Cable processing station, cable processing machine with cable processing station and computer-implemented method

Abstract

The invention relates to a cable processing station 20 for processing cable ends 12 of a cable 10, comprising at least one first tool 22 for processing the cable 10, a control device 40 for controlling the at least one tool 22 and a first imaging sensor device 25 for detecting at least one image of at least one cable end 12 of the cable 10 and an image processing system 30. In order to exchange control-specific parameters, the image processing system 30 is connected to the control device 40 and is configured to identify a first cable-specific image parameter and at least one second cable-specific image parameter from the at least one detected image and to create a control-specific parameter and to transmit it to the control device 40 for controlling the first tool 22. The invention also relates to a computer-implemented method for automatically determining and generating control data sets and / or control-specific parameters for controlling at least one cable processing station and to a cable processing machine having at least one cable processing station.

Description

Technical Field

[0001] This invention relates to a cable processing station, a cable processing machine with a cable processing station, and a computer-implemented method conforming to each of the individual claims. Background Technology

[0002] Optical cable inspection devices are known to exist. They are used for quality inspection during the cable manufacturing process to sample and inspect finished cable systems consisting of a cable end and a cable connector, such as a plug that electrically or optically connects a cable to a terminal device.

[0003] US20020036770A1 describes an inspection device for evaluating the tightness of a cable crimped by a metal connector. The device includes a light source, a CCD camera for capturing images, a dark box, and a control unit. The control unit evaluates the crimping condition of at least a portion of the crimped component based on a dark area within an inspection region of the image, determining whether the crimping is normal or abnormal. A similar method is described in EP 0702 227 A1.

[0004] The drawback of these known solutions is that they identify defects and remove defective parts, thus not making any significant changes to the cable manufacturing process.

[0005] EP 3 146 600 B1 illustrates a crimping device with an image capturing device that monitors the positioning process of a cable relative to a connecting element before crimping, wherein the image capturing device detects the position of the cable end and transmits it to the control device of the crimping device.

[0006] The drawback of these known solutions is that they cannot identify incorrect cable types within the crimping device. Furthermore, the crimping tool must remain open for an extended period to capture images, resulting in relatively long downtime during the process.

[0007] DE 10 2016 122 728 A1 includes a conductor positioning function controlled by a measurement system, comprising a cable processing apparatus with an image processing system for capturing cable end images and a feeding device for feeding the cable into the cable processing apparatus, wherein the position of the cable in the processing area of ​​the cable processing apparatus can be determined based on the captured cable end images.

[0008] The drawback of this known solution is that the cable can only be positioned within the processing area of ​​the cable processing device using the feeding device. Another drawback is that no material area is detected on the cable.

[0009] Therefore, the object of the present invention is to provide a cable processing station that avoids at least one of the aforementioned disadvantages, and can quickly track the cable processing process, especially when deviations from the ideal process are detected. Furthermore, a cable processing machine with high reliability in the cable processing process should be provided. Additionally, a computer-implemented method for controlling at least one cable processing station should be provided, which corrects production faults in the cable processing station, thereby improving the cable processing process. Summary of the Invention

[0010] The task is accomplished through the features of the independent claims. Advantageous further improvements are set forth in the accompanying drawings and dependent claims.

[0011] A cable processing station according to the present invention, for processing a cable end (especially a cable or optical fiber), includes at least one first tool for processing the cable and a control device for controlling at least one of the first tools. Furthermore, the cable processing station according to the present invention includes at least one first imaging sensor device for detecting at least one image of at least one cable end of the cable, and an image processing system. To exchange control-specific parameters, the image processing system is connected to and configured to identify a first cable-specific image parameter and at least one second cable-specific image parameter from at least one detected image, and to create at least one control-specific parameter based on the first and second cable-specific image parameters, and transmit it to the control device for controlling the first tool.

[0012] In other words, at least one first cable-specific image parameter and a second cable-specific image parameter are combined to create at least one control-specific parameter. The first tool can be automatically controlled or adjusted based on at least one control-specific parameter, enabling rapid tracking of the cable processing process within the cable processing station. In the control device, at least one control-specific parameter applies to the first tool of the cable processing station to create a control data record with at least one control command.

[0013] As a first cable-specific image parameter, the conductor (typically also referred to as the cable core or cable core wire) is identified in the detected image. As a second cable-specific image parameter, the cable insulation layer (typically also referred to as the conductor sheath) is identified in the detected image. By combining the identification of at least one first cable-specific image parameter and the second cable-specific image parameter, a specific type of cable can be obtained in the image processing system, which can be processed by a cable processing station, or is designed for processing in a cable processing station. Control data records related to this cable type can be automatically retrieved by a control device to process the cable end with a first tool. For example, the cable processing station can be a wire stripping station for stripping cable ends, a crimping machine for crimping cable connectors onto conductors, or other stations, wherein the first tool is a cable conveyor, a wire stripper, or a crimping tool. Furthermore, the cable processing station can also include a sealing assembly device through which a seal can be installed onto the cable end or conductor. Here, the first tool can be a tool for attaching a seal to a cable, typically, for example, a gripping mechanism or a pneumatic assembly device. Typically, each tool can be driven by a drive unit, enabling it to perform the designed machining process steps. The respective drive unit of the tool is electrically connected to a control unit to exchange control-specific parameters.

[0014] As the primary imaging sensor device, a fixed camera or a line sensor can be used, employing a typical grid method for relative movement above the cable. A fixed camera can be positioned in a space-saving manner on a cable processing station. A movable line sensor can detect a large cable end on the cable. Alternatively, the camera can be mounted on a mobile device at the cable processing station, allowing it to be swiveled and moved from one tool to another at the station. The camera can detect multiple images of the cable end, providing the image processing system with more detected images to choose from. During this process, the camera can capture images of the cable or its end from different angles, particularly detecting at least one frontal view facing the cable axis to record the cable's hierarchical structure. This allows the presence of kinks in the conductors to be identified in the detected images. Typically, the camera described herein has a zoom lens and a number of commonly used filters, configured to recognize three-dimensional images.

[0015] In addition, another imaging sensor device can be arranged on the cable processing station so that at least one detection image can be composed of multiple sub-images, and / or the cable end can be detected from multiple shooting angles to improve image quality or the quality of the detection image.

[0016] Preferably, an artificial intelligence (AI) module is provided, connected to and configured with at least one first imaging sensor device to acquire first cable-specific image parameters and at least one second cable-specific image parameter from at least one detection image. For a specific cable type, the AI ​​module can be trained using external or individual image data or parameters (material, structure, color, shape, etc.). To this end, the AI ​​module has a computing unit for training. This allows for a wide range of applications, improving application accuracy, especially when analyzing a large number of different cable types. Furthermore, it can improve the analysis speed in image processing systems. The AI ​​module can flexibly work with different cable types and also with different cable connection elements.

[0017] Preferably, the artificial intelligence module includes at least one neural network designed to analyze at least one probe image, which can be used to train the neural network. The neural network can be trained with image parameters to improve future checks of processing steps in the cable processing station based on the quality of the training image, reference image, or reference contour, thereby achieving an ideal cable processing process. Furthermore, the artificial intelligence module allows for the execution of the cable processing process or multiple processing steps within the process with minimal operator intervention, as the neural network replaces the operator. Therefore, downtime in the cable processing station is minimized because the neural network requires no learning process when the processing task changes from one cable processing step to another, and no operator adjustments are required on the cable processing machine.

[0018] More preferably, the artificial intelligence module performs semantic segmentation on at least one probe image to associate each pixel of the probe image with at least one cable-specific image parameter, and transmits the analyzed first cable-specific image parameter and at least one analyzed second cable-specific image parameter to the image processing system. For example, such image processing is disclosed in Liang-Chieh Chen et al., “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation”, Google Inc, 2018, arXiv: 1802.02611, in which the probe image is processed. Here, if a pixel of the probe image does not relate to a cable end or cable, a background signal is associated with that pixel of the probe image as a cable-specific parameter. In an alternative embodiment, an image processing system is configured to perform segmentation on at least one probe image. During this process, the probe image is processed to filter or analyze it, resulting in a higher image quality, particularly in areas where the cable end can be identified, thereby allowing for better determination of the analyzed first cable-specific image parameter and the analyzed second cable-specific image parameter. Preferably, the image processing system has a computing unit that performs at least the aforementioned analysis or calculation steps.

[0019] Alternatively or as a supplement, the image processing system is configured to, for the cable end, segment at least one detection image into at least two material-specific regions based on first and second cable-specific image parameters. In the captured images, one material-specific region and the other are distinguished by having at least different materials. This segmentation of the cable end into at least two regions allows the image processing system to identify excess cable insulation residue or other foreign objects on the cable end. Furthermore, for coaxial cables, this simple segmentation facilitates the identification of excess cable shielding filaments on the insulation, thereby preventing short circuits or defective high-frequency connections in the processed cable later.

