Method and device for automatically selecting a tool

Abstract

The invention relates to a method and a control device for automatically selecting a tool for processing a workpiece blank to form at least one workpiece. The method comprises multiple method steps: In a first step, a geometric data set is detected for at least one workpiece to be produced. In a further step, the geometric data set is segmented into segment elements to be processed. In a further step, the respective tool is selected on the basis of an evaluation of the segment elements according to their processing suitability for punching and / or lasering based on at least one predefined processing criterion.

Description

[0001] TRUMPF Werkzeugmaschinen SE + Co. KG 2024P00224WG

[0002] Method and device for the automatic selection of a tool

[0003] DESCRIPTION

[0004] The present disclosure relates to a control device and / or a method for automatically selecting a tool for processing a workpiece blank into at least one finished product. Additionally or alternatively, a computer program is provided which includes commands that, when executed by a computer, cause the computer to execute the method, at least partially. Furthermore, a machine tool is provided.

[0005] STATE OF THE ART

[0006] In industrial manufacturing, selecting suitable tools for producing a wide variety of geometries is a demanding and time-consuming process. Companies face the challenge of deciding whether specific contours should be achieved through machining processes such as stamping or laser cutting. Stamping often offers advantages in terms of process reliability and efficiency, particularly for certain geometries, but frequently requires specific tools for each shape. This leads to high acquisition costs and ties up both capital and storage capacity.

[0007] While punching is advantageous in certain cases, laser cutting offers greater flexibility, as the laser head can cut any contour without requiring a special tool. However, laser cutting reaches its limits with demanding materials or precision requirements and can cause undesirable thermal effects or deformation. These varying advantages and disadvantages of the processes often make selecting the appropriate machining technique a complex decision. Assessing whether investing in specific tools for particular production orders is worthwhile, or whether alternative processes are more economical, is therefore often done through manual, time-consuming evaluations. These decisions are frequently based on experience and estimates, resulting in a resource-intensive process that can negatively impact production flexibility and efficiency.Automating this selection process, based on sound prior data and proven experience, is desirable in order to enable targeted and economical use of tools.

[0008] DE 102021 200310 A1 discloses a method and a device for the automated control of a machine tool.

[0009] In light of this prior art, the purpose of the present disclosure is to specify a method and a control device for the automatic selection of a tool, each of which is suitable to enrich the prior art.

[0010] The problem is solved by the features of the independent claims. The dependent and subordinate claims each contain optional further developments of the disclosure.

[0011] The problem is then solved by the method according to claim 1. The method according to the disclosure for automatically selecting a tool for processing a workpiece blank into at least one workpiece comprises several steps.

[0012] The method described in the disclosure allows for a more efficient machining process and the automatic selection of the most suitable tool for the specific characteristics of the workpiece. Automation reduces the time required for manual intervention and increases the precision of tool selection, resulting in faster and more economical production.

[0013] For the purposes of this disclosure, the method for automatically selecting a tool comprises the steps of data acquisition, segmentation and evaluation of workpiece characteristics in order to make an optimized tool selection for the respective machining requirements.

[0014] The first step involves capturing a geometric data set for at least one workpiece to be manufactured.

[0015] This feature has the technical effect of digitally capturing the exact geometric properties of the workpiece and making them available for further machining. Capturing the geometric data set lays the foundation for a precise and automated analysis of the workpiece characteristics, thereby optimizing the subsequent selection process for the appropriate tool.

[0016] For the purposes of this disclosure, a geometric data set can be understood as a collection of digital information describing the shape, size, and structure of the workpiece to be machined. This data set can include two-dimensional or three-dimensional geometries and contain digital image formats, such as SVG, DXF, GEO, LST, JSON, or similar file formats, which are used for planning and controlling the machining process.

[0017] In a further step, the geometric data set is segmented into segments to be processed. This has the technical effect of dividing the geometric data set into smaller, manageable elements, enabling more targeted and efficient machining. Segmentation allows specific characteristics of the workpiece to be individually evaluated and the tool selection to be precisely tailored to the requirements of each individual segment, thus increasing the quality and accuracy of the machining process.

[0018] For the purposes of this disclosure, the term "segment elements" can be understood as individual sections of the geometric data set that must be processed separately. These segments can include internal and external contours, line segments, curves, and / or geometric shapes, which are individually prepared for the respective processing method by punching and / or laser cutting. In a further step, the appropriate tool is selected based on an evaluation of the segment elements according to their suitability for punching and / or laser cutting based on at least one predefined processing criterion.

