Patent Publication Number: US-2023141971-A1

Title: Method and device for collision avoidance during workpiece processing by a multi-axis processing machine

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims benefit to German Patent Application No. DE 10 2021 129 148.8, filed on Nov. 9, 2021, which is hereby incorporated by reference herein. 
     FIELD 
     The present disclosure relates to a method and a device for processing a workpiece. 
     BACKGROUND 
     During the three-dimensional processing of a workpiece, in particular during laser cutting or laser welding, collision between one part of a processing machine, in particular a tool head of the processing machine, and a workpiece or another part of the processing machine may occur. 
     It is known to avoid such collisions by manually checking a processing path. The applicant is furthermore aware of the use of a brute force algorithm for collision avoidance during workpiece processing. However, both methods are complex or require very long computation times. 
     SUMMARY 
     In an embodiment, the present disclosure provides a method that processes a workpiece using a device. The device has a processing machine configured for three-dimensionally processing the workpiece, and a tool head having a tool. The tool head is movable about at least a first axis and a second axis. The method is carried out by the device. The method includes: capturing input data concerning a contour of the workpiece, a contour of the tool head, a distance between the tool head and the workpiece, the first axis, and the second axis; processing the input data to form feature data; processing the feature data in a machine learning algorithm of the device; and outputting a forecast from the machine learning algorithm regarding a collision of the tool head with the workpiece or some other part of the processing machine. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Subject matter of the present disclosure will be described in even greater detail below based on the exemplary figures. All features described and/or illustrated herein can be used alone or combined in different combinations. The features and advantages of various embodiments will become apparent by reading the following detailed description with reference to the attached drawings, which illustrate the following: 
         FIG.  1    shows a processing machine having a tool head for processing a workpiece; 
         FIG.  2    shows the processing machine from  FIG.  1   , the tool head colliding with the workpiece during the processing of a specific location of the workpiece; 
         FIG.  3    shows the processing machine from  FIG.  2   , the tool head not colliding with the workpiece during the processing of the same location as in  FIG.  2   ; 
         FIG.  4    shows a method for recognizing and avoiding a collision by means of a machine learning algorithm and for training the machine learning algorithm; 
         FIG.  5    shows a method for recognizing and avoiding a collision using a sensor; 
         FIG.  6   a    shows a close-up view of a collision between a tool head and a workpiece; 
         FIG.  6   b    shows an edge image of the situation in accordance with  FIG.  6   a   ; and 
         FIG.  6   c    shows a filtered Z-buffer image of the situation in accordance with  FIG.  6   b   . 
     
    
    
     DETAILED DESCRIPTION 
     Aspects of the present disclosure provide a method and a device which enable collision avoidance in an effective and reliable manner. 
     According to an aspect of the present disclosure, a method is provided for processing a workpiece using a device, the device comprising a processing machine configured for 3D processing of the workpiece and comprising a tool head, the tool head being movable about a first axis and a second axis, the method being carried out by the device and comprising the following method steps:
     B) capturing input data concerning the geometry of the tool head, of the workpiece and of the distance between tool head and workpiece, and also concerning the first axis and concerning the second axis;   C) processing the input data to form feature data;   D) processing the feature data in a machine learning algorithm of the device;   E) outputting a forecast from the machine learning algorithm regarding the collision of the tool head with the workpiece and/or some other part of the processing machine.   