[0020] Preferably, the image processing system is configured to, for the cable end, segment at least one detection image into at least two material-specific regions based on the analyzed first and second cable-specific image parameters. This allows for better identification of excess cable insulation residue or other foreign objects on the cable end, as the detection images are filtered to obtain the analyzed cable-specific image parameters. Furthermore, the differences in material-specific regions on the images can be used to identify the cable shielding layer that has been folded back on the cable, or the cable membrane layer of high-voltage cables. As a result, later-stage interruptions in cable processing can be avoided, and higher-quality final products can be produced simultaneously, such as extending the service life of the processed cables.

[0021] Preferably, the first material-specific region includes at least the cable conductor, and the second material-specific region includes at least the cable insulation or cable shield (e.g., for coaxial cables or high-voltage cables). These material-specific regions can be analyzed by an image analysis system for their surface size and edge structure (also known as contour) to analyze the boundary region between the first and second material-specific regions. The image processing system is configured to identify or measure and / or calculate the surface size, surface perimeter, or surface diameter, or contour structure of the respective material-specific regions. During the cable insulation stripping process and the included stripping process steps, one cable core may be cut, and after the cable insulation is stripped, a cable core may protrude or extend relative to the conductor. Now, by identifying the aforementioned material-specific regions by an artificial intelligence module and / or the image processing system, it is possible to identify in the analyzed image whether it involves a protruding cable core or a section of cable insulation. For subsequent processing steps, when installing cable connection elements, a section of cable insulation may be allowed at the tip of the conductor. Known similar methods, such as the shadow image method, cannot distinguish the material of the protruding object from the material of the rest of the object in the detection image, which can lead to false fault alarms and cause otherwise usable cables to be discarded.

[0022] For example, a wire stripper is a rotating blade configured with controls and a dedicated control parameter to automatically adjust the cutting depth of the rotating blade when processing cables, such as coaxial cables (e.g., increasing the cutting depth). This allows the rotating blade to repeat the cutting process if, upon cutting the shield, at least one conductor remains uncut and is detected as a material-specific area (material area) on the insulation. Such uncut conductors can be dangerous, as they may cause short circuits in the plug later in the life of the finished cable. Furthermore, this subsequent processing of unfinished cable ends offers the advantage of eliminating the need to discard already cut cables, thus reducing material waste.

[0023] Additionally, for sheathed cables with multiple conductors, or shielded multi-core cables with filler, the orientation of the conductors can be detected and analyzed using an image processing system, thereby obtaining an end-face image. In the second step, based on the analyzed orientation of the conductors, a rotating module, acting as a first tool, is activated by control parameters of a control device to horizontally align the conductors in the sheathed cable. To calculate the rotation angle of the rotating module, the measured orientation is calculated in a calculation unit using the known torsion angle of the conductors, so that in a subsequent process step, a profile cutter can be used as another tool to cut the sheath or filler without damaging the contained conductors. For the aforementioned subsequent process steps, the control device includes other control-specific parameters and is configured to control the profile cutter in the subsequent process steps. Preferably, a database is provided, connected to the image processing system, wherein the database has at least one storage unit, and at least one storage unit stores reference images of different cable ends. The database storage unit stores at least one, preferably multiple, reference images of different cable types, which can be processed in the cable processing station and retrieved by the image processing system. Typically, at the start of the cable processing process, a work order containing the data for the cable to be processed is transmitted to the control unit of the cable processing station, which has access to a database and its contents. The image processing system compares the detected or analyzed images with reference images and / or work order information in the database. The reference images have segmented regions, which can therefore be easily compared with the detected or analyzed images. This allows the cable processing process to be performed without human intervention. The work order data for the cable or cable type to be processed includes control data records and / or control-specific parameters and corresponding control tolerances for controlling the first tool, as well as cable-specific image parameters and / or material-specific regions and tolerance values, allowing cables that fall within the corresponding tolerance range after processing to be further processed. This reduces the scrap rate of defective cable ends and improves the production efficiency of the cable processing station.

[0024] Alternatively or as a supplement, a database connected to the graphics processing system exists. This database has at least one storage unit, and in at least one of these units, reference profiles of different cable ends and / or sealing elements and / or cable connection elements are stored. The database storage unit stores at least one, preferably multiple, reference profiles of different cable types, which can be processed within the cable processing station and retrieved by the graphics processing system. Typically, at the start of the cable processing process, a work order containing the work order data for the cable to be processed is transmitted to the control device of the cable processing station, which can access the database and its contents. The graphics processing system compares the detected or analyzed image with the reference profiles and / or work order information in the database. The work order data for the cable to be processed or the cable type includes control data records and / or control-specific parameters and corresponding control tolerances for controlling the first tool, as well as cable-specific image parameters and / or material-specific areas and tolerance values, allowing cables within the corresponding tolerance range to be further processed. This reduces the scrap rate of defective cable ends and improves the production efficiency of the cable processing station.

[0025] Preferably, the reference contours are saved as contour vectors. The contour vectors can be associated with the detected or analyzed images. The data size of a contour vector is relatively small compared to the data size of a reference image, thus speeding up data processing.

[0026] Preferably, control-specific parameters and / or control data records are stored in the database, through which at least one first tool can be controlled or regulated. The control-specific parameters and / or control data records can be transmitted to the control device, thereby facilitating the control of the drive device of at least one first tool.

[0027] Alternatively, or as a supplement, the database is connected to an artificial intelligence module, where reference images of different cable ends are stored in at least one database storage unit. The image processing system compares the detected or analyzed images with the reference images in the database without operator intervention.

[0028] Alternatively, or as a supplement, the database is connected to an artificial intelligence module, wherein reference profiles of different cable ends are stored in at least one database storage unit. The image processing system compares the detected or analyzed image with the reference profiles in the database without operator intervention. As mentioned above, the reference profiles can be stored in the database as profile vectors.

[0029] Alternatively or as a supplement, at least one rating is stored in the database for at least one cable-specific image parameter. These ratings may be ratings for the stripped length at the cable end, and / or the width or thickness of the conductor, and typically include tolerance values ​​applicable to the specific cable type. Additionally, these ratings may include symmetrical or proportional values, such as the ratio between the stripped length and the conductor thickness for the cable type currently being processed.

[0030] Alternatively or as a supplement, at least one specification value is stored in the storage unit of the database for at least one material-specific region. These specifications may include specifications for surface size, surface perimeter, or surface diameter, or specifications for the edge structure of a specific material-specific region, or a combination of the above values, and have corresponding tolerance values. The image processing system can retrieve these specifications individually or in batches and compare them with the identified material-specific regions.

[0031] Preferably, the computing unit is configured to compare at least one rated value with at least one measured cable-specific image parameter, and based on this, create at least one deviation value that can be easily further edited. Preferably, the image processing system is configured to acquire at least a first cable-specific image parameter and / or at least an analyzed first cable-specific image parameter from an image of the cable end of the probed cable using an image measurement method. The image measurement method includes at least one image measurement algorithm, which is disclosed in S. and Abe, K., “Topological Structural Analysis of Digitized Binary Images by Border Following”, CVGIP301, pp 32-46 (1985). In this way, the edge structure of the material-specific region can be calculated. The edge structure can be subdivided into contour points, and then mathematically filtered using direction vectors to obtain only the measurement points of the tangent edges. In a subsequent step, statistical analysis can be performed on the median, 10th percentile, and 90th percentile of the measurement points along the longitudinal axis of the cable. Based on the statistical discretization of measurement points on the longitudinal tangent of the cable, a cutting quality attribute during cable processing can be derived. Furthermore, a cable-specific parameter can include the stripped length at the cable end, and thus can be a length dimension.

[0032] Alternatively or as a supplement, the image measurement algorithm is configured to perform geometric measurements on the probed image in a first step to determine first and second cable-specific image parameters. During this process, the probed image is processed to determine the stripping length of the cable end as the first cable-specific image parameter in a first step. This can be done by measuring the length between the conductor end and the cable insulation layer on the probed image. Simultaneously, the second cable-specific image parameter is determined by identifying the boundary region to the cable insulation layer, or the beginning of the cable insulation layer. For the identified boundary region to the cable insulation layer, the cable's position in the cable processing station can be determined based on the specific design, and / or the correct stripping of the cable insulation layer at the cable end and / or the correct positioning of the cable end sealing element can be determined or checked. Alternatively or as a supplement, the image measurement algorithm is configured to identify dirt or production residues on the probed image, thereby meeting another quality standard for an ideal cable processing process.