[0019] This has the technical effect of allowing the optimal machining process to be selected for each segment element, ensuring high machining quality and process reliability. The selection of the appropriate tool – whether for punching or laser cutting – is based on defined processing criteria, making the machining process efficient and gentle on the material. The specific evaluation of the segment elements allows the machining to be precisely tailored to the individual requirements of the workpiece, resulting in greater accuracy and improved product quality. Furthermore, the efficiency of tool selection is improved, and the optimal processing suitability for punching and / or laser cutting for specific workpiece characteristics can be determined automatically.This contributes to a reduction in manual intervention and an increase in machining accuracy by automatically selecting the most suitable tool, such as a punching tool and / or laser, based on the geometric properties and processing suitability of the workpiece. Through automation, the system can benefit from reduced machining time and improved efficiency.

[0020] For the purposes of this disclosure, a processing criterion may include the technical parameters and / or properties that assess the suitability of a tool for machining a workpiece. These may include, for example, the complexity of the desired machining shape, the thickness of the workpiece material, safety requirements of the machining process, and qualitative requirements for the optical appearance of the final product.

[0021] For the purposes of this disclosure, a tool may comprise a punching tool or laser cutting tool intended for processing workpieces by material removal, deformation, or contouring. The tool is specifically designed to shape or cut the workpiece in one or more processing steps. For use cases or application situations that may arise during the described process and are not explicitly described herein, the process may provide for the output of an error message and / or a prompt for user feedback, and / or the setting of a default value and / or a predetermined initial state.

[0022] The method may be a computer-implemented method, meaning that one, several, or all steps of the method may be at least partially executed by a computer or a data processing device, optionally a control device, in particular the control device disclosed. Where appropriate, it may be provided that the steps are executed in a different order than described.

[0023] Possible further developments of the procedure described above are explained in detail below.

[0024] It may be stipulated that the processing criterion includes the complexity of the processing. This ensures that the tool selection is specifically tailored to the geometry and physical properties of the workpiece, leading to improved machining quality and increased process reliability.

[0025] Furthermore, the material thickness of the workpiece blank can also be used as a processing criterion. Considering the complexity of the processing has the technical effect of allowing the process to adapt the appropriate machining method to the specific shape and level of detail of the segment element. This leads to more precise and reliable manufacturing.

[0026] In the context of this disclosure, processing complexity can be understood as a measure of the number and level of detail of the contours, corners, and shapes of a segment element. High complexity includes undercuts in the contours (indentations, like a puzzle piece), fine details, or tight radii, which place particular demands on accuracy during machining. Punching is a suitable method for reliably processing small, complex segment elements, especially geometries and / or sections of the workpiece, as it allows the contour to be machined with high quality in one (or a few) punching strokes. Laser cutting is typically best suited for simpler, longer contours of the segment elements. Laser cutting can also be advantageous for segment elements of workpieces with unusual shapes, as purchasing a suitable punching tool would not be economical.However, the type, thickness, and quality of the material must also be considered for the final evaluation. For example, punching is only possible with material thicknesses of a few millimeters.

[0027] Process reliability can also play a decisive role as a criterion for processing by punching and / or laser cutting.

[0028] Considering process reliability ensures reliable and error-free processing of the disclosed methods by minimizing potential risks of tool failure or process errors. This leads to increased operational reliability and reduces the probability of downtime or workpiece damage.

[0029] For the purposes of this disclosure, process safety can encompass the robustness and stability of the machining process. This includes, for example, the repeatability and reliability of the chosen method for carrying out the machining process without undesirable interruptions or quality defects.

[0030] Furthermore, optical requirements can also be considered as a processing criterion.

[0031] Considering optical requirements allows the process to be adapted to aesthetic and visual demands in order to achieve high-quality surfaces and cut edges. Selecting a tool, such as a punching tool and / or laser, that meets these requirements contributes to improved product quality, especially for workpieces with high visual appeal.

[0032] Punching tools can be used for material thicknesses up to 5 mm, and especially up to 7 mm for aluminum, provided the contour can be produced in just a few punching strokes. Punching typically produces uniform edges with minimal burrs, resulting in high visual quality. Lasers, on the other hand, can produce uniform edges with a higher degree of roughness, including a more pronounced burr. This can be subjectively perceived as lower quality compared to punching. Nevertheless, lasers can be preferred over punching tools because thicker sheets, especially from 5 mm (7 mm for aluminum), and / or long, straight segments can be cut without machining marks. Although the cut edge cannot visually match that of sheet metal up to 5 mm thick (7 mm for aluminum), laser cutting is significantly more common in practice. This can be factored into the optical requirements as a processing criterion.

[0033] Furthermore, the laser can be more flexible than a punching tool, as no separate punching tools are required for each segment element. In addition, the cutting edge quality in laser cutting can be influenced by the specific processing parameters of the laser unit, such as the distance between the laser nozzle and the sheet metal, the feed rate, and the cutting gas pressure. This can be incorporated into the optical requirements as a processing criterion.

