US-20260126775-A1 - COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM, DATA CARRIER SIGNAL, AND MANUFACTURING SYSTEM FOR GENERATING CONTROL DATA FOR CONTROLLING AT LEAST ONE MANUFACTURING MACHINE

US20260126775A1US 20260126775 A1US20260126775 A1US 20260126775A1US-20260126775-A1

Abstract

The disclosure relates to a computer-implemented method, to a computer program, to a data carrier signal and to a manufacturing system for generating control data for controlling at least one manufacturing machine, wherein a plurality of steps for manufacturing process control are included for this purpose and point cloud data relating to a workpiece are registered, wherein the registration of the point cloud data relating to the workpiece 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 center of gravity coordinates of the point cloud.

Inventors

Franz-Georg Ulmer
Daniel Goersch

Assignees

CARL ZEISS DIGITAL INNOVATION GMBH

Dates

Publication Date: 20260507
Application Date: 20251230

Claims (14)

1 . A computer-implemented method, and/or a method carried out on at least one processor and/or carried out under a control of the at least one processor and/or initiated by the at least one processor, for generating control data for controlling at least one manufacturing machine, the method comprising the following steps and at least one of the following steps of the method being performed on the at least one processor and/or another processor: obtaining point cloud data of a workpiece; registering the point cloud data of the workpiece; fitting geometric surface elements and/or form elements to the point cloud data of the workpiece; obtaining at least one of an inspection plan, a computer-aided design (CAD) model of the workpiece, actual control data, and available infrastructure or environmental data of at least one manufacturing machine for manufacturing the workpiece; comparing the inspection plan and/or CAD model of the workpiece with the fitted geometric surface elements and/or form elements and identifying divergences; evaluating or assessing the divergences identified in the comparing based on at least one of specified tolerances of the workpiece, the actual control data, and the infrastructure or environmental data; generating new target control data and/or new infrastructure or environmental data for continued manufacture of the workpiece or for manufacturing a new workpiece in accordance with the evaluating and assessing of the divergences identified in the comparison; and transferring at least one of the new target control data and the new infrastructure or environmental data to the at least one manufacturing machine and/or to at least one other manufacturing machine and/or to a factory controller superordinate to the manufacturing machine and/or the at least one other manufacturing machine in accordance with the evaluating or assessing of the divergences identified in the comparison, wherein registering the point cloud data of the workpiece is based on a singular value decomposition (SVD) of a difference matrix of the point cloud data with a determination of associated singular vectors and/or based on a determination of eigenvectors of a covariance matrix of the point cloud data which is formed from the difference matrix, the difference matrix being formed from a difference between point cloud data coordinates and centroid coordinates of the point cloud, wherein registering the point cloud data of the workpiece further comprises calculating scalar products of the point cloud data and at least two determined singular vectors and/or eigenvectors, and/or wherein registering the point cloud data of the workpiece further comprises determining at least two coordinates in the X and/or Y and/or Z direction of the point cloud data coordinates rotated into the computer coordinate system or CAD coordinate system.
2 . The method as claimed in claim 1 , wherein the scalar products and/or the coordinates are used to determine a most probable initial value and/or a final value of the expansion of the workpiece in a direction of a singular vector and/or an eigenvector and/or in a coordinate direction by considering a density distribution or a frequency distribution of the scalar products in the direction of a corresponding singular vector and/or the eigenvector and/or by considering the density distribution or the frequency distribution of the coordinates.
3 . The method as claimed in claim 2 , wherein, when considering the density distribution or frequency distribution of the scalar products and/or the coordinates, evaluating or assessing of the divergences is performed based on mean values, threshold values, half-value widths, threshold value widths and/or gradients of local density distributions or frequency distributions.
4 . The method as claimed in claim 2 , wherein the most probable initial value and/or the final value of the expansion of the workpiece is determined by identifying a local maximum and/or a local minimum of a first partial derivative of the density distribution or frequency distribution in the direction of the corresponding singular vector and/or the eigenvector and/or in the coordinate direction, and wherein, when ambiguities occur, a first extremum in the direction under consideration is used.