[0033] Preferably, the first cable-specific image parameters and / or the second cable-specific image parameters form a parameter set, which at least includes the color, structure, or shape of the conductor and / or cable insulation layer and / or cable shielding layer and / or sealing element. This allows for the identification of different colors and / or different structures and / or different shapes on at least one detection image.

[0034] Color recognition allows for the rapid differentiation of conductors and cable insulation. Furthermore, it identifies the material of the conductors, distinguishing between copper and aluminum wires. Additionally, it can determine plating, such as tin plating on conductors. If the color of the sealing element is identified using first cable-specific image parameters, and the edge structure of the sealing element is identified or inspected using second cable-specific image parameters, then, as a further result, interruptions in cable manufacturing can be avoided, and damage or scrapping of cable connectors and / or cable materials can be prevented.

[0035] Structural identification allows for the differentiation between conductors composed of multi-strand cable cores and single-strand conductors. Furthermore, it identifies the different materials used in the cable insulation. Additionally, structural identification can determine whether the cable and / or sealing elements and / or cable connection elements are free of defects. For example, it can identify defective components that are incorrectly positioned at the cable end. In particular, it can identify defective sealing elements before they are positioned at the cable end, thus preventing such cables from being further processed. This allows for the timely detection of potential rejects, preventing further cable processing steps from being performed using defective sealing elements.

[0036] Shape recognition allows for the identification of incompletely cut or poorly cut cable insulation layers at the cable end after sheathing has been removed. Furthermore, shape recognition can distinguish between unstretched and stranded cable ends and identify whether one or more core wires protrude in the desired direction at the cable end. For example, when installing a sealing element to a cable end, the positioning of at least one core wire can result in an unsealed area at the cable end, which can lead to corrosion damage on the finished cable later. As mentioned above, the image processing system and / or artificial intelligence module autonomously identify different material-specific areas belonging to the conductors, cable insulation, and sealing elements, and as a further result, can prevent the production of defective cables. The aforementioned identification can be performed separately during or after the cable end is processed with a first tool, or in various combinations thereof.

[0037] Preferably, the artificial intelligence module is configured to acquire at least one additional cable-specific image parameter from at least one detection image. This ensures that the at least one control-specific parameter is based on at least one other piece of information about the cable end, or it could be based on a process parameter during cable processing. This process parameter may include the insertion position of the cable end into the first tool. Similar to the aforementioned image parameters, another cable-specific image parameter is identified. Alternatively or supplementarily, the image processing system is configured to acquire at least one additional cable-specific image parameter from at least one detection image.

[0038] In particular, it is possible to correlate a cable end with a cable connector element and another cable-specific image parameter. For example, by analyzing the length of the material-specific region of the cable end and the length of the material-specific region of the cable connector element, the insertion position of the cable end within the cable connector element can be calculated. Specifically, using the aforementioned image measurement algorithm, it is possible to calculate how the areas of the conductor and the cable insulation layer are divided. Similarly, it is possible to measure exactly how much the tip of the conductor protrudes from the fixed area at the very front of the cable connector element; for this purpose, the length of the material-specific region at the very front of the conductor is calculated.

[0039] Preferably, the control device is designed to stop and / or prohibit a certain processing step of at least one first tool based on at least one control-specific parameter. By rejecting defective cable ends, the production of defective cables is avoided. This improves process reliability and prevents subsequent damage to equipment carrying processed cables. A method according to the present invention, for automatically determining and generating control data records and / or control-specific parameters, and implemented by a computer, is used to control at least one cable processing station responsible for processing at least one cable end. This involves detecting at least one image of at least one cable end using a first imaging sensor device, and automatically generating and saving at least one control data record and / or one control-specific parameter. Additionally, an image processing system receives at least one detected image, identifies a first cable-specific image parameter and at least one second cable-specific image parameter, and creates at least one control data record and / or at least one control-specific parameter based on the first and second cable-specific image parameters.

[0040] At least one first cable-specific image parameter and one second cable-specific image parameter will be combined, and based on this, at least one control-specific parameter and / or at least one control data record will be created. By combining at least one first cable-specific image parameter and one second cable-specific image parameter, a specific type of cable can be obtained in the image processing system, which can be processed, or is designed for processing, in the cable processing station described herein. Next, a first tool will be controlled or adjusted based on at least one control-specific parameter, enabling rapid tracking of the cable processing process at the cable processing station.

[0041] Controlling data logging and / or controlling specific parameters will at least control or regulate the movement or operation of the first tool. During this process, the first tool can move from an initial state to an ending state, and / or be activated or deactivated. These movements typically involve controlling tolerances.

[0042] Preferably, at least one control-specific parameter is transmitted to the control device, thereby enabling direct and automatic control or regulation of the first tool.

[0043] Preferably, at least one control data record is transmitted to a storage unit. This allows the control data record to be conveniently stored in the storage unit for long-term retrieval. Preferably, an artificial intelligence module is used to acquire first cable-specific image parameters and at least one second cable-specific image parameter from at least one detection image. The AI ​​module analyzes and processes the at least one detection image. The AI ​​module flexibly and automatically identifies different cable types and different cable connection elements. Alternatively or supplementarily, an image processing system is used to acquire first cable-specific image parameters and at least one second cable-specific image parameter from at least one detection image. The image processing system analyzes and processes the at least one detection image. This allows for convenient and rapid preparation of cable-specific parameters for subsequent processes.

[0044] Preferably, semantic segmentation is performed on at least one probe image, and each pixel of the probe image is associated with at least one cable-specific image parameter. During this process, the probe images are filtered or analyzed to improve image quality, particularly in areas where the cable ends can be identified, thereby determining a first analyzed cable-specific image parameter and a second analyzed cable-specific image parameter.

[0045] Preferably, the artificial intelligence module is trained using at least one cable-specific image parameter, which includes a neural network. The neural network can be trained with the cable-specific image parameter to generate better control data records and / or control-specific parameters in the future, based on the quality of the training or reference images, and to improve future checks on processing steps in the cable processing station. Furthermore, the artificial intelligence module allows for the execution of cable processing procedures or multiple processing steps within a procedure with minimal operator intervention, as the neural network replaces the operator. Downtime in the cable processing station is minimized because the neural network requires no learning process when the processing task changes from one cable processing procedure to another, and no operator adjustments are required on the cable processing machine.

[0046] Preferably, for the cable end, based on the first and second cable-specific image parameters, at least one detection image is divided into at least two material-specific regions. The difference between one material-specific region and the other is that they are made of at least different materials. This allows the cable end to be divided into at least two regions, enabling the image processing system to identify excess cable insulation residue or other foreign objects on the cable end. Furthermore, for coaxial cables, this simple division facilitates the identification of excess cable shielding wires on the insulation, thereby preventing short circuits or defective high-frequency connections at the processed cable end in the future.

[0047] Preferably, for the cable end, based on the analyzed first and second cable-specific image parameters, at least one detection image is divided into at least two material-specific regions. This allows for better identification of excess cable insulation residue or other foreign objects on the cable end, as the detection images are filtered to obtain the analyzed cable-specific image parameters. As a result, later-stage interruptions in cable processing can be avoided, and higher-quality final products can be produced, such as extending the service life of the processed cable.

[0048] Preferably, the first material-specific region is associated with the electrical conductors of the cable, and the second material-specific region is associated with the cable insulation layer. Next, the material-specific regions are measured or analyzed, particularly for surface size and / or region length and / or edge structure, to facilitate identification of the boundary region between the first and second material-specific regions. The position of the first tool and its associated control parameters are calculated based on the size of the material-specific region (length multiplied by width). The region length can be measured to determine the positional changes of the stripping blade serving as the first tool. This processing step is improved because the material-specific region consists of multiple measurement points, ensuring that not only are there measurement points where the cable insulation protrudes from the electrical conductors, but also measurement points on the detected or analyzed image that can identify the transition from the first to the second material-specific region.

[0049] The measurement employs an image measurement algorithm that geometrically measures images. One such algorithm is described in S. and Abe, K., “Topological Structural Analysis of Digitized Binary Images by Border Following”, CVGIP 30 1, pp 32-46 (1985). This allows for the measurement of numerous points, followed by the calculation of the edge structure of the material-specific region. The edge structure can be subdivided into contour points, which are then mathematically filtered using direction vectors to obtain only the measurement points along the cut edge. In a subsequent step, statistical analysis is performed on the median, 10th percentile, and 90th percentile of the measurement points along the longitudinal axis of the cable. Based on the statistical discretization of the measurement points along the longitudinal cut edge of the cable, a cutting quality attribute during cable processing is derived. This allows the determination of the ideal replacement time for the wire stripper blade.

