Method for providing a process instruction for additive manufacturing, by way of machine learning

Abstract

The invention relates to a method for providing a process instruction for the additive manufacture of a component, including the method steps: reading in geometrical data of the component; producing a layer structure of a building structure, the building structure comprising the component; and generating a process instruction for the additive manufacturing of the building structure, a machine learning algorithm being used to generate the process instruction.

Description

[0001] Method for providing a process instruction for additive manufacturing using machine learning

[0002] The invention relates to a method for providing a process instruction for the additive manufacturing of a component with the process steps of reading in geometric data of the component, creating a layer structure of a building structure, wherein the building structure comprises the component, and creating a process instruction for the additive manufacturing of the building structure, wherein an ML algorithm is used to create the process instruction.

[0003] State of the art

[0004] 3D printing or additive manufacturing is a comprehensive term for all manufacturing processes in which material is applied layer by layer to create three-dimensional components. The layer-by-layer build-up is computer-controlled from one or more liquid or solid materials according to specifications from a CAD / CAM system. The layers can then be broken down into strips, particularly in direct energy deposition processes. In addition, in a process known as hatching, a layer is divided into strips (hatches) or squares and parallel vectors are distributed within them. With powder bed-based technologies, such as selective laser melting, the component is manufactured without further subdivision of the layers. When the workpiece is built up layer by layer, a print head or a laser is usually moved horizontally, i.e. in the XY plane, and at the same time, strips of material are applied using the print head or laser.Once a layer is completed, the build plate on which the workpiece is being manufactured is usually moved vertically downwards, i.e. in the Z direction, and another layer is started. During the application or melting of the layers, the workpiece may experience problems such as cracking, deformation and an uneven crystal structure, depending on the type of material applied and the set process parameters (e.g. temperature, feed rate). In other cases, this can even lead to the entire manufacturing process having to be stopped. In the prior art, a process plan is usually created based only on the geometric design of a workpiece. If such a process plan is executed, it can lead to a phenomenon that a specific section of the workpiece becomes overheated during a manufacturing process, for example, and it becomes difficult to control the temperature of the workpiece effectively.In addition, in the state of the art in an additive manufacturing process, it is impossible to change and adapt the process plan, especially during the manufacturing process, once a process plan has been completely created.

[0005] It is therefore an object of the invention to provide a method for providing a process instruction for the additive manufacturing of a component, with which an improved process plan for the additive manufacturing of a workpiece is provided.

[0006] The object is achieved by means of the method according to the invention for providing a method instruction for the additive manufacturing of a component according to claim 1. Advantageous embodiments of the invention are set out in the following subclaims.

[0007] The method according to the invention for providing a process instruction for the additive manufacturing of a component comprises three process steps: In the first process step, geometric data of the component is read in.

[0008] The second step involves creating a layered structure of a build structure, which includes the component. A manufacturing data set is generated from the data set created using the CAD program. This data set contains a preparation of the workpiece's geometry into layers or slices suitable for additive manufacturing. This data transformation is called slicing.

[0009] The third process step involves creating a process instruction for the additive manufacturing of the build structure, using an ML algorithm to create the process instruction. A process instruction refers to data provided to an additive manufacturer for the additive production of the build structure. This includes the process parameters for the additive manufacturer and the definition of a tool path, such as one or more parameters from the group of energy beam power, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, traversing speed of the energy beam, changing the hatch distance between the vectors, the vector sequence, the vector length, vector orientation, and / or changing the geometry of the support structure.The tool path typically consists of a series of vectors that the additive manufacturer follows. The process instructions thus define a process control that the additive manufacturer executes to produce a build structure.

[0010] For manufacturing reasons, the aforementioned 3D printing processes require the use of a support structure to support delicate or overhanging structures during the printing process. These structures would otherwise collapse under the force of gravity. Support structures are also sometimes necessary in metal 3D printing, but for different reasons than in plastic-based additive processes. Here, the risk is less that the model could collapse during printing, but rather to prevent impending warping. Thin areas of the model, in particular, can easily bend. However, the internal stresses in additive manufacturing are so high that even massive components can warp significantly.

[0011] By means of the process instruction, a structure can be produced which does not overheat in particularly vulnerable areas during the manufacturing process and which has a lower residual stress distribution in the cooled and post-processed state, which is relevant, for example, in the manufacture of turbine blades.

[0012] Additive manufacturing processes within the meaning of this application are processes in which the material from which a build structure is to be produced is added to the build structure during its creation. The build structure is created in its final form, or at least approximately in this form, and then undergoes post-processing. In particular, the build structure to be manufactured has a support structure comprising one or more support points. This support structure is removed during post-processing.

[0013] The techniques used by an additive manufacturer to produce a part include, for example, extrusion deposition or selective deposition modeling (SDM), techniques such as fused deposition modeling (FDM) and fused filament fabrication (FFF), stereolithography (SLA), polyjet printing (PJP), multijet printing (MJP), selective laser sintering (SLS), selective laser melting (SLM), three-dimensional printing (3DP), techniques such as inkjet printing (CJP), directed energy deposition (DED) and the like.

[0014] Fused filament fabrication (FFF), also known as fused deposition modeling or filament freeform fabrication, is a 3D printing process that uses a continuous filament made of a thermoplastic material. The filament is fed from a large spool through the printer's moving, heated extruder head and deposited onto the growing workpiece. The print head is moved under computer control to define the printed shape. Typically, the head moves in two dimensions to deposit one horizontal plane or layer at a time; the workpiece or print head is then moved vertically a small amount to begin a new layer. The speed of the extruder head can also be controlled to stop and start deposition, creating a discontinuous plane without threads or drips between sections.

[0015] Directed Energy Deposition (DED) refers to a category of additive manufacturing or 3D printing processes in which powder or wire is fed coaxially to an energy source (usually a laser) to form a molten or sintered layer on a substrate.

[0016] Melt filament printing is currently the most popular 3D printing process, especially among hobbyists. Other processes such as photopolymerization and powder sintering can produce better results, but are significantly more expensive. The 3D printer head, or 3D printer extruder, is a part in additive manufacturing through material extrusion that is responsible for melting or softening the raw material and forming it into a continuous profile. A wide variety of filament materials are extruded, including thermoplastics such as acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene terephthalate glycol (PETG), polyethylene terephthalate (PET), high-impact polystyrene (HIPS), thermoplastic polyurethane (TPU), and aliphatic polyamides (nylon).

[0017] The method according to the invention for providing a method instruction for the additive manufacturing of a building structure is computer-aided, whereby the term "computer-aided" is used, for example, in this document in such a way that one or more computers execute or execute at least one method step of the method. Computers can be, for example, personal computers, servers, handheld computer systems, pocket PC devices, mobile radio devices, and other communication devices that can process data in a computer-aided manner, as well as processors and other electronic devices for data processing, which can also be connected to form a network.

[0018] The starting point for additive manufacturing is a geometric description of the workpiece using a dataset. Using 3D modeling software (e.g., a CAD program), the dataset for the structural design of the component to be manufactured is created. This dataset contains the three-dimensional geometric data for production using the additive manufacturing process.

[0019] The possible ML and / or AI algorithms used are described in the following paragraphs.

[0020] Random forest regression is a machine learning method of ensemble learning. An ensemble of multiple decision trees is combined and used for regression. This is a supervised learning technique. Gradient boosted trees is another ensemble learning technique that can be used for regression and classification. It is classified as supervised learning.

