring rooms take up large areas that could otherwise be used for production. Highly qualified personnel must also be available for the measuring rooms. The level of automation in the use of measuring rooms is also low, which causes additional personnel costs. Accordingly, US 10,180,667 B2 discloses a measuring technology integrated into the production machine, in which the measurement results are interpreted by a trained AI. WO 2018/204410 also discloses a trained AI in which measurement results from metrology sensors or coordinate measuring machines are processed via a cloud computing network or management system for one or more production machines. All measuring techniques integrated into the production line have the fundamental problem of comparing the point cloud recorded from a workpiece with target specifications, usually test plans derived from CAD data. To do this, the recorded point clouds of the workpiece must first be transferred to the computer or CAD coordinate system for the actual comparison by means of registration, i.e. alignment based on the CAD model, rotation and displacement. This is particularly true when the workpieces are moved uncoordinatedly at random locations and with random orientations on a conveyor belt past the measuring technology sensors for measuring the point clouds, or when the workpieces are held in different poses in the capture range of the measuring technology sensors for measuring the point clouds, for example by robots. US 11,049,236 B2 discloses a special solution that is particularly suitable for the registration of flat workpieces moving on a conveyor belt. Based on the objective described at the beginning, a measurement technology integrated into production, it is the task of the present invention, in view of the state of the art, to provide a robust method that is suitable for all types of workpieces and can be used globally on all possible production machines, which enables a quick target / actual comparison based on test plans or CAD models with point clouds recorded from the workpiece in order to generate new control commands for a production machine. This object is achieved according to the invention by a computer-implemented method for generating control data of at least one production machine and/or running on at least one processor and/o