[0050] Preferably, at least one cable-specific image parameter is compared with a rated value of that cable-specific image parameter, and at least one control-specific parameter is created based on this comparison. These rated values ​​may be rated values ​​for the stripping length at the cable end, and / or the width or thickness of the conductor, and typically include tolerance values ​​applicable to the specific cable type rating. Additionally, these rated values ​​may include symmetry or proportional values, such as the ratio between the stripping length and the conductor thickness for the cable type currently being processed. The control-specific parameter thus created may include the position of the stripping blade when cutting into the cable insulation, and / or the stripping length when removing the cable insulation from the cable end. Alternatively or additionally, at least one cable-specific image parameter is compared with a rated value of that cable-specific image parameter, and at least one control data record is created based on this comparison. A control data record typically contains multiple control-specific parameters for controlling one or more tools in the cable processing station. By comparing at least one cable-specific image parameter with a corresponding rated value, the cable type in the cable processing station can be determined, enabling automatic control and execution of the cable processing process.

[0051] Alternatively or as a supplement, at least one material-specific region is compared with a rated value for that material-specific region, and at least one control data record and / or one control-specific parameter is created based on this comparison. A control data record typically contains multiple control-specific parameters for controlling one or more tools in the cable processing station. By comparing at least one material-specific region with a corresponding rated value, the type and size of the cable material can be determined, and consequently, the type of cable in the cable processing station can be determined, enabling it to be automatically controlled and the cable processing process executed.

[0052] Preferably, after comparing the cable-specific image parameters with the corresponding rated values, an average value is calculated in the image processing system. This average value is then compared with the corresponding tolerance value or rated value to create a tracking control-specific parameter. By calculating the average value, erroneous values, such as abnormal deviations in the deviation value, can be filtered out.

[0053] Alternatively or as a supplement, after comparing the material-specific region with the corresponding nominal value, an average value is calculated in the image processing system, which is compared with the corresponding tolerance value or nominal value, and then a tracking control-specific parameter is created.

[0054] Preferably, the measured cable-specific image parameters are compared with corresponding rated values ​​in the image processing system, and a deviation value is created based on the comparison. This allows for improvements in subsequent cable processing procedures.

[0055] Preferably, if there is a deviation in the comparison parameters, the cable in the cable processing process will be discarded to ensure that defective cables do not remain in the cable processing process.

[0056] A cable processing machine conforming to the present invention and having at least two cable processing stations, wherein at least one cable processing station employs the design described herein and can perform the computer-implemented methods described herein, and further includes an image processing system. The image processing system is connected to a central control unit for exchanging control-specific parameters and / or control data records. The image processing system is configured to create at least one control-specific parameter and / or control data record based on a first cable-specific image parameter and a second cable-specific image parameter, and transmit it to the central control unit to control at least one tool at at least one of the two cable processing stations. This allows for fully automated control of each cable processing station. The control-specific parameters and / or control data records are transmitted to the central control unit, rather than to the individual control units of each cable processing station. This results in high reliability of the cable processing process, eliminating the need for an operator on the cable processing machine.

[0057] Preferably, the first imaging sensor device is designed to detect at least one image of the cable end, wherein the cable end is in a state unprocessed by the cable processing station. In this way, at least one material-specific area can be determined based on at least one detected image, particularly in conjunction with the aforementioned cable processing station implementation, thereby allowing the cable type to be determined. Furthermore, an inspection can be performed on the unprocessed cable, thereby enabling the early rejection of damaged cable ends.

[0058] Preferably, the first imaging sensor device is designed to detect a second image of the cable end, wherein the cable end has been processed by at least one cable processing station. This allows for the early creation of first control-specific parameters for controlling the first tool, based on the implementation of the cable processing station described herein.

[0059] Preferably, the first imaging sensor device is designed to detect a single image of the cable end at each existing cable processing station, wherein the cable end has been processed by the corresponding cable processing station. This allows for rapid tracking of the entire cable processing process using multiple cable processing stations should a deviation from the ideal process be detected. Simultaneously, it ensures high reliability of the cable processing process.

[0060] A computer program product conforming to the present invention can be directly loaded into the memory of the central control device of the cable processing machine and / or the control device of the cable processing station described herein, and contains control-specific parameters and / or control data records. When the computer program product is running on the cable processing station or cable processing machine conforming to the present invention, it can execute the steps of one of the aforementioned methods.

[0061] Other advantages, features and details of the invention are described in the following description, wherein embodiments of the invention are illustrated with reference to the accompanying drawings.

[0062] The list of reference numerals, along with the technical content and illustrations in the patent claims, are integral parts of the patent disclosure. The drawings are presented in a related and comprehensive manner. The same reference numerals indicate the same parts, while reference numerals with different symbols indicate parts that have the same or similar functions. Attached Figure Description

[0063] Figure 1 A schematic diagram of a first embodiment of a cable processing station conforming to the present invention.

[0064] Figure 2 A schematic diagram of an image detected by a sensor device at the end of a first cable.

[0065] Figure 3 Figure 2 A segmentation map of the image.

[0066] Figure 4 Figure 3 A segmented view of the image with a first cable connection element.

[0067] Figure 5 A split view of the other cable end of another cable with another cable connector.

[0068] Figure 6 A segmented diagram of one end of a high-voltage cable.

[0069] Figure 7 Figure 6 Another segmented image in the middle, showing the high-voltage cable end sleeve.

[0070] Figure 8 A split front view of one end of another high-voltage cable.

[0071] Figure 9 Figure 8 Another split side view of the image shows a section of high-voltage cable tape.

[0072] Figure 10 Figure 9 Another segmented front view of the image.

[0073] Figure 11 A first flowchart of a control method for a cable processing station is published.

[0074] Figure 12 With conformity Figure 1 A perspective view of a cable processing machine conforming to the present invention at a cable processing station.

[0075] Figure 13 conform to Figure 12 A schematic diagram of a cable processing machine, and

[0076] Figure 14 Another document discloses a cable processing machine (compliant with...) Figure 12 )Flowchart of the control method. Detailed Implementation

[0077] Figure 1A cable processing station 20 is shown for processing a cable end 12 of a cable 10. It includes at least one first tool 22 for processing the cable 10 and a control device 40 for controlling at least one of the first tools 22. The cable 10 shown has different cable ends 12a-12d, which can be processed at least with the first tool 22 in different processing steps of the cable processing station 20. The cable 10 with cable end 12a is unprocessed and has a cable insulation layer 13 that surrounds the conductor 14 in sections and extends from the end face of the cable 10. After another processing step, the same cable 10 has a cable end 12b, where the conductor 14 is exposed after stripping, and the cable insulation layer 13 is removed from the area of ​​the cable end 12b. After another processing step, the same cable 10 has a cable end 12c, where a sealing element 15 is arranged in the area of ​​the cable end 12c. After another processing step, the same cable 10 has a cable end 12d, in which a cable connector 16 is arranged and crimped. The first tool 22 of the cable processing station 20 and the cable 10 can move relative to each other in the direction of movement 23. Depending on the specific processing step, the first tool 22 may include (but is not limited to) a stripping blade for removing the cable insulation layer 13, an assembly device for assembling the sealing element 14, and a crimping tool for crimping the cable connector 16 onto the cable 10. Furthermore, the cable processing station 20 includes at least one first imaging sensor device 25 for detecting at least one image of the cable ends 12a-12d of the cable 10, and an image processing system 30. The first imaging sensor device 25 is a camera and has a zoom lens and numerous commonly used filter elements 27. To exchange control-specific parameters, the image processing system 30 is electrically connected to and configured with the control device 40 to identify a first cable-specific image parameter and at least one second cable-specific image parameter from at least one detected image, and to create at least one control-specific parameter based on the first and second cable-specific image parameters, which is then transmitted to the control device 40 for controlling the first tool 22. The first cable-specific image parameter identifies the conductor 14, and the second cable-specific image parameter identifies the cable insulation layer 13. In this way, the image processing system 30 identifies the specific cable type of the cable 10. The image processing system 30 has an artificial intelligence module 32, which is connected to and configured with at least one first imaging sensor device 25 to acquire the first cable-specific image parameter and at least one second cable-specific image parameter from at least one detected image.For a specific cable type, an AI (artificial intelligence) module can be trained using external or individual image data or parameters (material, structure, color, shape, etc.). To this end, the AI ​​module 32 has a computing unit 33 and a neural network 34, designed to analyze at least one detection image, wherein the neural network 34 can be trained.