[0021] Deep learning (also known as multi-layer learning or in-depth learning) is a method of machine learning. Most deep learning algorithms are deep neural networks (DNNs). They consist of many layers of linear and nonlinear processing units, the artificial neurons. The more neurons and layers a neural network contains, the more complex the data it can represent.

[0022] Another type of deep learning algorithm is decision trees (Random Decision Forests, or RDFs). They also consist of many layers, but instead of neural structures, RDFs are constructed from decision trees and output a statistical average (mode or mean) of the predictions of the individual trees.

[0023] Deep learning is used wherever large amounts of data need to be analyzed for patterns and trends. Within AI, this happens, for example, in the following areas: facial, object, and speech recognition.

[0024] A convolutional neural network (CNN or ConvNet) is an artificial neural network. It is a concept in the field of machine learning inspired by biological processes. Convolutional neural networks are used in numerous artificial intelligence technologies, primarily in the machine processing of image or audio data.

[0025] Recurrent or feedback neural networks are neural networks that, in contrast to feedforward networks, are characterized by connections between neurons in one layer and neurons in the same or a previous layer. In the brain, this is the preferred wiring style for neural networks, particularly in the neocortex. In artificial neural networks, the recurrent wiring of model neurons is used to discover temporally encoded information in the data. Examples of such recurrent neural networks are the Elman network, the Jordan network, the Hopfield network, and the fully connected neural network. A convolutional neural network (CNN or ConvNet) is an artificial neural network. It is a concept in the field of machine learning inspired by biological processes.Convolutional neural networks are used in numerous artificial intelligence technologies, primarily in the machine processing of image or audio data.

[0026] Recurrent or feedback neural networks are neural networks that, in contrast to feedforward networks, are characterized by connections between neurons in one layer and neurons in the same or a previous layer. In the brain, this is the preferred wiring configuration for neural networks, particularly in the neocortex. In artificial neural networks, the recurrent wiring of model neurons is used to discover temporally encoded information in the data. Examples of such recurrent neural networks are the Elman network, the Jordan network, the Hopfield network, and the fully connected neural network.

[0027] In a further development of the invention, a process instruction comprises a geometric start (x, y, z) and a geometric end point (x, y, z) for each individual vector (exposure vector). In an optional development according to the invention, the laser power and / or the laser speed are included in a process instruction. In a further embodiment of the invention, a process instruction has one or more elements of the following group of parameters: type of vector (fill, contour vector; overhang vector, surface vector), polygon that describes the outer boundaries of the part, start / end time of each vector, pause times between the vectors (forced (e.g.) for cooling or due to optical / mechanical conditions (so it cannot be done any faster)), pause times between layers, coater times and information, build plate temperature, assignment of which vector is written by which laser, areas that the individual lasers can reach, continuous or pulsed vector, focus of the laser and which laser mode (per vector), rarely: circular movements of the laser (wobble) and / or information about the gas used and its direction of flight in the build chamber.

[0028] In a further embodiment of the invention, the ML algorithm is applied to an initial procedural instruction. In a further embodiment of the invention, the initial procedural instruction is created without an ML algorithm.

[0029] The method according to the invention creates an initial process instruction for the additive manufacturing of a first build structure and a process instruction for the additive manufacturing of a second build structure, wherein the initial process instruction is created without an ML algorithm and the process instruction is created using an ML algorithm. The two build structures preferably have the same component but different support structures. Thus, one and the same component can be manufactured using the two process instructions, wherein the process instruction is created on the basis of the initial process instruction. The initial process instruction is optimized using an ML algorithm such that the component manufactured using the process instruction has an improved residual stress distribution and / or an improved temperature distribution, for example to avoid local and / or global overheating.

[0030] In a further embodiment of the invention, ML data is read from a database for use with the ML algorithm. ML data is data used by an ML algorithm to generate a process instruction. This data is stored in a database, which, in a further development of the invention, is separate.

[0031] A computer unit within the meaning of the invention encompasses all electronic devices with data processing capabilities. A computer unit is thus, for example, a personal computer, server, handheld computer system, pocket PC device, mobile radio device, and other communications device capable of computer-aided data processing, as well as processors and other electronic devices for data processing, which may also be connected to a network. A computer unit also has a storage unit or is connected to a storage unit. The storage unit is optionally embodied as a database and is also optionally arranged separately from the computer unit.

[0032] In a further embodiment of the invention, the ML data contains data from different manufacturing processes for additive manufacturing. The ML data is created for different CAM processes, wherein CAM processes include laser and / or electron beam powder bed fusion, direct energy deposition (DED), binder jetting, fused filament fabrication (FFF), melt filament printing, and / or other non-abrasive computer-aided manufacturing processes that rely on a tool path with associated process parameters. Different additive manufacturers use different CAM processes to manufacture a component. The data includes the possible process parameters of the additive manufacturer, the possible travel speeds, and the possible travel paths of the component to the additive manufacturer. The data is different for different additive manufacturers and is therefore used to create the process instruction.At the same time, it is possible to create process instructions for different additive manufacturers; the process according to the invention can therefore be applied to different additive manufacturers.

[0033] In an advantageous embodiment of the invention, experimental data and / or simulation data are used for the application of the ML algorithm. In a further development of the invention, the experimental data include the local temperature, power of the energy beam, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, and / or the travel speed of the energy beam. Experimental data include experimentally determined data. The experimental data include data of a building structure that was acquired in-situ in real time and / or in previous manufacturing processes. With the experimental data, the creation of the process instruction can include data that is acquired based on real, non-simulated manufacturing processes. In a further aspect of the invention, calculated data is determined from the experimental data.In a further embodiment of the invention, experimental data are recorded in-situ in an advantageous embodiment of the invention during the implementation of a first part of the process instruction for manufacturing a first section of the component. In a preferred variant of the invention, a first section of the component can be additively manufactured using the process instruction. The process instruction is therefore an initial process instruction, in other words a first part of the process instruction with which a first section of the component can be additively manufactured. The entire component can be manufactured using the process instruction. The first section of the component is, for example, a layer; the first part of the process instruction accordingly comprises a process instruction for the additive manufacturing of the first layer of the component. The first section can also comprise a layer sequence consisting of several layers or even parts of layers.

[0034] To acquire the experimental data, preferably during the production of the component in real time, different methods and detection devices can be used, e.g. the detection device is a temperature detection device configured to measure an irradiation point temperature of the component, an imaging device configured to measure a light emission quantity in order to detect a generated spray or atomization quantity, an imaging device configured to acquire a mold surface image of the component or an imaging device configured to detect a melt pool size.

[0035] In a further advantageous embodiment of the invention, a second part of the process instruction for manufacturing the component is created from the in-situ acquired experimental data during the execution of the process instruction. The acquired experimental data is sent to the second storage device and also stored in the second storage device. This experimental data is read in, and calculated data is determined from the experimental data. The ML algorithm is applied to this calculated data, and a second part of the process instruction is created during the execution of the initial process instruction.

[0036] In a further embodiment of the invention, the second part of the process instruction is created for the production of a second section of the component. In a further embodiment of the invention, the second part of the process instruction has modified parameters for the production of the second section of the component compared to the initial process instruction. In a further embodiment of the invention, the modified parameters comprise one or more parameters from the group of energy beam power, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, traversing speed of the energy beam, change in the hatch distance between the vectors, the vector sequence, the vector length, vector orientation, and / or modified geometry of the support structure.

[0037] This process is repeated until all sections of the component have been manufactured. After the second process instruction for additive manufacturing a second layer of the component is generated, a third process instruction for additive manufacturing a third layer of the component is generated using the ML algorithm, and so on, with each nth process instruction being created based on the (n-1)th process instruction using the ML algorithm.