[0034] For the purposes of this disclosure, optical requirements may include the specific visual specifications of the final product, such as the smoothness of the edges, the absence of burrs, and the desired surface quality, which are achieved by choosing the appropriate processing method.

[0035] Including these criteria makes it possible to optimally consider both technical and aesthetic requirements and to coordinate the entire processing procedure for the most error-free execution possible.

[0036] The segment elements can be designed to include internal and / or external contours. This allows for the comprehensive processing of diverse workpiece geometries, increasing the flexibility of the process. By considering both internal and external contours, the process enables detailed machining tailored to the specific requirements and shapes of the workpiece.

[0037] For the purposes of this disclosure, segment elements can comprise geometries or sections of the workpiece that are suitable for processing by punching, laser cutting, or similar processes. These segments can be defined as internal contours, for example, areas within the outer workpiece boundary, and / or external contours, outer boundaries of the workpiece.

[0038] It can be implemented that segment elements above a certain material thickness are laser-cut, while thinner materials are punched. This feature optimizes processing time and reduces tool wear for varying material thicknesses. By selecting the appropriate processing method based on material thickness, process reliability can be increased and tool life extended. Thinner materials, which are easier to punch, place less stress on the tools, while thicker materials can be efficiently processed by laser, which also reduces thermal effects.

[0039] For the purposes of this disclosure, the material thickness can denote the specific thickness of the workpiece with respect to its suitability for certain machining processes. A specific material thickness represents a threshold value that determines when laser cutting is preferable to punching. For example, a material thickness between 0 and 200 mm, and in particular between 0 and 80 mm, may be suitable for laser processing.

[0040] It may be possible to base the selection of the appropriate tool on previous manufacturing data in order to consider process-reliable characteristics of past manufacturing problems. Incorporating this data enables continuous improvement of process stability and reduces potential sources of error in future production runs. By utilizing manufacturing data from previous machining operations, problematic workpiece characteristics can be identified, and the most suitable tool for reliable machining can be selected. This contributes to reducing downtime and increasing production quality.

[0041] For the purposes of this disclosure, previous manufacturing data may include a collection of historical machining information containing details of previous manufacturing processes, errors encountered, and workpieces processed, in order to optimize future tool selection processes.

[0042] It can be implemented that the segment elements are classified according to their complexity using an algorithm, categorizing them into small and large, short and long, and simple and complex geometries. This has the technical effect of enabling efficient differentiation and selection of the most suitable tools for different segment elements and geometries, thus increasing overall productivity. By classifying the segment elements according to their size and shape, the most appropriate processing method (process) can be selected, for example, stamping for smaller and complex shapes or laser cutting for long, simple shapes, which can lead to more precise and resource-efficient manufacturing.

[0043] For the purposes of this disclosure, an algorithm is understood to be a rule or a computational method that makes it possible to classify geometric segments according to predefined criteria such as size, length, and complexity. This classification supports the selection of the tool and the machining method by taking into account the specific geometric requirements.

[0044] Different geometries require different processing strategies, such as punching and / or laser cutting.

[0045] Distinguishing between "small" and "large" geometries allows for targeted tool selection and adaptation of the processing method. Small geometries, which typically include fine details, may be better suited for stamping, while large geometries, which cover larger areas, can potentially be processed efficiently by laser, improving both precision and speed.

[0046] In the context of the present revelation, small geometries can be understood as those that include fine details and tight radii, while large geometries cover more extensive areas and may have less detailed features.

[0047] By classifying geometries into "short" and "long," the process can select the appropriate processing method based on the length of the contours to be processed. Long geometries, such as linear or continuous shapes, tend to be better suited for laser cutting, while short geometries, which often represent interrupted or isolated shapes, can be processed more efficiently by punching. Distinguishing between simple and complex geometries allows the processing method to be tailored to the required precision and level of detail. Simple geometries with few details can often be processed efficiently by punching and / or laser cutting. Complex geometries, which include, for example, tight radii or finely structured patterns, can achieve higher accuracy and better quality through laser cutting.

[0048] In the context of this disclosure, simple geometries can be understood as those that do not contain tight radii or numerous details. Complex geometries are characterized by detailed structures, fine forms, and potentially changing directions in the contour.

[0049] It may be necessary for the optical requirements of the workpiece to influence the choice of tool, so that laser processing, i.e., suitability for laser cutting, is used for geometries with high demands on cut quality. This contributes to high final quality, especially for workpieces with high aesthetic requirements. By specifically selecting the tool based on optical requirements, the cut quality is optimized, resulting in a high-quality appearance and precise contours. This is particularly advantageous for workpieces where smooth and fine cut edges are desired.