[0078] Artificial intelligence module 32 performs semantic segmentation on at least one detection image to associate each pixel of the detection image with at least one cable-specific image parameter, and transmits the analyzed first cable-specific image parameter and at least one analyzed second cable-specific image parameter to image processing system 30. Image processing system 30 is configured to, for cable ends 12a-12d, segment at least one detection image into at least two material-specific regions based on the analyzed first and second cable-specific image parameters (see also...). Figure 3 and 4 The difference between one material-specific region and another is that they are made of at least different materials. Furthermore, a database 50 is electrically connected to the image processing system 30, wherein the database 30 has a storage unit 55. The storage unit 55 stores reference images of different cable ends 12a-12d, different cable types and / or seals 15 and cable connectors 16, which can be processed in the cable processing station 20 and retrieved by the image processing system 30. Additionally, the storage unit 55 stores reference profiles in vector form for different cable ends 12a-12d, different cable types and / or seals 15 and cable connectors 16, which can be processed in the cable processing station 20 and retrieved by the image processing system 30. Furthermore, the database 50 stores control-specific parameters and / or control data records, through which at least one first tool 22 can be controlled or regulated. Additionally, the storage unit 55 of the database 50 stores the rated values ​​of the cable-specific image parameters. These ratings may be ratings for the stripping length of the cable end 12b, and / or the width or thickness of the conductor 14, and typically include tolerance values ​​applicable to the specific ratings. Additionally, these ratings may include symmetrical or proportional values, such as the ratio between the stripping length and the thickness of the conductor 14 for the type of cable currently being processed. Furthermore, the ratings for segmented material-specific regions are stored in storage unit 55 of database 50 and can be retrieved by image processing system 30. These ratings may include ratings for surface size, surface perimeter, or surface diameter, or ratings for the edge structure of a specific material-specific region, or combinations thereof, and have corresponding tolerance values.

[0079] The image processing system 30 is configured to acquire at least first cable-specific image parameters and / or at least analyzed first cable-specific image parameters from corresponding images of the cable ends 12a-12d of the probed cable 10 using an image measurement method. This applies to the conductor 14 and / or the cable insulation 13 and / or the cable shield (see...). Figure 5 The color, structure, or shape of the sealing element 15 and / or the color, structure, or shape of the first cable-specific image parameters and / or the second cable-specific image parameters are used to identify the image parameters.

[0080] The image measurement method includes at least one image measurement algorithm that can process probed and / or segmented images. The image measurement algorithm is configured to perform geometric measurements on the probed or segmented images in a first step to determine first and second cable-specific image parameters and to identify edge structures. Edge structures can be subdivided into contour points, and then mathematically filtered using direction vectors to obtain only measurement points along the cut edges. In a subsequent step, statistical analysis can be performed on the median, 10th percentile, and 90th percentile of the measurement points along the longitudinal axis of cable 10. Based on the statistical discretization of the measurement points along the longitudinal cut edges of cable 10, a cutting quality attribute in the cable processing process can be derived.

[0081] The image processing system 30 compares cable-specific image parameters and / or material-specific regions in the detected or analyzed images with reference images or corresponding nominal values ​​in the database 50, and creates control-specific parameters, which are transmitted to the control device 40. The control device 40 controls the drive device 24 of the first tool 22 using the control-specific parameters. Figures 2 to 4 A first embodiment of the aforementioned images is shown. According to this example, the state of the cable ends 12a-12d from the processing steps and technological steps described herein is shown graphically. Figure 2 An image of a cable end 12b detected by sensor device 25 is shown. The cable end 12b of cable 10 has a colored (blue) cable insulation layer 13 and electrical wires 14 with multi-strand cores that are copper-colored and twisted. Figure 3The image shows a segmented cable end 12b using an artificial intelligence module 32, where each pixel of the probed image is associated with at least one cable-specific graphic parameter. Here, if a pixel of the probed image does not relate to the cable end 12b or the cable 10, a background signal (e.g., black) is associated with that pixel as a cable-specific parameter. The cable insulation layer 13 is shown in monochrome, and the multi-strand conductor 14 has a dashed area. The artificial intelligence module is trained accordingly and assigns the conductor 14 to a first material-specific region, while assigning the cable insulation layer 13 to a second material-specific region. Regardless of... Figure 2 China or Figure 3 The cable ends 12b shown have a section of core wire 14a that deviates from and protrudes from the original orientation of the conductor 14. As described here, the image processing system 30 measures this area 18 and compares the analyzed cable-specific image parameters with the nominal values ​​in the database 50. Figure 4 The image shows the cable end 12d of cable 10 segmented using artificial intelligence module 32, wherein cable 10 has a cable connector 16 designed as a plug. Cable connector 16 is secured or crimped to two crimped areas 16a, conductors 14, and cable insulation 13 using a tool 22 designed as a crimping tool. For the aforementioned areas or components, the segmented image shows associated color or dashed areas, specifically indicating their color and / or structure and / or shape, and is identified and measured by image processing system 30 so that a new control-specific parameter or control data record can be transmitted to control device 40 if necessary. The aforementioned ratings may include the coating value of the core wire on conductor 14, or the length value of the protruding core wire 14a, or the tolerance value of residual dirt on core wire 14a, and may also be stored in storage unit 55 of database 50.

[0082] Similar to Figure 4 Regarding the aforementioned image of cable end 12d′, Figure 5Another embodiment is shown, in which the cable 10' is shown as a coaxial cable, on which another plug is arranged as a cable connector 16'. The cable connector 16' is secured or crimped to the two crimped areas 16a', the conductor 14', and the cable insulation layer 13' using a tool designed as a crimping tool. Additionally, the cable 10' has an insulator 15' and a cable shield 17'. For the aforementioned areas or components, the segmented image shows associated markers or dashed areas, specifically indicating their color and / or structure and / or shape, and is identified and measured by the image processing system 30. As described here, the image processing system 30 measures the corresponding area 18 so that, if necessary, a new control-specific parameter or control data record can be created and transmitted to the control device 40.

[0083] Similar to Figures 2 to 5 Regarding the aforementioned image of the cable end 12d″, Figure 6 and 7 Another embodiment is shown, in which cable 10″ is shown as a high-voltage cable. The high-voltage cable 10″ has an electrical conductor 14″, an inner cable insulation layer 13″, and an outer cable insulation layer 13a″, wherein a cable shielding layer 17″ and a membrane layer 17a″ are arranged between the cable insulation layers 13″ and 13a″. For the aforementioned regions or components, the segmented image shows associated markers or dashed areas, specifically indicating their color and / or structure and / or shape, and is identified and measured by the image processing system 30. As described here, the image processing system 30 measures the corresponding region 18″ to create a new control-specific parameter or control data record, and transmits it to the control device 40. For example, for such high-voltage cables, it is important that the membrane layer 17a″ does not protrude beyond the outer cable insulation layer 13a″, wherein the control-specific parameter or control data record, used as a first tool, ensures this and controls the stripping blade accordingly.

[0084] like Figure 7 Another segmentation image is shown in the image. Figure 6The high-voltage cable is fitted with an end sleeve 19″, and the cable shield 17″ is folded over the end sleeve 19″. The folding of the cable shield 17″ can be achieved using a rotatable brush as a first tool. As described here, the image processing system 30 measures region 18″, which defines the overlap area of ​​the folded cable shield 17″ above the end sleeve 19″, wherein newly created control-specific parameters or control data records for the stripper blade and / or rotatable brush ensure sufficient overlap and control the stripper blade and / or rotatable brush. In addition to the end sleeve 19″, a section of tape can also be placed on the outer cable insulation layer 13a″, wherein the image processing system 30 identifies whether the entire cable shield is placed under the tape (not shown). Similar to... Figure 6 and Figure 7 Regarding the aforementioned image of the cable end 12d″′, Figures 8 to 10 Another embodiment is shown, in which cable 10″′ is shown as a high-voltage cable as a cable type.

[0085] Figure 8 A front view of a high-voltage cable or cable 10″′ in its unprocessed state is shown. The cable 10″′ has a first conductor 14″′ and a second conductor 14a″′, which respectively include an inner cable insulation layer 13″′, 13a″′ and an outer cable insulation layer 13c″′. An insulating filler 13b″′ is arranged between the inner cable insulation layers 13″′, 13a″′ and the outer cable insulation layer 13c″′. The insulating filler 13b″′ is wrapped by a cable shielding layer 17″′. Figure 9 and Figure 10 A side view of the cable 10″′ after the wire stripping process is shown. Figure 9 ) and front view ( Figure 10 ), among which, except Figure 8 In addition to the components shown, an end sleeve 19″′ is shown, which is located on the outer cable insulation layer 13c″′ and the cable shielding layer 17″′ is folded around the end sleeve 19″′. The cable shielding layer 17″′ is secured to the outer cable insulation layer 13c″′ with an adhesive strip 17b″′.