[0038] In a further embodiment of the invention, the initial process instruction was created on a first computer unit and the process instruction is created on a second computer unit, wherein the first computer unit is different from the second computer unit. The two different computer units preferably also differ in their location and particularly preferably in the access rights that a user has to the computer units. Preferably, a user with access rights to the second computer unit can transfer process instructions to these, e.g. those created by the user themselves, which are used by the second computer unit to create a process instruction. The method according to the invention therefore enables users to access a computer unit to create a process instruction that is optimized with regard to its residual stress distribution.

[0039] In one development of the invention, the initial process instruction was created using a first software program, and the process instruction is created using a second software program, wherein the first software program is different from the second software program. The formalized sequences of the two different methods are implemented and processed using different software. In a further development of the invention, the method executed by the first software is different from the method executed by the second software. The second software uses an ML and / or AI algorithm to create the second process instruction that is different from the first software used to create the first process instruction. The first software optionally does not use an ML and / or AI algorithm.

[0040] In one development of the invention, the first structural design is different from the second structural design. In a further embodiment of the invention, the first structural design comprises the component and a first support structure, while the second structural design comprises the component and a second support structure, the first support structure being different from the second support structure. The two structural designs preferably have the same component but different support structures. Thus, one and the same component can be manufactured using the first and second process instructions, with the second process instruction being created based on the first process instruction.

[0041] In a further embodiment of the invention, the initial process instruction is created using a first method, and the process instruction is created using a second method, wherein the first method is different from the second method. A method within the meaning of the invention is a systematic and targeted approach for creating a process instruction for the additive manufacturing of a building structure using formalized processes. The formalized processes are defined, for example, in a computer program.

[0042] In a further embodiment of the invention, the first method for creating the initial procedural instruction comprises the use of a first software and the second method for creating the procedural instruction comprises the use of a second software, wherein the first software is different from the second software.

[0043] In one development of the invention, the initial process instruction is created using a first software program, and the process instruction is created using a second software program, wherein the first software program is different from the second software program. The formalized sequences of the two different methods are implemented and processed using different software. In a further development of the invention, the method executed by the first software is different from the method executed by the second software. The second software uses an ML and / or AI algorithm to create the process instruction that is different from the first software used to create the initial process instruction. The first software optionally does not use an ML and / or AI algorithm.

[0044] In a further embodiment of the invention, the initial process instruction is created using a first data set. The first data set includes machine data of the additive manufacturer for which the initial process instruction is to be created, as well as component data, simulation data of the temperature distribution in the build structure during the manufacturing process, and / or experimental data.

[0045] In a further aspect of the invention, the process instruction is created using a second data set. In a further embodiment of the invention, the first data set is different from the second data set. Preferably, the second data set comprises empirical data. The empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or building structures, as well as the process instructions specific to each component. The empirical data includes machine data of the additive manufacturer for which the process instruction is to be created, as well as component data, simulation data of the temperature distribution in the building structure during the manufacturing process, and / or experimental data.

[0046] In a further embodiment of the invention, the second data set comprises experimental data, component data, empirical data, and / or machine data. In a further development of the invention, the second data set comprises experimental data, component data, empirical data, and / or machine data from different additive manufacturing processes. In a further aspect of the invention, the second data set comprises experimental data, component data, empirical data, and / or machine data from different additive manufacturers.

[0047] The machine data includes the possible process parameters of the additive manufacturer, the possible travel speeds, and the possible travel paths of the component to the additive manufacturer. Machine data differs for different additive manufacturers and is therefore used to create the process instructions. At the same time, it is possible to create process instructions for different additive manufacturers; the method according to the invention can therefore be applied to different additive manufacturers.

[0048] The component data includes the geometry of the build structure, the geometry of the component, and / or material data, where the material data includes the phases, phase concentration, microstructure, mechanical properties, melting temperature, and / or boiling temperature. A build structure to be manufactured often contains thin-walled or overhanging structures. In these areas, the body provides a significantly smaller local thermal capacity, so that the structure can overheat locally when using standard process parameters. This leads, for example, to undesirably large melt pools, which hinder the manufacturing process by forming large melt beads. Corresponding data must be stored in the database for all possible combinations of process and material parameters.In the individual application case, the appropriate data must be retrieved from the database and taken into account when calculating the temperature development.

[0049] The simulation data comprises calculated data determined based on a model and one or more specified parameters. The simulation data contains data about a building structure in which shrinkage and the development of structural stresses are taken into account during shaping by producing a geometry of the building structure modified using the simulation process, which assumes the desired geometry of the building structure due to the stresses and shrinkage. The experimental data comprises experimentally determined data. The experimental data comprises data about a building structure that was created in-situ in real time and / or in previous manufacturing processes. With the experimental data, the creation of the second process instruction can include data recorded based on real, non-simulated manufacturing processes.

[0050] In an advantageous embodiment of the invention, the second data set is used for an ML / AI algorithm. In a further development of the invention, the process instruction is created using an ML / AI algorithm.

[0051] In a further embodiment of the invention, the data of the initial process instruction are transferred to the second computer unit, wherein the data of the initial process instruction comprise the build structure geometry, the component geometry, the irradiation path of an energy beam, the exposure vectors and / or the process parameters of the beam source and / or the process parameters for influencing the energy input into the build structure. The initial process instruction provides basic data and process parameters for the additive manufacturing of a build structure, which forms the basis for creating a process instruction. Using the process instruction, a build structure can be produced which does not overheat in particularly vulnerable areas during the manufacturing process and has a lower residual stress distribution in the cooled and post-processed state, which is relevant, for example, in the manufacture of turbine blades.

[0052] In one development of the invention, the data of the initial procedural instruction are transferred to the second computer unit via a public network. Users have access to the public network. Users can transfer their own created procedural instructions to the public network and / or download procedural instructions stored on the public network. The procedural instructions created by the user can also have different file formats. In another development of the invention, the data of the procedural instruction are transferred to the first computer unit. In a further aspect of the invention, the data of the procedural instruction are transferred to the first computer unit via a public network.The data of the second process instruction are optionally sent from the first computer unit to the additive manufacturer and the build structure is additively manufactured using the process instruction.

[0053] In a further embodiment of the invention, the second computer unit is capable of reading initial procedural instructions in different data formats. The procedural instructions transferred by users to the public network can have different file formats, which are read by the second computer unit and used to create the procedural instructions. In an alternative embodiment, the second computer unit is implemented in a cloud environment.

[0054] In a further embodiment of the invention, the second computer unit is suitable for creating process instructions in different data formats. The process instructions can also be read in by additive manufacturers of different designs and used to produce a build structure.

[0055] In a further development of the invention, the initial process instruction comprises the irradiation path of an energy beam, the exposure vectors, the process parameters of the beam source and / or the process parameters for influencing the energy input into the building structure.

[0056] Process parameters are all variables that influence the manufacturing process using additive manufacturing. Process parameters are all variables that influence the process. The additive manufacturer requires process parameters to produce the component, e.g., the height of the layers to be produced, the orientation of the vectors, i.e., the direction and length of the path that the tool describes on the surface of the component to be manufactured. The method according to the invention creates an initial process instruction and a process instruction for a specific material intended for processing by additive manufacturing. The process parameters used depend on the additive manufacturer used to produce the component.

[0057] The tool path typically consists of a series of vectors that are traversed by the additive manufacturer. The process instructions thus define a process control that is executed by the additive manufacturer for additive manufacturing.