5 . The method as claimed in claim 2 , wherein the most probable initial value and/or the final value of the expansion of the workpiece in the direction of the singular vector and/or the eigenvector and/or in the coordinate direction is determined by evaluating the density distribution or frequency distribution of the scalar products or coordinates having a largest or a smallest absolute value, and wherein the scalar products or the coordinates that diverge by more than 5% from the scalar products or the coordinates having the largest or the smallest absolute value are ignored when evaluating or assessing the divergences.
6 . The method as claimed in claim 1 , wherein registering the point cloud data of the workpiece is carried out based on a parallelization by or with an involvement of at least one graphics processor.
7 . The method as claimed in claim 1 , wherein the point cloud data of the workpiece are measured by at least one sensor as coordinates of points on the workpiece and are further-processed by the at least one processor to generate further point cloud data of the workpiece, the further point cloud data representing at least one subregion of the workpiece to be manufactured and the point cloud data being further-processed by the at least one processor and/or by at least one other processor in accordance with other method steps and/or being transferred to other processors for further processing.
8 . The method as claimed in claim 1 , wherein the at least one of the inspection plan, the CAD model of the workpiece, the actual control data, and the available infrastructure or environmental data of the at least one manufacturing machine are preprocessed and transferred by the at least one processor of the at least one manufacturing machine and/or by at least one processor of the factory controller superordinate to the at least one manufacturing machine.
9 . The method as claimed in claim 1 , wherein the new target control data and/or the new infrastructure or environmental data are further processed by the at least one processor of the at least one manufacturing machine and/or the at least one other manufacturing machine and/or by the at least one processor of the factory controller superordinate to the at least one manufacturing machine and/or the at least one other manufacturing machine, for manufacturing the new workpiece.
10 . A computer program comprising commands that, when the computer program is executed by the at least one processor, causes the at least one processor and/or other processors to carry out the method as claimed in claim 1 .
11 . A data carrier signal that transmits all or part of the computer program as claimed in claim 10 .
12 . A manufacturing system, comprising: a computer program as claimed in claim 11 ; and the at least one processor.
13 . A manufacturing system, comprising: at least one processor and at least one memory, the at least one processor interchanging data with the at least one memory, and the manufacturing system being configured to: obtain point cloud data of a workpiece; register the point cloud data of the workpiece; fit geometric surface elements and/or form elements to the point cloud data of the workpiece; obtain at least one of an inspection plan, a CAD model of the workpiece, actual control data, and available infrastructure or environmental data of at least one manufacturing machine for manufacturing the workpiece; compare the inspection plan and/or CAD model of the workpiece with the fitted surface elements and/or form elements and identifying divergences; evaluate or assess the divergences identified in the comparison based on at least one of specified tolerances of the workpiece, the actual control data, and the infrastructure or environmental data; generate new target control data and/or new infrastructure or environmental data for continued manufacture of the workpiece or for manufacturing a new workpiece in accordance with the evaluating or assessing of the divergences identified in the comparison; transfer at least one of the new target control data and the new infrastructure or environmental data to the at least one manufacturing machine and/or to at least one other manufacturing machine and/or to a factory controller superordinate to the at least one manufacturing machine and/or the at least one other manufacturing machine in accordance with an evaluation or assessment of the divergences identified in when the inspection plan and/or CAD model of the workpiece are compared with the fitted surface elements and/or form elements; wherein when the point cloud data of the workpiece is registered based on a singular value decomposition (SVD) of a difference matrix of the point cloud data with a determination of associated singular vectors and/or based on a determination of eigenvectors of a covariance matrix of the point cloud data that is formed from the difference matrix, the difference matrix is formed from a difference between point cloud data coordinates and the centroid coordinates of the point cloud, and wherein when the point cloud data of the workpiece is registered the scalar products of the point cloud data and at least two determined singular vectors and/or eigenvectors are calculated, and/or wherein when the point of cloud data of the workpiece is registered, 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 coordinate system or CAD coordinate system are determined.