[0086] Regarding the aforementioned areas or components Figures 8 to 10The segmented image shows associated markers or dashed areas, specifically indicating their color and / or structure and / or shape, which are identified and measured by the image processing system 30. As described here, the image processing system 30 measures the corresponding areas 18″′, 18a″′ to create a new control-specific parameter or control data record and transmit it to the control device 40. For example, for such high-voltage cables, it is important that the two conductors are free of any kinks. The image processing system 30 checks this using an image detected by the imaging sensor device 25, specifically detecting a front view of the unprocessed high-voltage cable 10″′. Figure 8 The image processing system 30 identifies the positions of the two electrical wires 14″′ and 14a″′. This forms the reference basis for subsequent wire stripping steps, as the orientation or position (torsion relative to the horizontal plane) of the two electrical wires 14″′ and 14a″′ can be determined here. Furthermore, the image processing system 30 measures the corresponding areas 18″′ and 18a″′ around the electrical wires 14″′ and 14a″′, which serve as reference bases for control-specific parameters or control data recording for the wire stripping blade or cable conveyor used as the first tool, in order to control these tools accordingly. Figure 9 The stripped high-voltage cable and conductors 14″′ and 14a″′, maintained at a certain distance from each other, are shown. Figure 10 This shows a front view of the stripped high-voltage cable and the kinked wires 14″′ and 14a″′ maintained at a certain distance from each other. The measurement areas 18″′ and 18a″′ have correspondingly different dimensions, allowing the image processing system 30 to compare these areas 18″′ using stored data records to define rejects if necessary. Otherwise, specific parameters or control data records are used in subsequent processing steps to control a cable feeder or crimping tool, which can be used as another tool.

[0087] The front view shown can be particularly used with the imaging sensor device 25. Figures 2 to 7 The cable shown is probed, and cable-specific parameters and / or material-specific regions that can be created based on this can be used by the image processing system 30 to create control-specific parameters that can improve the positioning of the sealing element or the positioning of the end sleeve on the cable end. Furthermore, in another embodiment, the imaging sensor device 25 is configured to detect the position of at least one of the tools described herein, such as the position of a crimping tool, thereby accurately positioning the cable (not shown) within the cable plug housing.

[0088] Figure 11A first method for automatically determining and generating control data records and / or control-specific parameters is shown to at least control cable processing station 20. This method is implemented via a computer and stored in the control device 40 of cable processing station 20. The following will be discussed... Figure 1 For reference. The methods described below can be used to identify and generate control data records and / or control-specific parameters for processing. Figures 1 to 10 The cable shown.

[0089] In one first step, an infinitely long cable or a cable 10 that has been cut to a defined length is conveyed to the cable processing station 20 (step 100).

[0090] Next, work order data for processing cable 10, including control data records and / or control-specific parameters for controlling the first tool 22, will be loaded from database 50 into control device 40 (step 101). In addition, the work order data for the cable 10 to be processed also includes corresponding control tolerances, cable-specific image parameters, and / or material-specific areas and tolerance values.

[0091] Next, the cable end 12a is processed, during which the cable insulation layer 13 is stripped (step 102). Next, an image 26 of at least one cable end 12b of the cable 10 is detected using the first imaging sensor device 25 (step 103).

[0092] Next, the detection image 26 is transmitted to the image processing system 30, and a first cable-specific image parameter and at least one second cable-specific image parameter are identified. The first and second cable-specific image parameters are analyzed by the artificial intelligence module 32, and semantic segmentation is performed on the detection image 26 (step 104). Next, based on the first and second cable-specific image parameters, the detection image 26 of the cable end 12b is segmented into at least two material-specific regions, which include the cable insulation layer 13 and the conductor 14. Geometric measurements of the material-specific regions are performed using the image measurement algorithm in the computing unit of the image processing system 30 (step 105).

[0093] Next, at least one of the measured cable-specific image parameters is compared with the rated value of that cable-specific image parameter, where the rated value is derived from the work order data (step 106). During this process, a deviation value is determined. After comparing the measured cable-specific image parameter with the corresponding rated value, it is determined whether the deviation value is within the control tolerance range of the control-specific parameter (step 107).

[0094] If the measured cable-specific image parameters are outside the corresponding control tolerances, the cable processing procedure is cancelled, and a fault message is transmitted to control device 40 or the operator of the cable processing station (step 109). For example, the fault message may request a replacement of the first tool 22 and will be visually displayed on a monitor in cable processing station 20. The defective cable will be discarded.

[0095] If the measured cable-specific image parameters are within the control tolerance range corresponding to the first tool control-specific parameters, an average value will be calculated in the image processing system 30 (step 108).

[0096] Based on the calculated average value, at least one new control data record and / or at least one new control-specific parameter will be created, saved in storage unit 55, and transmitted to control device 40 for control or regulation of tool 22 (step 110).

[0097] In a subsequent step 111, the measured individual cable-specific image parameters are compared with the corresponding rated values ​​or the tolerance values ​​of the corresponding rated values ​​in the image processing system 30. If there is a deviation in the comparison parameters, the cable 10 in the cable processing process is discarded (step 112), or the cable processing process is terminated (step 113). Figure 12 and 13 A cable processing machine 120 with multiple cable processing stations 60, 70, and 80 is shown. A cable conveyor 90 is arranged on the cable processing machine 120 for conveying cables 10 to the cable processing stations 60, 70, and 80. Figure 1 As shown and described, cable 10 is processed here, and there are multiple cable processing stations 60, 70, 80, each with an independent tool, rather than a single cable processing station 20 with multiple tools 22. Figure 10 The cable processing machine 120 shown has a cable processing station 60 for stripping the cable insulation 13 from the cable 10, a cable processing station 70 for assembling a sealing element 15 onto the cable 10, and a cable processing station 80 for crimping a cable connector 16 onto the cable 10. Cable processing stations 60, 70, and 80 each have a tool, namely a stripping blade 61, an assembly device 71, and a crimping tool 81, each including a drive unit and electrically connected to a central control unit 140 for exchanging control-specific parameters and / or recording control data.

[0098] like Figure 1As described, an image processing system 30 is electrically connected to a central control unit 140 of the cable processing machine 120 for exchanging control-specific parameters and / or control data records. The image processing system 30 is configured to create at least one control-specific parameter and / or a control data record based on first and second cable-specific image parameters, and transmit it to the central control unit 140 for controlling the tools at cable processing stations 60, 70, and 80. A first imaging sensor device 25 is designed to detect at least one independent image 62, 72, or 82 of the cable end of the cable 10 at each cable processing station 60, 70, and 80 prior to each processing step.

[0099] Figure 14 Another method is shown for automatically determining and generating control data records and / or control-specific parameters to control at least a cable processing machine 120 with multiple cable processing stations 60, 70, 80, which are implemented by a computer and stored in a central control unit 140 of the cable processing machine 120. The method described below at least partially includes Figure 11 The method published in [the document]. Next, we will... Figure 1 , Figure 12 as well as Figure 13 For reference. The methods described below can be used to identify and generate control data records and / or control-specific parameters for processing. Figures 1 to 10 The cable shown. In a first step, an infinitely long cable or a cable 10 already cut to a defined length is conveyed to the cable processing station 60 via a cable conveyor 90 (step 200).

[0100] Next, the work order data for processing cable 10, including control data records and / or control-specific parameters for controlling the stripper blade 61, will be loaded from the storage unit 55 of the database 50 into the central control device 140 (step 201). In addition, the work order data for the cable 10 to be processed also includes the corresponding control tolerances, as well as cable-specific image parameters and / or material-specific areas and tolerance values.

[0101] Next, the cable end 12a is processed, during which the cable insulation layer 13 is stripped (step 202). Next, a first image 62 of at least one cable end 12b is detected using the first imaging sensor device 25 (step 203).

[0102] Next, the probe image 62 is transmitted to the image processing system 30, and a first cable-specific image parameter and at least one second cable-specific image parameter are identified. The first and second cable-specific image parameters are analyzed by the artificial intelligence module 32, and semantic segmentation is performed on the probe image 62 (step 204). Next, based on the first and second cable-specific image parameters, the probe image 62 of the cable end 12b is segmented into at least two material-specific regions associated with the cable insulation layer 13 and the conductor 14. Geometric measurements of these material-specific regions are performed in the computational unit of the image processing system 30 using image measurement algorithms, taking into account their surface dimensions and / or region lengths and / or edge structures (step 205).

[0103] Next, at least one cable-specific image parameter will be compared with a rated value of that cable-specific image parameter, or at least one material-specific region will be compared with a rated value of that material-specific region, wherein the corresponding rated value comes from the work order data (step 206). During this process, a deviation value will be determined.