[0058] The warmer the already produced structure is, the slower the heat dissipation in the build occurs. The vector length influences the temperature development in that the repeated heating of neighboring points is spaced further apart due to the parallel position of successively exposed vectors. Another important factor is the mass distribution around the vectors, as this directly influences heat dissipation and thus the risk of overheating.

[0059] In a further embodiment of the invention, the process parameters for influencing the energy input into the build structure include the power of the energy beam, the irradiation times of individual vectors, the pause times between the irradiation times of individual vectors, the traversing speed of the energy beam, the hatch distance between the vectors, the vector sequence, the vector length, and / or the vector orientation. In this way, overheating in vulnerable areas of the component can be prevented. Process parameters are understood to be all variables that influence the manufacturing process using additive manufacturing.

[0060] In a further embodiment of the invention, data from the initial process instruction is read in and / or entered to create the process instruction, wherein the data from the initial process instruction comprises the build structure geometry, the component geometry, the irradiation path of an energy beam, the exposure vectors and / or the process parameters of the beam source and / or the process parameters for influencing the energy input into the build structure. The initial process instruction provides basic data and process parameters for the additive manufacturing of a build structure, which forms the basis for creating a process instruction. Using the process instruction, a build structure can be produced that does not overheat in particularly vulnerable areas during the manufacturing process and has a lower residual stress distribution in the cooled and post-processed state, which is relevant, for example, in the manufacture of turbine blades.

[0061] In a further embodiment of the invention, machine data of the additive manufacturer are read in and / or entered and / or used to create the process instruction, wherein the machine data includes the possible process parameters of the additive manufacturer, the possible travel speeds, and the possible travel paths of the component to the additive manufacturer. Machine data differs for different additive manufacturers and is therefore used to create the process instruction. At the same time, it is possible to create process instructions for different additive manufacturers; the method according to the invention can therefore be applied to different additive manufacturers.

[0062] In a further embodiment of the invention, the component data is read in and / or entered and / or used to create the process instruction, wherein the component data comprise the geometry of the build structure, the geometry of the component and / or material data, and wherein the material data comprise the phases, the concentration of the phases, the microstructure, the mechanical properties, the melting temperature and / or the boiling temperature. A build structure to be manufactured often contains thin-walled or overhanging structures. In these areas, the body provides a significantly smaller local thermal capacity, so that the structure can overheat locally when using standard process parameters. This leads, for example, to undesirably large melt pools, which hinder the manufacturing process due to the formation of large melt beads.For all possible combinations of process and material parameters, corresponding data must be stored in the database and / or in an ML model to predict process parameters, such as temperature. For each individual application, the appropriate data and / or ML model must be retrieved from the database and taken into account when calculating, for example, temperature development.

[0063] In a further embodiment of the invention, simulation data is read in and / or input and / or used to create the process instruction, wherein the simulation data comprises calculated data determined based on a model and set or predefined parameters. The simulation data includes data about a structural element in which shrinkage and the formation of structural stresses are taken into account during shaping by producing a structural element geometry modified by the simulation method, which assumes the desired structural element geometry due to the stresses and shrinkages.

[0064] In a further embodiment of the invention, experimental data is read in and / or input and / or used to create the process instruction, wherein the experimental data comprises experimentally determined data. The experimental data comprises data of a building structure that was created in-situ in real time and / or in previous manufacturing processes. With the experimental data, the creation of the second process instruction can include data acquired based on real, non-simulated manufacturing processes.

[0065] In a further embodiment of the invention, process parameters of the second process instruction are determined using an ML and / or AI algorithm to create the second process instruction. The ML and / or AI algorithm can use different methods to determine the process parameters.

[0066] In a further embodiment of the invention, the ML and / or AI algorithm uses empirical data to determine the process parameters of the process instruction, wherein the empirical data comprises machine data, component data, simulation data, and / or experimental data. The empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or structures, as well as the process instructions specific to each component. This data is stored in a database. For each individual application, the appropriate data must be retrieved from the database and used in calculating the temperature development using an ML and / or AI algorithm.

[0067] In a further development of the invention, the empirical data comprises machine data from different additive manufacturers. In a further embodiment of the invention, the different additive manufacturers comprise additive manufacturers of different designs. In a further embodiment of the invention, the different additive manufacturers use different CAM processes to manufacture a component. The machine data comprises the possible process parameters of the additive manufacturer, the possible travel speeds, and the possible travel paths of the component to the additive manufacturer. Machine data is different for different additive manufacturers and is therefore used to create the second process instruction. At the same time, it is possible to create process instructions for different additive manufacturers; the process according to the invention can therefore be applied to different additive manufacturers.

[0068] In a further embodiment of the invention, the empirical data comprises data from different CAM processes, wherein CAM processes include laser and / or electron beam powder bed fusion, direct energy deposition (DED), binder jetting, and / or other non-abrasive computer-aided manufacturing processes that rely on a tool path with associated process parameters. Such empirical data is used to create the process instruction; such a second process instruction can therefore be used for different CAM processes.

[0069] In an advantageous embodiment of the invention, the process instruction includes modified values ​​for the energy beam power, the irradiation times of individual vectors, the pause times between the irradiation times of individual vectors, the traversing speed of the energy beam, the increase in the hatch distance between the vectors, the vector sequence, the vector length, and / or the vector orientation compared to the initial process instruction. Using the process instruction, a structure can be produced that does not overheat in particularly vulnerable areas during the manufacturing process and exhibits a lower residual stress distribution in the cooled and post-processed state. The manufactured structure is therefore better protected against local and / or global overheating.

[0070] In a further embodiment of the invention, the structural structure and / or the component manufactured according to the process instruction has different mechanical properties compared to a structural structure and / or a component manufactured according to the initial process instruction. In a further aspect of the invention, the mechanical properties include the residual stress distribution in the structural structure and / or the component. Advantageously, the component manufactured according to the process instruction has a minimized residual stress distribution. The mechanical properties of the component are significantly improved compared to previously known processes. By means of the process according to the invention, local overheating is avoided, the quality of the finished product is increased, and the production yield is increased by producing less scrap.

[0071] In a further embodiment of the invention, during the manufacturing process, a modified residual stress distribution is generated in the structural member manufactured according to the process instruction and / or in the component manufactured according to the process instruction compared to a structural member manufactured according to the initial process instruction and / or a component manufactured according to the initial process instruction. Advantageously, the component manufactured according to the process instruction has a minimized residual stress distribution. The mechanical properties of the component are significantly improved compared to previously known processes. By means of the process according to the invention, local overheating is avoided, the quality of the finished product is increased, and the production yield is increased by producing less scrap.In a further embodiment of the invention, the structural member manufactured according to the process instruction has a different geometry than a structural member manufactured according to the initial process instruction. A geometry within the meaning of the invention is a spatial arrangement and includes properties such as angle, thickness and structure of the structural member. In a further development of the invention, the different geometry comprises the geometry of the component. In a further aspect of the invention, the different geometry comprises the geometry of the support structure. Preferably, the structural member manufactured according to the process instruction has a different support structure in terms of geometry than a structural member manufactured according to the initial process instruction such that the support structure has different attachment points on the component, such that the residual stress distribution in the manufactured component is changed.

[0072] In a further embodiment of the invention, the first method for creating the initial process instruction accesses a first set of empirical data, and the second method for creating the process instruction accesses a second set of empirical data, wherein the first set of empirical data is different from the second set of empirical data. The first set of empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or build structures, as well as the process instructions specific to each component. The empirical data includes machine data of the additive manufacturer for which the initial process instruction is to be created, as well as component data, simulation data of the temperature distribution in the build structure during the manufacturing process, and / or experimental data.The second set of empirical data includes the additive manufacturer's machine data, component data, simulation data and / or experimental data to determine the process parameters of the process instruction.