[0050] For the purposes of this disclosure, optical requirements can include specifications for the desired surface finish, smoothness of the cut edge, and visual aesthetics of a workpiece. These requirements guide the selection of the tool based on which process delivers the best possible optical quality.

[0051] It can be configured that the position and spacing of the segment elements on the workpiece surface are taken into account to minimize deformation or thermal effects. Activating this function results in more stable machining and a reduction in workpiece deformation. Considering the spatial arrangement of the segment elements allows for optimal heat distribution during machining, thereby reducing thermal stresses and material deformation. This contributes to maintaining the dimensional accuracy and structural integrity of the workpiece.

[0052] For the purposes of this disclosure, thermal influences can include effects caused by heat input during machining that may lead to deformation or impairment of the workpiece material. These influences can be controlled by appropriate tool selection, e.g., punching and / or laser cutting, and the arrangement of the machining segments.

[0053] The process may include a cost-benefit analysis that considers tool wear and maintenance costs to determine the optimal machining suitability for each geometry. This can make the production process more economical and easier to maintain. By evaluating the wear and maintenance costs for each tool, the process can maximize the long-term benefits and profitability of the tooling strategy. This leads to reduced operating costs and optimized tool utilization by prioritizing tools with lower total cost of ownership.

[0054] For the purposes of this disclosure, optimal machining suitability may include the selection of the tool that offers the most economical combination of efficiency, durability and cost per machining cycle for a specific workpiece geometry.

[0055] The cost-benefit analysis process can be designed to calculate a return on investment (ROI) for the acquisition of new stamping tools, based on the projected production volume. This has the technical effect of enabling well-informed investment decisions regarding tool procurement. By calculating the ROI based on the projected production volume, the process can assess whether the acquisition of a specific tool is economically viable. This leads to optimized tooling costs and helps avoid unnecessary expenditures by procuring only tools, such as stamping tools, that will have a positive economic impact at the expected production volume. This can mean that acquiring specialized stamping tools only makes sense if multiple uses are guaranteed.Otherwise, in individual cases, a laser can be used, since the laser can use universal nozzles as a tool.

[0056] For the purposes of this disclosure, the term Return on Investment (ROI) can be understood as an economic indicator that measures the ratio of benefits to costs for the acquisition and use of a new tool, based on its frequency of use and the expected production capacity utilization.

[0057] It can be implemented that material losses due to offcuts are considered as a processing criterion, and the appropriate tool is selected based on the optimization of material utilization. This function contributes to more efficient material use and reduces production costs. By minimizing offcuts, material consumption is optimized, which represents a significant economic advantage, especially with expensive or hard-to-procure materials. Considering offcuts as a criterion enables targeted tool selection aimed at maximizing material yield.

[0058] For the purposes of this disclosure, offcuts can be understood as the loss of material that occurs during the machining process and cannot be used to produce the desired workpiece.

[0059] Optimizing material utilization, as defined in the disclosure, refers to minimizing this loss through efficient arrangement of geometries, segment elements, and tool selection.

[0060] It may be possible for the evaluation of segment elements and the selection of the tool to additionally consider user-specific manufacturing data and usage preferences. In this way, the process can incorporate user-specific machining requirements and preferences, leading to greater satisfaction and a more precise tool selection. By accessing specific user manufacturing data and preferences, the process can offer a customized machining solution that optimizes the efficiency and accuracy of the production process for individual requirements. For the purposes of this disclosure, user-specific manufacturing data and usage preferences can include information that reflects the specific requirements and historical machining data of the respective user. This includes parameters such as preferred machining methods, frequently used geometries and material properties, as well as individual quality requirements.

[0061] The process may also include uploading geometric datasets to an online tool, integrated into a user system or a cloud-based environment, to evaluate the geometries and provide automated tool recommendations. This can simplify and accelerate tool selection for the user. By uploading the geometric data and linking it to an online tool, the user can automate the tool recommendation process, saving time and effort. Cloud integration enables location-independent access to the machining data and flexible adaptation to user requirements.

[0062] For the purposes of this disclosure, the online tool can be a web-based software application capable of analyzing geometry data and automatically providing recommendations for suitable tools based on algorithms. The tool can be integrated both locally within the user's system and in a cloud-based environment.

[0063] The online tool can be configured to provide users with a recommendation for stamping tools to be procured, based on the frequency and type of segment elements to be processed, and this recommendation can be directly linked to an ordering option. This function enables convenient and efficient procurement of new tools and promotes needs-based inventory management. Through automatic analysis and linking to an ordering option, the procurement process is accelerated and simplified, giving users direct access to the tools suitable for their production. This leads to optimized production preparation and improved material availability.