[0104] After comparing the cable-specific image parameters or material-specific regions with the corresponding rated values, it is determined whether the deviation value is within the control tolerance range of the control-specific parameters (step 207).

[0105] If the measured cable-specific image parameters or material-specific areas are outside the corresponding control tolerances, the cable processing process will be cancelled, and a fault message will be transmitted to the central control unit 140 or the operator of the cable processing station (step 209). For example, the fault message may request a tool replacement and will be visually displayed on a monitor at the cable processing station. The defective cable will be discarded.

[0106] If the measured cable-specific image parameters or material-specific areas are within the control tolerance range corresponding to the control-specific parameters of the wire stripper 61, an average value will be calculated in the image processing system 30 (step 208).

[0107] In a subsequent step 211, the material-specific area is compared with the corresponding tolerance value in the image processing system 30, wherein if there is a deviation in the deviation value, the cable 10 in the cable processing process is discarded (step 212).

[0108] In a subsequent step, the previously processed cable 10 is conveyed to the cable processing station 70 along the movement direction 23 using a cable conveyor 90, and the work order data for processing the cable 10 is loaded from the storage unit 55 of the database 50 into the central control unit 140. This data includes control data records and / or control-specific parameters for controlling the assembly device 71 and / or the cable conveyor 90 for assembling the sealing element 15. Next, the cable end 12c is processed, wherein the sealing element 15 is arranged on the cable end 12c (step 300). In addition, the work order data for the cable 10 to be processed also includes corresponding control tolerances and cable-specific image parameters and / or material-specific areas and tolerance values.

[0109] Next, a second image 72 of the cable end 12c, which has at least one cable, will be detected using the first imaging sensor device 25 (step 302).

[0110] Next, the second detection image 72 is transmitted to the image processing system 30, and a first cable-specific image parameter, a second cable-specific image parameter, and another / third cable-specific image parameter are identified. The first cable-specific image parameter, the second cable-specific image parameter, and the third cable-specific image parameter are analyzed by the artificial intelligence module 32, and a semantic segmentation is performed on the detection image 72 (step 303).

[0111] Next, based on the first cable-specific image parameters, the second cable-specific image parameters, and the third cable-specific image parameters, the second detection image 72 of the cable end 12c will be divided into at least three material-specific regions, which are associated with the cable insulation layer 13, the conductor 14, and the sealing element 15. Furthermore, the material-specific regions will be geometrically measured in the calculation unit of the image processing system 30 using an image measurement algorithm based on their surface dimensions and / or region lengths and / or edge structures (step 304).

[0112] Next, at least one cable-specific image parameter will be compared with a rated value of that cable-specific image parameter, or at least one material-specific region will be compared with a rated value of that material-specific region, wherein the corresponding rated value comes from the work order data (step 305). During this process, a deviation value will be determined.

[0113] After comparing the cable-specific image parameters or material-specific regions with the corresponding rated values, it is determined whether the deviation value is within the control tolerance range of the control-specific parameters (step 307).

[0114] If the measured cable-specific image parameters or material-specific areas are outside the corresponding control tolerances, the cable processing process will be cancelled, and / or a fault message will be transmitted to the central control unit 140 or the operator of the cable processing station (step 309). For example, the fault message may request cleaning tools and will be visually displayed on a monitor at the cable processing station. The defective cable will be discarded.

[0115] If the measured cable-specific image parameters or material-specific areas are within the control tolerance range corresponding to the control-specific parameters of the assembly device 71 and / or cable conveyor 90, an average value will be calculated in the image processing system 30 (step 308).

[0116] Based on the calculated average value, at least one new control data record and / or at least one new control-specific parameter will be created, saved in storage unit 55, and transmitted to central control device 140 to control or regulate assembly device 71 and / or cable conveyor 90 (step 310).

[0117] In a subsequent step 311, the material-specific area is compared with the corresponding tolerance value in the image processing system 30, wherein if there is a deviation in the deviation value, the cable 10 in the cable processing process is discarded (step 212).

[0118] In a subsequent step, the previously processed cable 10 is conveyed along the movement direction 23 to the cable processing station 80 by the cable conveyor 90, and the work order data for processing the cable 10 is loaded from the storage unit 55 of the database 50 into the central control unit 140. This data includes control data records and / or control-specific parameters for controlling the crimping tool 81 and / or the cable conveyor 90 for crimping a cable connector 16 to the cable end of the cable 10. Next, the cable end 12d is processed, wherein the cable connector 16 is arranged on the cable end 12d (step 400).

[0119] Next, a third image 82 (step 402) will be detected using the first imaging sensor device 25 at least one cable end 12d of the cable 10.

[0120] Next, the third detection image 82 is transmitted to the image processing system 30, and a first cable-specific image parameter, a second cable-specific image parameter, a third cable-specific image parameter, and another cable-specific image parameter are identified. The first cable-specific image parameter, the second cable-specific image parameter, the third cable-specific image parameter, and the other cable-specific image parameter are analyzed by the artificial intelligence module 32, and a semantic segmentation is performed on the third detection image 82 (step 403).

[0121] Next, based on the first cable-specific image parameters, the second cable-specific image parameters, the third cable-specific image parameters, and another cable-specific image parameters, the third detection image 82 of the cable end 12d will be divided into at least four material-specific regions, which are associated with the cable insulation layer 13, the conductor 14, the sealing element 15, and the cable connection element 16. The material-specific regions will be geometrically measured in the calculation unit of the image processing system 30 using an image measurement algorithm for their surface dimensions and / or region lengths and / or edge structures (step 404).

[0122] Next, at least one cable-specific image parameter will be compared with a rated value of that cable-specific image parameter, or at least one material-specific region will be compared with a rated value of that material-specific region, wherein the corresponding rated value comes from the work order data (step 405). During this process, a deviation value will be determined.

[0123] After comparing the cable-specific image parameters or material-specific regions with the corresponding rated values, it is determined whether the deviation value is within the control tolerance range of the control-specific parameters (step 407).

[0124] If the measured cable-specific image parameters or material-specific areas are outside the corresponding control tolerances, the cable processing process will be cancelled, and a fault message will be transmitted to the central control unit 140 or the operator of the cable processing station (step 409). For example, the fault message may request a tool replacement or check for tool damage, and will be visually displayed on a monitor at the cable processing station. The defective cable will be discarded.

[0125] If the measured cable-specific image parameters or material-specific areas are within the control tolerance range corresponding to the control-specific parameters of the crimping tool 81 and / or cable conveyor 90, an average value will be calculated in the image processing system 30 (step 408).

[0126] Based on the calculated average value, at least one new control data record and / or at least one new control-specific parameter are created, saved in storage unit 55, and transmitted to central control device 140 to control or regulate crimping tool 81 and / or cable conveyor 90 (step 410).

[0127] Next, the material-specific area will be compared with the corresponding tolerance value in the image processing system 30. If there is a deviation in the deviation value, the cable 10 in the cable processing process will be discarded (step 212).

[0128] Next, the processed cable 10 will be placed in a cable storage area (step 411).

[0129] List of reference numerals

[0130] 10 Cables

[0131] 10′ cable

[0132] 10″ cable (high voltage cable)

[0133] 10″′ Cable (High Voltage Cable)

[0134] 12a-12d 10 cable ends

[0135] 12d′ 10′ cable end

[0136] 12d″ 10″ cable end

[0137] I2d″′ 10′″ cable end

[0138] 13 10 cable insulation layer (jacket)

[0139] 13′ 10′ cable insulation jacket

[0140] 13″ Inner cable insulation

[0141] 13a″ Outer isolation layer of cable

[0142] 13″′ Inner cable insulation layer

[0143] 13a″′ Internal cable insulation layer

[0144] 13b″′ Insulating filler

[0145] 13c″′ Outer insulation layer of the cable

[0146] 14 10 wire (strand)

[0147] 14′ 10′ wire

[0148] 14″ 10″ wire

[0149] The first conductor (strand) is 14″′ 10″′.

[0150] The second conductor (strand) is 14a″′ 10″′.

[0151] 15 10 sealing element (dielektric)

[0152] 15′ 10′ insulator (dielektric)

[0153] 16 10 cable connection element (terminal)

[0154] 16′ 10′ cable connection element (crimp region)

[0155] The crimp region of 16a′ and 10′.