[0073] In a further embodiment of the invention, the first set of experience data is stored on a first storage device and the second set of experience data is stored on a second storage device, wherein the first storage device is different from the second storage device. In the context of the invention, a storage device is understood to mean, for example, a computer-readable memory in the form of a random-access memory (RAM) or a hard disk. Cloud storage is also possible.

[0074] In a further embodiment of the invention, the additive manufacturing of a component comprises CAM processes, wherein CAM processes include laser and / or electron beam powder bed fusion, direct energy deposition (DED), binder jetting, and / or other non-abrasive computer-aided manufacturing processes based on a tool path with associated process parameters. The method according to the invention for creating a process instruction and the created process instruction can therefore be used for different CAM processes.

[0075] Embodiments of the method according to the invention for providing a process instruction for the additive manufacturing of a component are shown in a simplified schematic form in the drawings and are explained in more detail in the following description.

[0076] They show:

[0077] Fig. 1 : Prior art method for providing a

[0078] Procedural instruction

[0079] Fig. 2: Inventive method for providing a procedural instruction

[0080] Fig. 3: Inventive method for providing a process instruction, two different software programs

[0081] Fig. 4: Method according to the invention for providing a process instruction, two different software programs and production of a first section of the component

[0082] Fig. 5: Inventive method for providing a process instruction, two different computer units Fig. 6: Inventive method for providing a process instruction, two different computer units and production of a first section of the component

[0083] Fig. 7: Inventive method for providing a method instruction, two different computer units and two different software programs

[0084] Fig. 8: Inventive method for providing a process instruction, two different computer units, two different software programs and production of a first section of the component

[0085] Fig. 9: Flowchart of the method according to the invention for providing a method instruction, separate computer units

[0086] Fig. 10: Flowchart of the method according to the invention for providing a process instruction, separate computer units and production of a first section of the component

[0087] Fig. 11: Flowchart of the method according to the invention for providing a method instruction, separate computer units and separate software programs

[0088] Fig. 12: Flowchart of the method according to the invention for providing a method instruction, separate computer units, separate software programs and production of a first section of the component

[0089] Fig. 1 shows an embodiment of a method for providing process instructions, as is known from the prior art. The starting point for carrying out additive manufacturing is a description of the workpiece using a data set. Using 3D modeling software (e.g. a CAD program), the data set for the structure of the component to be manufactured is created (CAD). The data set contains the three-dimensional data for preparation for production using the additive manufacturing process. This is followed by preprocessing 110 on the build platform such that the data set comprises a volume model of the component to be manufactured and is exported in another form that represents the self-contained surface geometry of the object. A manufacturing data set is generated from the data set, which contains a preparation of the geometry of the workpiece in layers or slices (so-calledSlices). This transformation of the data is called slicing 120.

[0090] In addition, the additive manufacturer requires additional process parameters and tool paths for production, e.g., the height of the layers to be produced, the orientation of the writing vectors, i.e., the direction and length of the path. These process parameters and tool paths are generated in the following process step 130 and sent to the additive manufacturer 300a / b. In the actual production process M, the structure described using CAD is additively manufactured layer by layer in the additive manufacturer using CAM.

[0091] An embodiment of the method according to the invention for providing a process instruction is shown in Fig. 2. In this and all subsequent embodiments, a process instruction is created to produce a build structure using directed energy deposition (DED). In DED, a powder or wire is fed coaxially to a laser to form a molten or sintered layer on a substrate. Support structures are often necessary in DED to attach the parts to the build plate and to secure overhangs.

[0092] First, the workpiece is 3D modeled using a dataset created using a CAD program. This is followed by preprocessing 110 on the build platform, followed by slicing 120. In the next process step, an initial process instruction is generated 100, whereby the data of the initial process instruction includes the build structure geometry, the component geometry, and the process parameters for influencing the energy input into the build structure. To create a process instruction 200, this data of the initial process instruction is read in 220, an ML algorithm AI / ML is applied to this data of the initial process instruction, and used to create 200 the process instruction.

[0093] In this and all other embodiments, an ML and / or AI algorithm (AI / ML) is used to generate the procedural instructions 200, which uses reinforcement learning. Reinforcement learning (RL) refers to a series of machine learning methods in which an agent independently learns a strategy to maximize received rewards. The agent is not shown which action is best in which situation; instead, it receives a reward, which can also be negative, at specific times through its interaction with its environment. Other possibilities include the use of an ML and / or AI algorithm that uses supervised learning or unsupervised learning, or intermediate stages of supervised learning or unsupervised learning. Deep learning can also be used.

[0094] The process instruction is sent to the additive manufacturer 300a / b, and the build structure to be manufactured is additively manufactured using the process instruction M.

[0095] Fig. 3 and Fig. 4 each show an exemplary embodiment of the method according to the invention, wherein the initial process instruction is created 100 using a first method PROG1, and the process instruction is created 200 using a second method PROG2, which is different from the first method PROG1. The first method PROG1 and the second method PROG2 are formalized processes defined in a first software program PROG1 and a second software program PROG2, respectively. The two software programs PROG1 and PROG2 are different from each other.

[0096] First, the workpiece is 3D modeled using a dataset created using a CAD program. This is followed by preprocessing 110 on the build platform using the first method PROG1, followed by slicing 120. In the next process step, an initial process instruction is generated 100, also using the first method PR0G1. The data of the initial process instruction includes the build structure geometry, the component geometry, and the process parameters for influencing the energy input into the build structure. These data and process parameters depend on the material of the build structure and on the CAM process used by the additive manufacturer to manufacture the build structure or component. The initial process instruction is created without an ML algorithm.

[0097] To this end, the first method PROG1 accesses 140 a first set of empirical data stored on a first storage device DB1. The first set of empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or build structures, as well as the process instructions specific to each component. The empirical data includes machine data of the additive manufacturer for which the initial process instruction is to be created, as well as component data, simulation data of the temperature distribution in the build structure during the manufacturing process, and / or experimental data.

[0098] The data and process parameters of the first process instruction created using the first method PROG1 are read in by the second method PROG2 to create 200 the second process instruction.

[0099] Furthermore, to create 200 the process instruction, machine data of the additive manufacturer used to manufacture M the building structure or component are read in and / or entered 210 and used to create 200 the process instruction. The machine data includes the possible process parameters of the additive manufacturer, the possible travel speeds, and the possible travel paths of the component to the additive manufacturer.

[0100] Component data is also read in and / or entered 210 to create 200 the process instruction and used for the creation 200 of the process instruction. The component data includes the geometry of the structure, the geometry of the component, and / or material data, whereby the material data includes the phases and the concentration of the phases at a given temperature profile, the microstructure, the mechanical properties, the melting temperature, and / or the boiling temperature.

[0101] In addition, simulation data is read in and / or entered 210 to create 200 the procedural instruction. The simulation data includes calculated data that was determined on the basis of a model and entered or specified parameters.

[0102] In addition, experimental data are read in and / or entered 210 to create 200 the process instruction. The experimental data include experimentally determined data and process parameters that are determined in real time during the manufacturing process M of the building structure and / or were determined from previous manufacturing processes.

[0103] Machine data of the additive manufacturer, component data, simulation data and experimental data are stored on a second storage device DB2 and are loaded from this to create 200 the process instruction.

[0104] The process instruction contains process parameters that are advantageously determined using an ML algorithm AI / ML. The ML algorithm uses empirical data to determine the process parameters of the second process instruction, wherein the empirical data includes the additive manufacturer's machine data, component data, simulation data, and / or experimental data stored on the second storage device DB2.