[0064] For the purposes of this disclosure, an ordering option may include a digital interface that enables the user to purchase recommended tools directly through the online tool, based on tool recommendations analyzed for his or her specific machining requirements.

[0065] The automated tool recommendation system can provide a list of preferred tools optimized for the fast and reliable production of uploaded geometries, which is then made available to the user as a shopping list via an online marketplace. This enables targeted, application-optimized tool procurement and simplifies order processing. By providing a specific shopping list, the user can quickly and precisely procure the tools required for machining. This improves planning and ensures that the tools meet production requirements, leading to increased efficiency and safety in production.

[0066] For the purposes of this disclosure, a shopping list may comprise a user-tailored, system-generated list of the required tools, which can be ordered via an online marketplace and is directly aligned with the user's processing requirements.

[0067] The online tool can be configured to display the ROI for recommended stamping tools based on projected production volume and tool utilization. This allows the process to support economically sound decisions in tool procurement and ensure long-term cost efficiency. The ROI provides the user with a direct assessment of the profitability of an investment, enabling tool procurement to be optimized based on expected production figures. This leads to better resource allocation and helps reduce overall manufacturing costs.

[0068] The online tool may additionally analyze the user's production history and the availability of their existing stamping tools in order to recommend tools for future orders. This contributes to planning reliability, optimizes tool utilization, and increases operational efficiency. By analyzing the production history, the process can monitor the availability and wear of existing tools and thus provide targeted recommendations for future orders. This enables proactive tool planning and reduces the risk of unexpected production bottlenecks. For the purposes of this disclosure, a production history may include a data record of previous production processes, containing details of the tools used, their frequency, and specific operating conditions, in order to analyze their availability and suitability for future orders.

[0069] Up to this point, the disclosure has been described in relation to the claimed method. Features, advantages, or alternative embodiments can be attributed to the other claimed objects (e.g., the control device or a machine tool) and vice versa. In other words, the subject matter claimed or described in relation to the control device can be improved by features described or claimed in the context of the method or the control device, and vice versa.

[0070] According to another aspect of the disclosure, a control device is provided for the automatic selection of a tool for processing a workpiece blank into at least one workpiece. A receiving unit is provided for capturing a geometric data set for at least one workpiece to be manufactured. Furthermore, a processing unit is provided. The processing unit is configured to segment the geometric data set into segment elements to be processed. In addition, the processing unit is configured to select the respective tool based on an evaluation of the segment elements according to their suitability for punching and / or laser cutting, based on at least one predefined processing criterion. This has the technical effect that the tool selection is controlled directly in the machine tool and efficiently adapted to the machining requirements of the workpiece.The integration of receiver and processor units into the control device enables precise and automated machining, as the relevant data is captured, processed and used in real time for tool selection.

[0071] For the purposes of this disclosure, a control device can be an electronic control unit (ECU). The electronic control unit can be an intelligent, processor-controlled unit that, for example, has various interfaces configured with different communication standards for communication with the machine tool and / or machine tool components, and may include Profibus, fieldbuses such as CAN bus, LIN bus, MOST bus, or FlexRay.

[0072] For the purposes of this disclosure, the control device can comprise a combination of hardware and software that automates the operation and tool selection within a machine tool. The receiving unit can be a sensor or communication module for data acquisition, while the processor unit handles the data processing and control of the tool selection.

[0073] According to another aspect of the revelation, a machine tool is created. This machine tool includes the control device described in the revelation for the automatic selection of a tool. The machine tool is designed to optimize machining processes and reduce setup times for tool changes. This enables more efficient use of the machine tool, as the automatic control allows for faster and more precise selection of the correct tool. The reduction in setup times contributes to increased productivity and minimizes downtime by allowing the machine to remain in continuous operation and requiring less manual intervention.

[0074] For the purposes of this disclosure, a machine tool can comprise a mechanical device used for machining workpieces with tools, such as punches and lasers. The machine tool is equipped with a control device that fully automates the tool selection process and optimizes machining efficiency.

[0075] The disclosure further provides a computer program with program code for executing the disclosed method when the computer program is executed on an electronic device. The computer program can be provided as a signal via download or stored in a storage unit of a portable device containing computer-readable program code to cause a method and a control device to execute instructions according to the above-mentioned method. As another solution, the disclosure also includes a computer-readable storage medium comprising program code which, when executed by a computer or a computer network, causes it to execute an embodiment of the disclosed method. The storage medium can be at least partially a non-volatile data storage medium (e.g.,The storage medium can be provided as flash memory and / or as an SSD (solid-state drive) and / or at least partially as volatile data storage (e.g., as RAM - random access memory). The storage medium can be located within the computer or computer network. However, the storage medium can also be operated, for example, as an app store server and / or cloud server on the internet. The computer or computer network can provide a processor circuit with, for example, at least one microprocessor. The program code can be provided as binary code and / or assembly code and / or source code in a programming language (e.g., C) and / or as a program script (e.g., Python). Alternatively, the computer-readable storage medium can be implemented as a signal containing computer-readable data, e.g., a time-varying voltage signal and / or a radio signal.