[0156] 17′ 10′ cable shield

[0157] 17″ 10″ cable shield

[0158] A 17a″ 10″ film (foil)

[0159] 17″′ 10″′ cable shield

[0160] 17a″′ Tape

[0161] 18 areas

[0162] 18″ area

[0163] 18″′ area

[0164] 18a″′ area

[0165] 19″ Ferrule

[0166] 19″′ Ferrule

[0167] 20 Cable processing station

[0168] 22 First Tool

[0169] 23 22 moving direction

[0170] 24 22 drive unit

[0171] 25 Imaging sensor devices

[0172] 26 images

[0173] 27 25 filter element

[0174] 30 Image Processing System

[0175] 32 Artificial Intelligence Module

[0176] 33 Computing Units

[0177] 34 Neural Networks

[0178] 40 Control device

[0179] 50 databases

[0180] 55 storage units

[0181] 60 Cable processing station

[0182] 61 Wire stripper blades

[0183] 62 First Image

[0184] 70 Cable processing station

[0185] 71 Assembly device

[0186] 72 Second Image

[0187] 80 Cable processing station

[0188] 81 Crimping tool

[0189] 82 Third Image

[0190] 90 Cable Conveyor

[0191] 120 Cable Processing Machine

[0192] 140 Central Control Unit

[0193] Processing steps 100-113

[0194] 200-411 Processing Steps

Claims

1. A cable processing station (20; 60; 70; 80) for processing cable ends (12a-12d; 12d') of a cable (10; 10'), comprising at least one first tool (22; 61; 71; 81; 90) for processing the cable (10; 10'), a control device (40) for controlling at least one of the first tools (22; 61; 71; 81; 90), and at least one first imaging sensor device (25) for detecting at least one image (62, 72, 82) of at least one cable end (12a-12d; 12d') of the cable (10; 10'), and an image processing system (30) connected to the control device (40), wherein, The image processing system (30) is configured to identify a first cable-specific image parameter of the cable ends (12a-12d; 12d') and the cable ends (12a-12d; 12d') from at least one probe image. A second cable-specific image parameter (12d') is obtained, and at least one control-specific parameter is created based on the first cable-specific image parameter and the second cable-specific image parameter, and the at least one control-specific parameter is transmitted to the control device (40) for controlling the first tool (22; 61; 71; 81; 90), wherein the control device (40) is configured to perform the processing steps of the first tool (22) using the transmitted at least one new control-specific parameter, and wherein the first cable-specific image parameter and the second cable-specific image parameter are selected from a group of parameters related to the inherent characteristics of the cable, consisting of material, structure, color, shape, cable insulation layer, cable shielding layer, sealing element, cable connection element and cable type.

2. The cable processing station according to claim 1, characterized in that, include: An artificial intelligence module (32) is connected to and configured to acquire first cable-specific image parameters and at least one second cable-specific image parameters from at least one of the probe images (62; 72; 82).

3. The cable processing station according to claim 2, characterized in that, The artificial intelligence module (32) includes at least one neural network (34) designed to analyze at least one of the probe images (62; 72; 82) and perform a semantic segmentation on at least one of the probe images (62; 72; 82) to associate each pixel of the probe image (62; 72; 82) with at least one cable-specific image parameter, and transmit the analyzed first cable-specific image parameter and the analyzed second cable-specific image parameter to the image processing system (30).

4. The cable processing station according to any one of claims 1 to 3, characterized in that, The image processing system (30) is configured to perform a segmentation on at least one of the probe images (62; 72; 82).

5. The cable processing station according to any one of claims 1 to 4, characterized in that, The image processing system (30) was configured to process the cable (10) based on the analyzed first cable-specific image parameters and the analyzed second cable-specific image parameters; The cable end (12a-12d; 12d') of 10' is at least one of the detection images (62; 72; 82) divided into at least two material-specific regions.

6. The cable processing station according to claim 5, characterized in that, The first material-specific region includes at least the electrical conductors (14; 14') of the cable (10; 10') and the second material-specific region includes at least the cable insulation layer (13; 13').

7. The cable processing station according to any one of claims 2 to 3, characterized in that, There exists a database (50) connected to the image processing system (30) and / or the artificial intelligence module (32), wherein the database (50) has at least one storage unit (55) and stores reference images and / or reference contours for different cable ends (12a-12d; 12d') in at least one of the storage units (55).

8. The cable processing station according to claim 7, characterized in that, At least one rating is stored in at least one of the storage units (55) for at least one cable-specific image parameter and / or at least one material-specific region on the cable end (12a-12d; 12d').

9. The cable processing station according to any one of claims 1 to 8, characterized in that, The image processing system (30) is configured to acquire at least first cable-specific image parameters and / or at least analyzed first cable-specific image parameters from the probe images (62; 72; 82) of the cable ends (12a-12d; 12d') of the cable (10; 10') using an image measurement method.

10. The cable processing station according to claim 6, characterized in that, The first cable-specific image parameters and / or the second cable-specific image parameters are a parameter group, which includes at least the color, structure or shape of the conductor (14; 14') and / or the cable insulation layer (13; 13') and / or a cable shield (17') and / or a sealing element (15).

11. The cable processing station according to claim 3, characterized in that, The artificial intelligence module (32) and / or the image processing system (30) are configured to acquire at least one additional cable-specific image parameter from at least one probe image (62; 72; 82), which can be associated with a cable connection element (15) at the cable end (12a-12d; 12d') of the cable (10; 10'), and the neural network (34) is designed in the artificial intelligence module (32).

12. The cable processing station according to any one of claims 1 to 11, characterized in that, The control device (40) is designed to stop and / or prevent at least one of the processing steps of the first tool (22; 61; 71; 81; 90) based on at least one control-specific parameter.

13. A method for automatically determining and generating control data records and / or control-specific parameters, implemented by a computer, for controlling at least one cable processing station (20; 60; 70; 80), wherein, An image (62; 72; 82) of at least one of the cable ends (12a-12d; 12d') is detected by a first imaging sensor device (25), and at least one control data record and / or a control-specific parameter is automatically generated and saved. In addition, an image processing system (30) is provided that receives the detected image (62; 72; 82). 72; 82), and identify a first cable-specific image parameter and at least one second cable-specific image parameter, and create at least one control data record and / or at least one control-specific parameter based on the first cable-specific image parameter and the second cable-specific image parameter, wherein the first cable-specific image parameter and the second cable-specific image parameter are selected from a group of parameters related to the inherent characteristics of the cable, consisting of material, structure, color, shape, cable insulation layer, cable shielding layer, sealing element, cable connection element and cable type.

14. The method implemented by a computer according to claim 13, characterized in that, At least one control-specific parameter will be transmitted to the control device (40), and at least one control data record will be transmitted to a storage unit (55).

15. The computer-implemented method according to claim 13 or 14, characterized in that, First cable-specific image parameters and second cable-specific image parameters are acquired from at least one of the detection images (62; 72; 82) using an artificial intelligence module (32), wherein the artificial intelligence module (32) and / or the image processing system (30) analyze at least one of the detection images (62; 72; 82).

16. The computer-implemented method according to any one of claims 13 to 15, characterized in that, An artificial intelligence module (32) and / or the image processing system (30) performs semantic segmentation on at least one of the probe images (62; 72; 82) and associates each item of the probe image with at least one cable-specific image parameter.

17. The computer-implemented method according to claim 15 or 16, characterized in that, The artificial intelligence module (32) will be trained using at least one cable-specific image parameter.

18. The computer-implemented method according to any one of claims 13 to 17, characterized in that, Based on the analyzed first cable-specific image parameters and the analyzed second cable-specific image parameters, the cable ends (12a-12d; 12d') of the cable (10; 10') are divided into at least two material-specific regions by the detection image (62; 72; 82) of at least one cable end (12a-12d; 12d').

19. The computer-implemented method according to any one of claims 13 to 18, characterized in that, Compare at least one cable-specific image parameter with a rated value of that cable-specific image parameter, and create at least one control-specific parameter and / or at least one control data record based on the comparison.

20. A cable processing machine (120) having at least two of the aforementioned cable processing stations (20; 60; 70; 80), wherein, At least one cable processing station (20) according to any one of claims 1 to 12 is designed to perform the computer-implemented method according to any one of claims 13 to 19, characterized in that the image processing system (30) is connected to a central control device (140) for exchanging control-specific parameters and / or control data records, and the image processing system (30) is configured to create at least one control-specific parameter and / or a control data record based on a first cable-specific image parameter and a second cable-specific image parameter, and transmit them to the central control device (140) to control at least one of the tools (22; 61; 71; 81; 90) of at least one of the two cable processing stations (20; 60; 70; 80), and wherein the first cable-specific image parameter and the second cable-specific image parameter are selected from a group of parameters related to the inherent characteristics of the cable, consisting of material, structure, color, shape, cable insulation, cable shielding, sealing element, cable connection element, and cable type.

Images