[0105] The process instruction is sent to the additive manufacturer 300a / b (Fig. 3), and the building structure to be manufactured is additively manufactured layer by layer using the process instruction M.

[0106] In a preferred variant of the invention, only a first section of the component is additively manufactured M using the process instruction. The process instruction created as described is therefore an initial process instruction, in other words a first part of the process instruction with which a first section of the component is additively manufactured M. In contrast, the entire component can be manufactured using the complete process instruction, as generated in Fig. 3. In this and the following exemplary embodiments, the first section of the component is a layer, and the first part of the process instruction is accordingly a process instruction for the additive manufacturing M of the first layer of the component. The ML algorithm AI / ML is applied to the initial process instruction (Fig. 4).

[0107] The production area in which the building structure is additively manufactured has a detection device S1 for this purpose (Fig. 4). By means of the detection device S1, in-situ experimental data of the manufacturing process are recorded in real time during the production of the first section of the building structure. In this and the other exemplary embodiments, the detection device S1 has a temperature detection device that records the temperature of the layer that is currently being additively applied. Further possibilities are imaging devices that record the melt pool sizes, a mold surface image and / or the spray or atomization quantity. The experimental data recorded by the detection device S1 also include, for example, the power of the energy beam, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, and / or the travel speed of the energy beam.

[0108] The acquired experimental data are sent to the second storage device DB2 and also stored in the second storage device DB2. These experimental data are read in 220 by the second method PROG2, and calculated data are determined from the experimental data. The ML algorithm AI / ML is applied to these calculated data, and during the execution of the initial process instruction, a second part of the process instruction for the production M of a second section of the component, i.e., a second layer of the component, is created 200. The second part of the process instruction has modified parameters for the production M of the second section of the component compared to the initial process instruction.The changed parameters include one or more parameters from the group of energy beam power, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, travel speed of the energy beam, change in the hatch distance between the vectors, the vector sequence, the vector length, vector orientation and / or changed geometry of the support structure.

[0109] This process is repeated until all sections, i.e., all layers of the component, have been manufactured. Following the generation of the second process instruction for additive manufacturing of a second layer of the component, a third process instruction for additive manufacturing of a third layer of the component is generated using the AI / ML ML algorithm, and so on. Each nth process instruction is created based on the (n-1)th process instruction using the AI / ML ML algorithm.

[0110] A further embodiment of the method according to the invention is shown in Fig. 5 and Fig. 6. Here, the initial process instruction is created 100 on a first computer unit COMP1 and the process instruction is created 200 on a second computer unit COMP2.

[0111] First, a 3D model of the workpiece is created on the first computer unit COMP1 using a dataset created using a CAD program. This is followed by preprocessing 110 on the build platform, followed by slicing 120. In the next process step, an initial process instruction is generated 100, with the data of the initial process instruction including the build structure geometry, the component geometry, and the process parameters for influencing the energy input into the build structure.

[0112] To create 200 a process instruction, these data of the initial process instruction are read in 220 by the second computer unit COMP2 and used for the creation 200 of the process instruction. The process instruction is sent 300a / b to the additive manufacturer, and the structure to be manufactured is additively manufactured M using the process instruction. Using the created initial process instruction, preferably only a first section of the component can also be additively manufactured. Using the detection device S1, in-situ experimental data of the manufacturing process are recorded in real time during the production of the first section of the structure (Fig. 6). The recorded experimental data are sent to the second computer unit COMP2 and also stored in the second storage device DB2. These experimental data are read in 220, and calculated data is determined from the experimental data.The ML algorithm AI / ML is applied to this calculated data and a second part of the process instruction for the production M of a second section of the component, i.e. a second layer of the component, is created 200.

[0113] This process is applied until all sections of the component have been manufactured. After the second process instruction for additive manufacturing of a second section of the component is generated, a third process instruction for additive manufacturing of a third section of the component is generated using the AI / ML algorithm, and so on, with each nth process instruction being created based on the (n-1)th process instruction.

[0114] Fig. 7 and Fig. 8 show the preferred embodiment of the method according to the invention. Here, the initial method instruction is created 100 on a first computer unit COMP1 using a first method PROG1. The method instruction is created 200 on a second computer unit COMP2 using a second method PROG2. The first computer unit COMP1 comprises the first storage device DB1, and the second computer unit COMP2 comprises the second storage device DB2. In this case, the first method PROG1 is different from the second method PROG2, the first computer unit COMP1 is different from the second computer unit COMP2, and the first storage device DB1 is different from the second storage device DB2.

[0115] First, the workpiece is 3D modeled using a dataset created using a CAD program. This is followed by preprocessing 110 on the build platform using the first method PROG1, followed by slicing 120. In the following process step, a first process instruction is generated 100 on the first computer unit COMP1, also using the first method PR0G1. The data of the first process instruction includes the build structure geometry, the component geometry, and the process parameters for influencing the energy input into the build structure. These data and process parameters depend on the material of the build structure and on the CAM process used by the additive manufacturer to produce the build structure or component.

[0116] To this end, the first method PROG1 accesses 140 a first set of empirical data stored on a first storage device DB1. The first set of empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or build structures, as well as the process instructions specific to each component. The empirical data includes machine data of the additive manufacturer for which the initial process instruction is to be created, as well as component data, simulation data of the temperature distribution in the build structure during the manufacturing process, and / or experimental data.

[0117] The data and process parameters of the initial process instruction created using the first method PROG1 are read into the second computer unit COMP2 by the second method PROG2 to create 200 the process instruction. Machine data of the additive manufacturer, component data, simulation data, and experimental data are stored on a second storage device DB2 and are loaded 210 from this storage device to create 200 the process instruction.

[0118] The process instruction contains process parameters, which are also determined using an ML algorithm. The ML algorithm uses empirical data to determine the process parameters of the process instruction, wherein the empirical data includes the machine data of the additive manufacturer, component data, simulation data, and / or experimental data stored on the second storage device DB2. The process instruction is sent to the additive manufacturer 300a / b, and the building structure to be manufactured is additively manufactured M using the second process instruction (Fig. 7).

[0119] Using the created initial process instruction, preferably only a first section of the component can also be additively manufactured. Using the detection device S1, in-situ experimental data of the manufacturing process are recorded in real time during the production of the first section of the structure (Fig. 8). The recorded experimental data are sent to the second computer unit COMP2 and also stored in the second storage device DB2. These experimental data are read in 220, and calculated data are determined from the experimental data. The ML algorithm AI / ML is applied to these calculated data using the second method PROG2, and a second part of the process instruction for the production M of a second section of the component, i.e., a second layer of the component, is created 200 using the second method PROG2.

[0120] This process is applied until all sections, i.e., all layers of the component, have been manufactured using the second method PROG2. Following the generation of the second process instruction for additive manufacturing of a second layer sequence of the component, a third process instruction for additive manufacturing of a third layer sequence of the component is generated using the ML algorithm AI / ML, and so on, with each nth process instruction being created based on the (n-1)th process instruction.

[0121] Fig. 9 and Fig. 10 show embodiments of a flow chart of the method 400 according to the invention. Initial method instruction and method instruction are created on separate and different computer units COMP1, COMP2 100, 200 (Fig. 9).

[0122] First, a 3D model of the workpiece is created using a data set created using a CAD program. In this and the following embodiment, the CAD program is executed on a computer unit different from the first computer unit COMP1 and the second computer unit COMP2. The CAD model contains data describing the structure to be manufactured. The data is provided in standardized file formats, for example, as an STL file (STL: Standard Tessellation Language). This CAD data is read by the first computer unit COMP1.