     The method according to an aspect of the present disclosure enables the reliable and uncomplicated collision avoidance between tool head and workpiece or some other part of the processing machine during the processing of the workpiece along a processing path. 
     The first axis and the second axis preferably extend through the processing point of the tool on the workpiece. The first axis can be a B-axis of the processing machine. The second axis can be a C-axis of the processing machine. 
     The distance between the tool head and the workpiece can be determined from a Z-buffer image 
     The device can comprise a computer having the machine learning algorithm. The computer can be part of the processing machine. 
     The processing machine is preferably embodied in the form of a 5- or 6-axis machine. Preferably three axes thereof are provided for moving to a processing point with the tool and two to three axes thereof are provided for setting a working position of the tool head. 
     The input data are preferably present as a geometry image (black-and-white image, greyscale image, red-green-blue (RGB) image) and/or a Z-buffer image (distance image). The feature data can be present in the form of a feature vector 
     The output in method step E) can analogously be effected in the form “will collide”, “will collide soon” or “will not collide”. 
     Before method step B), the machine learning algorithm can be trained by inputting verified input data of collisions and/or absent collisions in a method step A). The verified input data can originate from verified forecasts obtained in method step E). Alternatively, the input data can originate from a simulation. The training of the machine learning algorithm constitutes an independent aspect of the present disclosure. 
     The input data in method step B) can be at least partly taken from computer assisted design (CAD) data or NC data of the processing machine. By way of example, in a simulation the CAD data can be converted into simulated sensor data, in particular simulated camera data. 
     As an alternative or in addition thereto, the input data can be at least partly taken from sensor data that were recorded by a sensor. The use of the sensor data enables the collision avoidance with significantly reduced computing power for creating or processing the input data. Furthermore, with the use of sensor data, it is also possible to react to unforeseen situations, for example if a holder - which in particular is unknown to the processing machine - is used for clamping the workpiece. 
     The sensor is particularly preferably arranged or embodied on the tool head. As a result, no or only little further processing of the sensor data is necessary. 
     In a particularly preferred configuration of an aspect of the present disclosure, the sensor is embodied in the form of a camera. The camera can be embodied in the form of a time-of-flight (TOF) camera or a contour-depth (RGB-D) camera. 
     In method step E), collision-free input data (values) of the first axis and of the second axis can be output by processing the input data from method step B). 
     As an alternative thereto, in the case of collision forecast in method step E), the following method steps can be carried out after method step E):
     F) varying the input data of the first axis and/or the second axis;   G) carrying out method steps B) to E);   H) carrying out method steps F) and G) until no collision is forecast in method step D) or a predefined number of passes of method steps F) and G) has been reached, in which case, in the absence of collision, the input data of the first axis and second axis used in method step B) are output.   