[0076] The above embodiments and further developments can be combined with one another as appropriate. Further possible embodiments, further developments, and implementations of the disclosure also include combinations of features of the disclosure described previously or subsequently with regard to the exemplary embodiments, even if not explicitly mentioned. In particular, the person skilled in the art will also add individual aspects as improvements or additions to the respective basic form of the present disclosure.

[0077] The above can be summarized in other words and in a possible more concrete elaboration of the revelation as described below, whereby the following description is to be interpreted as not being restrictive for the revelation.

[0078] All geometries to be manufactured are broken down into their segment elements and then grouped according to common segment elements. Each segment element is checked for its suitability for stamping. For example, is stamping worthwhile, or is laser cutting faster and / or cheaper with the same process reliability? Subsequently, the number of stamping-suitable segment elements is multiplied by the production quantity of their associated geometries to estimate the number of expected stamping strokes.

[0079] This assessment allows for a comparison of the costs and benefits of potential stamping tools and enables recommendations to be made to the user regarding the purchase of stamping tools.

[0080] The challenge lies in meaningfully dividing contours into individual, comparable processing segments and subsequently evaluating the suitability of these segments for punching. This evaluation, for example using processing criteria, can be based on material, thickness, optical requirements, and process reliability—for instance, how high is the risk of machine downtime if laser cutting is used instead?

[0081] To assess the suitability for stamping, previous production data, such as historical data, are analyzed and expert knowledge is compiled. In the future, user-specific evaluation models can be trained, allowing a user's history and experience to be taken into account.

[0082] The present disclosure will be explained in more detail below with reference to the exemplary embodiments shown in the schematic figures of the drawings. These show:

[0083] Fig. 1 is a block diagram illustrating an embodiment of a machine tool with the control device as disclosed;

[0084] Fig. 2 shows a flowchart illustrating an embodiment of a method according to the disclosure, and

[0085] Fig. 3 shows a schematic representation of several produced workpieces using an embodiment of the method as disclosed.

[0086] The accompanying drawings are intended to provide a further understanding of the embodiments of the disclosure. They illustrate embodiments and, in conjunction with the description, serve to explain the principles and concepts of the disclosure. Other embodiments and many of the mentioned advantages become apparent with reference to the drawings. The elements of the drawings are not necessarily shown to scale.

[0087] In the figures of the drawing, identical, functionally equivalent, and equally effective elements, features, and components—unless otherwise specified—are to be provided with the same reference symbols.

[0088] Figure 1 shows a control device 200 designed for the automatic selection of a tool for processing a workpiece blank 20 into a finished workpiece 30. The control device 200 comprises a receiving unit 210 designed for acquiring a geometric data set of the workpiece 30 to be manufactured. The control device 200 also includes a processor unit 220 that analyzes and segments the acquired data set to identify segment elements 21 that are specifically suitable for processing.

[0089] The selection of the tool, for example laser cutting tool 11 and / or punching tool 12, is carried out by the processor unit 220 on the basis of an evaluation of the segment elements 21 according to certain processing criteria, including the complexity of the machining, the material thickness of the workpiece blank 20, the process reliability and optical requirements for the finished workpiece 30.

[0090] In addition, segment elements 21 are classified according to their geometry and categorized as internal or external contours to determine whether they are laser-cut or punched. For example, thinner materials can be punched and thicker materials laser-cut. For segment elements with particularly high requirements for cut quality, the laser cutting tool 11 is preferably used to ensure a clean cut edge. Furthermore, the processor unit considers historical production performance and sources of error to incorporate process-reliability-relevant characteristics of previous manufacturing problems and to optimize tool selection.

[0091] The control device 200 is capable of considering a wide range of criteria, including thermal effects that can occur during laser processing, in order to minimize material distortion. If necessary, the tool can be selected based on the spacing and positions of the segment elements 21 on the workpiece surface to avoid deformation or undesirable thermal effects. The control device 200 can also perform a cost-benefit analysis that considers tool wear and maintenance costs to determine the optimal processing suitability for each geometry.