[0123] This is followed by pre-processing 110 on the build platform, followed by slicing 120. In the next process step, an initial process instruction is generated 130, which includes the build structure geometry, the component geometry and the process parameters for influencing the energy input into the build structure.

[0124] A first set of empirical data is then loaded 140 by the first computer unit COMP1 from a first database DB1, which is stored on a first storage device DB1. In this and the following exemplary embodiment, the first storage device DB1 is arranged in the first computer unit COMP1. The first set of empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or building structures, as well as the process instructions specific to each component. The empirical data includes machine data of the additive manufacturer for which the initial process instruction is to be created. Using this empirical data, the initial process instruction is created 150 by generating the process parameters and tool paths of the additive manufacturer.

[0125] Depending on the additive manufacturer's CAM method, the initial process instruction includes the irradiation path of an energy beam, the exposure vectors, the process parameters of the beam source, and / or the process parameters for influencing the energy input into the build structure. The process parameters for influencing the energy input into the build structure include the power of the energy beam, the irradiation times of individual vectors, the pause times between the irradiation times of individual vectors, the traversing speed of the energy beam, the hatch distance between the vectors, the vector sequence, the vector length, and / or the vector orientation. The process parameters for influencing the energy input into the build structure depend on the material of the build structure.

[0126] The process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors, and the process parameters of the beam source of the initial process instruction are optionally sent to a public network CL 150 (Fig. 6) and optionally stored on a storage unit of the public network CL. Users have access to the public network CL. Users can transfer their own created process instructions to the public network CL and download process instructions stored on the public network CL. The process instructions created by the user can also have different file formats.

[0127] Optionally, this initial process instruction is sent to an additive manufacturer 300a / b, and the build structure can be manufactured based on the first process instruction. Using the first process instruction, a first build structure—that is, a first component with a first support structure—can be manufactured.

[0128] Advantageously, a second structural element can be produced using the process instruction which is different from the first structural element. The structural element produced using the process instruction has modified mechanical characteristics compared to a structural element produced according to the initial process instruction and / or a component produced according to the initial process instruction, wherein the mechanical characteristics of the structural element produced using the process instruction in particular have modified, in particular minimized, distortion and improved residual stress distribution compared to the structural element produced using the initial process instruction. The structural element produced using the process instruction therefore has a modified geometry, in particular of the support structure, and possibly also of the component, compared to the structural element produced using the initial process instruction.

[0129] For this purpose, the process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors, and the process parameters of the beam source of the initial process instruction are read in 220 by a second computer unit COMP2, wherein the first computer unit COMP1 and the second computer unit COMP2 are arranged differently from each other and at a distance from each other. The process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors, and the process parameters of the beam source of the initial process instruction are read in 220 after they have been sent 150 from the first computer unit COMP1 to the second computer unit COMP2 (Fig. 9).In a further embodiment, the process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors and the process parameters of the beam source are read into the initial process instruction 220 after they have been optionally sent from the first computer unit COMP1 to the public network CL.

[0130] The second computer unit COMP2 is suitable for reading in initial procedural instructions in different data formats 220 and is also suitable for creating procedural instructions in different data formats.

[0131] The process instructions transferred by users to the optional public network CL can have different file formats, which are read in 220 by the second computer unit COMP2 and used to create 200 the process instructions. In addition, machine data of the additive manufacturer, component data, simulation data, and experimental data are read in and / or entered 210, which are stored on a second storage device DB2.

[0132] The process instruction contains process parameters that are also determined 230 using an ML algorithm AI / ML. The ML algorithm AI / ML uses empirical data to determine 230 the process parameters of the process instruction, wherein the empirical data includes the machine data of the additive manufacturer, component data, simulation data, and / or experimental data stored on the second storage device DB2. This is followed by a query 240 as to whether, based on the process parameters determined using the ML algorithm AI / ML, less distortion and, in particular, improved residual stress distribution and thus minimized distortion in the structure to be produced are achieved.

[0133] Using the created initial process instruction, preferably only a first section of the component can be additively manufactured. Using the detection device S1, in-situ experimental data of the manufacturing process are recorded in real time during the production of the first section of the structure (Fig. 10). The recorded experimental data are sent to the second computer unit COMP2 and also stored in the second storage device DB2. These experimental data are read in 220, and calculated data are determined from the experimental data. The ML algorithm AI / ML is applied to these calculated data, and a second part of the process instruction for the production M of a second section of the component, i.e., a second layer of the component, is created 200.

[0134] This process is applied until all sections, i.e., all layers of the component, have been manufactured using the second method PROG2. Following the generation of the second process instruction for additive manufacturing of a second layer of the component, a third process instruction for additive manufacturing of a third layer of the component is generated using the ML algorithm AI / ML, and so on, with each nth process instruction being created based on the (n-1)th process instruction.

[0135] The process parameters determined using the ML algorithm AI / ML are applied in further iterations of the application of the second method as the starting value for the application 230 of an ML algorithm AI / ML until a minimum of the residual stress distribution in the structure to be manufactured is determined. The process instruction therefore has process parameters with which a structure with minimized residual stress distribution can be manufactured. The process instruction is sent 300a / b to the additive manufacturer, and the structure to be manufactured is additively manufactured using the second process instruction M. Alternatively, the ML algorithm is used to determine 230 a predictive model of the distortion and residual stress distribution in the structure to be manufactured using the empirical data 220 loaded from the second database DB2. This predictive model is used by the second method PROG2 as the starting value for optimization algorithms.By means of the optimization algorithms, process parameters of the second process instruction are optimized by means of process steps 220 to 240 until a minimized distortion and optimized residual stress distribution in the structure to be produced is determined.

[0136] By means of the method 400 according to the invention, a method instruction for the additive manufacturing of a building structure is provided, with which a building structure can be produced using various CAM methods. The CAM methods include laser and / or electron beam powder bed fusion, direct energy deposition (DED), binder jetting, fused filament fabrication (FFF), melt filament printing, and / or other non-abrasive computer-aided manufacturing methods that rely on a tool path with associated process parameters.

[0137] Fig. 11 and Fig. 12 show preferred embodiments of flow diagrams of the method 400 according to the invention. The embodiments shown here correspond to the previous embodiments (see Fig. 9, Fig. 10), only the method steps preprocessing on build platform 110, slicing 120 and generation of the process parameters and tool paths 130 are carried out on the first computer unit COMP1 using a first method PROG1, i.e. a first computer program PROG1. On the second computer unit COMP2, the method steps reading in data from the initial process instruction 220, creating the process instruction including the data from the initial process instruction and applying 230 an ML algorithm AI / ML and query 240 are carried out using a second method PROG2, i.e. a second computer program PROG2. The first PROG1 and second computer program PROG2 are executed differently from one another.The first computer program PROG1, unlike the second computer program PROG2, does not have an AI / ML algorithm. First, a 3D model of the workpiece is created using a data set created using a CAD program. In this and the following exemplary embodiment, the CAD program is executed on a computer unit different from the first computer unit COMP1 and the second computer unit COMP2. The CAD model contains data describing the structure to be manufactured. The data is provided in standardized file formats, for example, as an STL file (STL: Standard Tessellation Language) or other implicit or explicit file formats. This CAD data is read in by the first computer unit COMP1.

[0138] This is followed by pre-processing 110 on the build platform, followed by slicing 120. In the next process step, an initial process instruction is generated 130, which includes the build structure geometry, the component geometry and the process parameters for influencing the energy input into the build structure.