14 . The method as claimed in claim 2 , wherein the most probable initial value and/or the final value of the expansion of the workpiece in the direction of the singular vector and/or the eigenvector and/or in the coordinate direction is determined by evaluating the density distribution or the frequency distribution of the scalar products or coordinates having a largest or a smallest absolute value, and wherein the scalar products or the coordinates that diverge by more than 1% from the scalar products or the coordinates having the largest or the smallest absolute value are ignored when evaluating or assessing the divergences.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is a continuation application of international patent application PCT/EP2023/067962, filed Jun. 30, 2023, designating the United States, and the entire content of the application is incorporated herein by reference. TECHNICAL FIELD The disclosure relates to a computer-implemented method, a computer program, a data carrier signal, and a manufacturing system for generating control data for controlling at least one manufacturing machine. The present computer-implemented method is not only a disclosure following the classical definition, that includes a computer, a computer network, or any other programmable device in which all or part of at least one feature is realized with a computer program, but also, in the age of cloud computing, a disclosure that additionally and/or alternatively includes a method for generating control data for controlling at least one manufacturing machine that takes place on at least one processor and/or takes place under the control of at least one processor and/or is initiated by at least one processor, the method including steps that are performed on the at least one and/or another processor. BACKGROUND In many branches of industry, efforts are being made to integrate the measurement technology necessary for quality assurance in production into the production line or even into the actual manufacturing machines. For example, U.S. Pat. No. 6,969,821 B2 discloses a method or a manufacturing machine for producing turbine blades in which the measurement technology for quality assurance is already integrated in the manufacturing machine. In addition, U.S. Pat. No. 10,220,566 B2, U.S. Pat. No. 10,532,513 B2, U.S. Pat. No. 11,104,064 B2, and US 2021/0379823 A1 describe measurement technology for an additive manufacturing (AM) process that is accordingly integrated in the manufacturing machine. There are several reasons for this approach. First, the measurement technology, which today is largely designed for measuring room infrastructure (controlled temperature, cleanroom/gray room, low vibration, etc.), is expensive. Second, the measuring rooms are separate from production and thus complicate workpiece logistics. In addition, the remote measuring rooms produce long latencies, and so the measurement results obtained there at a later time cannot be used for effectively controlling production. Furthermore, the information from the measuring rooms is not conditioned in such a way that production can use it in automated form for process control. Therefore, remote measuring rooms are unsuitable for online capture and online control of process states, especially in sometimes global manufacturing alliances, and also cannot support process development or process approaches for the increasing demand for small-series and one-off production. Furthermore, the measuring rooms take up large areas that can otherwise be used for production. In addition, highly qualified personnel must be provided for the measuring rooms. The degree of automation when using measuring rooms is also low and thus gives rise to additional personnel costs. Accordingly, U.S. Pat. No. 10,180,667 B2 describes a measurement technology integrated in the manufacturing machine where the measurement results are interpreted by a trained artificial intelligence (AI). WO 2018/204410 also describes a trained AI in which measurement results from metrology sensors or from coordinate measuring machines are processed via a cloud computing network or management system for one or more manufacturing machines. For all measurement technologies integrated in the production line, the fundamental problem is comparing the point cloud recorded from a workpiece with target specifications, usually inspection plans derived from computer-aided design (CAD) data. To achieve this, the recorded point clouds of the workpiece must first be transferred, for the actual comparison, to the computer coordinate system or CAD coordinate system by way of a registration, i.e., an alignment based on the CAD model, with a rotation and a translation. This applies in particular if the workpieces are guided past the measurement technology sensors for measuring the point clouds in an uncoordinated manner at any location and with any orientation on a conveyor belt, or if the workpieces are held in the capture range of the measurement technology sensors for measuring the point clouds, for example by robots in different poses. In this regard, U.S. Pat. No. 11,049,236 B2 describes a special solution that is particularly suitable for registering flat workpieces moving on a conveyor belt. SUMMARY It is an object of the present disclosure, in view of the prior art, to provide a robust method suitable for all types of workpieces, which can be used globally on all possible manufacturing machines, and which permits a quick target/actual comparison, based on inspection plans or CAD models, with point clouds recorded from the workpie