[0092] Figure 1 also shows that the machine tool 300 contains, in addition to machine components 13, both the laser cutting tool 11 and the punching tool 12. The control device 200 can incorporate previous manufacturing data into the decision-making process and, using the algorithm, classify the segment elements 21 according to their complexity (e.g., small or large, short or long, simple or complex geometries). Small and complex geometries are preferentially punched, while long and simple geometries are more likely to be laser cut.

[0093] Furthermore, the Control Device 200 offers the possibility of taking user-specific manufacturing data and usage preferences into account to ensure optimal adaptation to the individual requirements of the user. Uploading the geometric data sets to an online tool, which is integrated into a user system or a cloud-based environment, enables the user to receive automated tool recommendations for manufacturing.

[0094] This control device 200 enables a targeted and optimized selection between punching and laser cutting, thereby improving both the efficiency and quality of workpiece processing. The control device 200 is designed to carry out the method 100 described in detail below, also with reference to Figure 2.

[0095] Figure 2 shows the method 100 for automatically selecting a tool for machining a workpiece blank 20 into a finished workpiece 30. The method begins with the acquisition 110 of a geometric data set containing the essential geometric features of the workpiece 30 to be manufactured. This detailed acquisition enables a precise analysis of the workpiece's shapes and structures, thus laying the foundation for consistent quality and increased process reliability. After acquisition, the geometric data set is divided into segment elements 21 in the segmentation step 120, representing the areas of the workpiece to be machined. This segmentation allows for flexible adaptation to complex contours and a differentiated evaluation of the internal and external contours 32, 33 of the workpiece 31, resulting in time-efficient and resource-saving machining.

[0096] In the subsequent selection step 130, the appropriate tool is chosen for each individual segment element. An evaluation is performed for each segment to determine whether the laser cutting tool 11 or the punching tool 12 should be used. The selection is based on various processing criteria such as the complexity of the geometry, the material thickness, process reliability, and the optical requirements for cut quality. This allows simple, long geometries to be laser-cut efficiently and small, complex geometries to be punched reliably. At the same time, material waste is minimized through targeted tool selection, thus optimizing material utilization. Furthermore, the inclusion of historical production data and user-specific preferences enables adaptation to the individual requirements of the user, thereby increasing cost-efficiency and satisfaction.

[0097] The process 100 forms the basis for automated and high-quality manufacturing, where the optimal choice of tools leads to increased efficiency and process reliability.

[0098] Figure 3 shows an embodiment of a workpiece blank 20 from which several workpieces 30, 31, 34, 35 are produced, wherein the workpieces 30, 31, 34, 35 have different geometries and segment elements for internal contours 32 and external contours 33, which were generated using method 100. The workpiece 34 consists of several areas with different contours and shapes, which were segmented for machining and selected according to the respective processing criteria.

[0099] Segment element 21 is part of a complex outer contour 33, which requires flexible adaptation of the processing methods. Laser cutting, for example, can be used here. The long, straight, and relatively large segment element 26 of the outer contour 33 is also suitable for laser cutting. Specific geometries, such as the hexagonal segment 22, the cross-shaped segment 23, and the rectangular segment 24, were analyzed and identified in the geometry and subsequent workpiece 34. These different geometries illustrate how process 100 uses segmentation to determine the appropriate processing method for each geometry and to capture complex contours. Such complex contours can preferably be punched to achieve high process reliability and material utilization.

[0100] The cross-shaped segment 23 can be punched. This is particularly useful in this example because the cross-shaped segment 23 can also be punched multiple times in workpiece 35. The square segment 24 within the geometry of workpiece 34 can be laser-cut. To ensure a clean separation of segment 24 from the remaining material, process 100 is applied such that the waste is cut and any undercuts are removed using the laser, indicated here by dashed lines. The combined use of punching and laser processing allows for precise cut edges and minimizes unnecessary material loss. This approach enables the targeted separation of complex internal areas, with punching used for the main contour and laser cutting for precise post-processing of undercuts to ensure high cut quality and process reliability.

[0101] The internal contours 32 comprise various shapes, including the circular segment elements 25 and the rectangular segment element 27, which have been specifically optimized for small, precise machining. These segment elements can be efficiently manufactured by stamping, enabling higher accuracy for small and regular shapes and increasing the efficiency of the machining process. The workpiece 35 has partially straight segments 28 and corners 23 identified as crosses on the outer contour 33, with the elongated outer contour 28 being ideally suited for laser machining to achieve a clean cut edge. The cross-shaped segments 23 can be produced with the same stamping tool as for the cross-shaped segments in workpiece 34. Due to the multiple uses, manufacturing a stamping tool for the star-shaped segments 23 is particularly advantageous.