     In a particularly preferred configuration of an aspect of the present disclosure, the machine learning algorithm is embodied in the form of a neural network. The neural network can comprise an input layer, a plurality of hidden layers and an output layer. The feature data can be fed to the input layer of the neural network. 
     The method according to an aspect of the present disclosure is particularly advantageous if the processing machine is embodied in the form of a cutting machine and/or in the form of a laser processing machine. The laser beam of a laser tool of the laser processing machine preferably has the processing point of the tool (here of the laser beam) at its focal point. 
     According to another aspect of the present disclosure, the device is described here for carrying out the method described here. 
     Further advantages of the invention are evident from the description and the drawings. Likewise, according to the invention, the features mentioned above and those that will be explained still further may be used in each case individually by themselves or as a plurality in any desired combinations. The embodiments shown and described should not be understood as an exhaustive enumeration, but rather are of exemplary character for outlining the invention. 
       FIG.  1    shows a device  10  having a processing machine  12  for processing a workpiece  14 . The processing machine  12  is embodied in a multi-axis fashion. The processing machine  12  has a first axis  16  and a second axis  18  Three further axes are provided for moving to a processing point  20 . 
       FIG.  2    likewise shows the device  10 . It is evident from  FIG.  2    that the processing machine  12  comprises a tool head  22 , which collides with the workpiece  14  in a collision region  24 . An advantage of implementations according to aspects of the present disclosure is to avoid such collisions in a particularly efficient manner. 
       FIG.  3    shows the device  10  with the processing machine  12  and the workpiece  14 . It is evident from a joint consideration of  FIGS.  2  and  3    that in  FIG.  3    the same processing of the workpiece  14  is performed, but the collision shown in  FIG.  2    is avoided by way of pivoting of the axes  16  and  18  (see  FIG.  1   ). 
       FIG.  4    shows a method  26  for recognizing and avoiding a collision using a machine learning algorithm  28 . In this case, in a simulator  30 , CAD data of the workpiece  14  (see  FIG.  3   ) are converted into simulated sensor data. In accordance with the simulation of the workpiece processing, a collision  32  or a resolved collision  34  can arise. A Z-buffer image (distance image)  36   a ,  36   b , a geometry image  38   a ,  38   b  and the associated values  40   a ,  40   b  of the axes  16 ,  18  (see  FIG.  1   ) can be present for each of these scenarios. These input data  42  (see also  FIGS.  6   a - c   ) are processed to form feature data  46 , here in the form of a feature vector  48 , in an extraction unit  44   
     The feature vector  48  is input into the machine learning algorithm  28 , here in the form of a neural network. In particular, the feature vector  48  is transferred to an input layer  50  and processed by hidden layers  52 . An output layer  54  outputs collision-free values  40   c  of the axes  16 ,  18  (see  FIG.  1   ). 
     For the purpose of training the machine learning algorithm  28 , these values  40   c  can be verified and fed back as feedback  55 . 
       FIG.  5    shows a method  26  wherein the input data  42  are at least partly generated by a sensor  56 , here in the form of a camera. The sensor  56  can be arranged on the tool head  22  of the processing machine  12 . Particularly preferably, the sensor  56  is arranged at the same position at which a virtual sensor was situated when data for training the machine learning algorithm  28  were generated by way of a simulation. 
     In this case, the device  10  comprises a controller  58 , which firstly predefines the values  40   a  of the first axis  16  and/or second axis  18  (see  FIG.  1   ). From the input data  42 , in the extraction unit  44 , the feature data  46  or the feature vector  48  are/is generated and transferred to the machine learning algorithm  28 , which outputs collision-free values  40   c . These collision-free values  40   c  can be transferred to the controller  58  in order to correct the processing path of the workpiece  14 , if appropriate. 
       FIG.  6   a    shows a close-up view of a geometry image  38   c  with a collision region  24 . 
       FIG.  6   b    shows the geometry image  38   c  from  FIG.  6   a    as an edge image  60 . 
       FIG.  6   c    shows the edge image  60  in accordance with  FIG.  6   b    as a filtered Z-buffer image  36   c . 
     The representations  38   c ,  60  and/or  36   c  can be used for determining a collision, the Z-buffer image  36  being particularly well suited to processing by the machine learning algorithm  28  (see  FIGS.  4  and  5   ). 
     With all figures of the drawing being jointly taken into consideration, aspects of the present disclosure relates to a method  26  and a device  10  for avoiding collisions between a part of a processing machine  12  and a workpiece  14  processed by the processing machine  12 . This involves simulating distances between at least one part of the processing machine  12  and the workpiece  14  in the case of the values  40   a , b of at least a first axis  16  and a second axis  18  of the processing machine  12  during workpiece processing, and/or measuring them by means of a sensor  56 . These input data  42  are preferably processed to form a feature vector  48  and fed to a trained machine learning algorithm  28 . The machine learning algorithm  28  determines collision-free values  40   c  of the axes  16 ,  18  depending on the feature vector  48 . These values  40   c , after being verified, can be fed to the machine learning algorithm  28  as feedback  54  indirectly or directly for the training of the machine learning algorithm  28 . 
     While subject matter of the present disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. Any statement made herein characterizing the invention is also to be considered illustrative or exemplary and not restrictive as the invention is defined by the claims. It will be understood that changes and modifications may be made, by those of ordinary skill in the art, within the scope of the following claims, which may include any combination of features from different embodiments described above. 
     The terms used in the claims should be construed to have the broadest reasonable interpretation consistent with the foregoing description. For example, the use of the article “a” or “the” in introducing an element should not be interpreted as being exclusive of a plurality of elements. Likewise, the recitation of “or” should be interpreted as being inclusive, such that the recitation of “A or B” is not exclusive of “A and B,” unless it is clear from the context or the foregoing description that only one of A and B is intended. Further, the recitation of “at least one of A, B and C” should be interpreted as one or more of a group of elements consisting of A, B and C, and should not be interpreted as requiring at least one of each of the listed elements A, B and C, regardless of whether A, B and C are related as categories or otherwise. Moreover, the recitation of “A, B and/or C” or “at least one of A, B or C” should be interpreted as including any singular entity from the listed elements, e.g., A, any subset from the listed elements, e.g., A and B, or the entire list of elements A, B and C. 
     LIST OF REFERENCE SIGNS 
     
         
           10  Device 
           12  Processing machine 
           14  Workpiece 
           16  First axis 
           18  Second axis 
           20  Processing point 
           22  Tool head 
           24  Collision region 
           26  Method 
           28  Machine learning algorithm 
           30  Simulator 
           32  Collision 
           34  Resolved collision 
           36   a - c  Z-buffer image 
           38   a - c  Geometry image 
           40   a - c  Values of the first axis  16  and/or second axis  18   
           42  Input data 
           44  Extraction unit 
           46  Feature data 
           48  Feature vector 
           50  Input layer 
           52  Hidden layers 
           54  Output layer 
           55  Feedback 
           56  Sensor 
           58  Controller 
           60  Edge image