[0102] The workpiece 30 has a complex outer contour 33. In this example, the outer contour 33 is produced using a hexagonal punching tool. The same punching tool is used to produce the hexagonal segments 22 in workpiece 34. The punching tool is used in an overlapping manner to produce the complex outer contour 33 of the workpiece 30. Since only a single punching tool is used to produce the complex outer contour 33, punching this outer contour 33 is particularly advantageous. Alternatively, the outer contour 33 can be produced by laser processing. Finally, the workpiece 30 shows a pentagon 31 with an outer contour 33 and a circular inner contour 32, which is intended for combined processing by punching and laser cutting to achieve a balanced mix of precision and cut quality. In this process, the outer contour 33 is laser-cut, and the inner contour 32 is, for example, punched.

[0103] By applying process 100, workpiece 30 can be efficiently divided into individual segments and machined selectively. This leads to optimal material utilization, as waste is minimized and the appropriate tools are precisely selected. The combination of laser and punching processes offers technical advantages in terms of process reliability, material savings, and machining quality, resulting in economically optimized production.

[0104] Overall, the examples demonstrate how geometry-based die-cutting tool recommendations can be provided. REFERENCE SYMBOL LIST

[0105] 11 Laser cutting tool

[0106] 12 Punching tools

[0107] 13 machine components

[0108] 20 workpiece blanks

[0109] 21-28 Segment elements

[0110] 30, 31, 34, 35 workpiece

[0111] 32 Inner contour

[0112] 33 Outer contour

[0113] 200 control device

[0114] 210 Receiving unit for capturing a geometric data set 220 Processor unit

[0115] 100 procedures

[0116] 110 - 140 process steps

[0117] 300 machine tool

Claims

25 PATENT CLAIMS 1. Method (100) for automatically selecting a tool (11, 12) for processing a workpiece blank (20) into at least one workpiece (30), comprising the steps: - Acquisition (110) of a geometric data set for at least one workpiece to be manufactured (30); - Segmenting (120) the geometric data set into segment elements (21) to be processed, and - Selections (130) of the respective tool (11, 12) based on an evaluation of the segment elements (21) according to their suitability for punching and / or laser cutting based on at least one predefined processing criterion.

2. A method according to the immediately preceding claim, wherein the processing criterion comprises at least: - Complexity of processing, - Material thickness of the workpiece blank, - Process reliability, and - optical requirements.

3. Method according to any of the preceding claims, wherein the segment elements (21) comprise inner contours and / or outer contours.

4. Method according to one of the preceding claims, wherein segment elements are laser-cut from a certain material thickness and punched for thinner materials.

5. Method according to one of the preceding claims, wherein the selection of the respective tool is further based on previous manufacturing data in order to take into account process safety-relevant features of previous manufacturing problems.

6. Method according to one of the preceding claims, wherein the segment elements (21) are classified according to their complexity into small and large and / or short and long and / or simple and complex geometries by means of a processing application, wherein small and complex geometries are preferably punched and long, simple geometries are laser cut.

7. Method according to one of the preceding claims, wherein optical requirements for the workpiece influence the choice of tool, such that laser processing is used for geometries with high requirements for cutting quality.

8. Method according to any of the preceding claims, wherein the position and spacing of the segment elements on the workpiece surface are taken into account to minimize deformations or thermal influences.

9. Method according to any of the preceding claims, wherein the method (100) additionally comprises a cost-benefit analysis which takes into account the wear and maintenance costs of the tools in order to determine the optimal processing suitability for each geometry.

10. Method according to one of the preceding claims, wherein material losses due to cutting are taken into account as a processing criterion, and the respective tool is selected according to the optimization of material utilization.

11. Method according to one of the preceding claims, wherein the evaluation of the segment elements (21) and the selection of the tool additionally take into account user-specific manufacturing data and usage preferences.

12. Method according to one of the preceding claims, wherein the method (100) further comprises uploading (140) the geometric data sets to an online tool which is integrated into a user system and / or a cloud-based environment to perform an evaluation of the geometries and an automated tool recommendation.

13. Control device (200) for automatically selecting a tool (11, 12) for processing a workpiece blank (20) into at least one workpiece (30), comprising: a receiving unit (210) configured to capture a geometric data set for at least one workpiece (30) to be manufactured, and a processor unit (220) trained to: - Segmenting (120) the geometric data set into segment elements (21) to be processed, and - Selections (130) of the respective tool (11, 12) based on an evaluation of the segment elements (21) according to their suitability for punching and / or laser cutting based on at least one predefined processing criterion.

14. Machine tool (300) with a control device (200) according to the immediately preceding claim.

15. Computer program, wherein the computer program comprises instructions which, when the program is executed by a computer, cause it to execute the method according to one of the preceding method claims.

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