[0139] From a first database DB1, the first computer unit COMP1 then loads 140 a first set of empirical data, which is stored on a first storage device DB1. The first storage device DB1 is arranged in the first computer unit COMP1. The first set of empirical data comprises data that was recorded and created using one or more previous additive manufacturing processes for components or building structures, as well as the process instructions specific to each component. The empirical data includes machine data of the additive manufacturer for which the first process instruction is to be created. Using this empirical data, the initial process instruction is created 150 by generating the process parameters and tool paths of the additive manufacturer.

[0140] Depending on the additive manufacturer's CAM method, the initial process instruction includes the irradiation path of an energy beam, the exposure vectors, the process parameters of the beam source, and / or the process parameters for influencing the energy input into the build structure. The process parameters for influencing the energy input into the build structure include the power of the energy beam, the irradiation times of individual vectors, the pause times between the irradiation times of individual vectors, the traversing speed of the energy beam, the hatch distance between the vectors, the vector sequence, the vector length, and / or the vector orientation. The process parameters for influencing the energy input into the build structure depend on the material of the build structure.

[0141] The process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors, and the process parameters of the beam source of the initial process instruction are optionally sent to a public network CL 150 (Fig. 6) and optionally stored on a storage unit of the public network CL. Users have access to the public network CL. Users can transfer their own created process instructions to the public network CL and download process instructions stored on the public network CL. The process instructions created by the user can also have different file formats.

[0142] Optionally, this initial process instruction is sent to an additive manufacturer 300a / b, and the build structure can be manufactured based on the initial process instruction. Using the initial process instruction, a first build structure—that is, a first component with a first support structure—can be manufactured.

[0143] Advantageously, a second structural element can be produced using the process instruction which is different from the first structural element. The structural element that can be produced using the process instruction has modified mechanical characteristics compared to a structural element manufactured according to the initial process instruction and / or a component manufactured according to the initial process instruction, wherein the mechanical characteristics of the structural element that can be produced using the process instruction have, in particular, a modified, in particular minimized, residual stress distribution compared to the structural element that can be produced using the initial process instruction. The structural element that can be produced using the process instruction therefore has, compared to the structural element that can be produced using the initial process instruction, a modified geometry, in particular of the support structure, and possibly also of the component.For this purpose, the process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors, and the process parameters of the beam source of the initial process instruction are read in 220 by a second computer unit COMP2, wherein the first computer unit COMP1 and the second computer unit COMP2 are arranged differently from each other and at a distance from each other. The process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors, and the process parameters of the beam source of the initial process instruction are read in 220 after they have been sent 150 from the first computer unit COMP1 to the second computer unit COMP2 (Fig. 9).In a further embodiment, the process parameters for influencing the energy input into the building structure, the irradiation path of an energy beam, the exposure vectors and the process parameters of the beam source are read into the initial process instruction 220 after they have been optionally sent from the first computer unit COMP1 to the public network CL.

[0144] The second computer unit COMP2 is suitable for reading in initial procedural instructions in different data formats 220 and is also suitable for creating procedural instructions in different data formats.

[0145] The process instructions transferred by users to the optional public network CL can have different file formats, which are read in 220 by the second computer unit COMP2 and used to create 200 the process instructions. In addition, machine data of the additive manufacturer, component data, simulation data, and experimental data are read in and / or entered 210, which are stored on a second storage device DB2.

[0146] The process instruction contains process parameters that are also determined 230 using an ML algorithm AI / ML. The ML algorithm AI / ML uses empirical data to determine 230 the process parameters of the process instruction, wherein the empirical data includes the machine data of the additive manufacturer, component data, simulation data, and / or experimental data stored on the second storage device DB2. This is followed by a query 240 as to whether a lower, in particular minimized, residual stress distribution is achieved in the structural element to be manufactured due to the process parameters determined using the ML algorithm AI / ML.

[0147] Using the created initial process instruction, preferably only a first section of the component can be additively manufactured. Using the detection device S1, in-situ experimental data of the manufacturing process are recorded in real time during the production of the first section of the structure (Fig. 10). The recorded experimental data are sent to the second computer unit COMP2 and also stored in the second storage device DB2. These experimental data are read in 220, and calculated data are determined from the experimental data. The ML algorithm AI / ML is applied to these calculated data, and a second part of the process instruction for the production M of a second section of the component, i.e., a second layer of the component, is created 200.

[0148] This process is applied until all sections, i.e., all layers of the component, have been manufactured using the second method PROG2. Following the generation of the second process instruction for additive manufacturing of a second layer of the component, a third process instruction for additive manufacturing of a third layer of the component is generated using the ML algorithm AI / ML, and so on, with each nth process instruction being created based on the (n-1)th process instruction.

[0149] The process parameters determined using the ML algorithm AI / ML are applied in further iterations of the application of the second method as the starting value of the application 230 of an ML algorithm AI / ML until a minimum of the residual stress distribution in the build structure to be produced is determined. The process instruction therefore has process parameters with which a build structure with minimized residual stress distribution can be produced. The process instruction is sent to the additive manufacturer 300a / b, and the build structure to be produced is additively manufactured using the second process instruction M. By means of the method 400 according to the invention, a process instruction for the additive manufacturing of a build structure is provided, with which a build structure can be produced using different CAM processes. The CAM processes include laser and / or electron beam powder bed fusion, direct energy deposition (DED) binder

[0150] Jetting, Fused Filament Fabrication (FFF), melt filament printing and / or other non-abrasive computer-aided manufacturing processes that rely on a tool path with process parameters assigned to it.

[0151] B EZ UG S CHARACTERS LIST

[0152] CAD Creating a CAD model

[0153] C0MP1 First Computer Unit

[0154] C0MP2 Second computer unit

[0155] PR0G1 First Software

[0156] PR0G2 Second Software

[0157] CL Public Network

[0158] AI / ML ML algorithm

[0159] DB1 First storage device

[0160] DB2 Second Storage Facility

[0161] S1 Detection device M Execution of the process instruction / Additive manufacturing of the component

[0162] 100 Creating the initial procedure instruction

[0163] 110 Preprocessing on build platform / in process chamber

[0164] 120 Slicing / Creating a layer structure

[0165] 130 Generation of process parameters and tool paths

[0166] 140 Reading data from the first database

[0167] 150 Generation of process parameters and tool paths using data from the first database

[0168] 200 Creating a procedural instruction

[0169] 210 Reading data from a second database

[0170] 220 Reading data from the initial procedure instruction

[0171] 230 Creating the process instruction using the data from the initial process instruction and applying an ML algorithm Query Sending the process instruction a / b Sending the process instruction to the additive manufacturer Procedure for providing a process instruction for the additive manufacturing of a component

Claims

PATENT CLAIMS 1. Method (400) for providing a process instruction for the additive manufacturing (M) of a component with the process steps: • Reading in geometric data of the component • Creating (120) a layer structure of a building structure, wherein the building structure comprises the component, • Creating (200) a process instruction for the additive manufacturing of the building structure, wherein the process instruction comprises one or more parameters from the group of power of the energy beam, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, travel speed of the energy beam, change in the hatch distance between the vectors, the vector sequence, the vector length, vector orientation and / or changed geometry of the support structure, wherein an ML algorithm (AI / ML) is used (230) to create (200) the process instruction.

2. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 1, characterized in that the building structure and / or the layer structure comprises a support structure.

3. Method (400) for providing a procedural instruction for additive Manufacturing (M) of a component according to claim 1 or 2, characterized in that the ML algorithm (AI / ML) is applied (230) to an initial process instruction. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 3, characterized in that the initial process instruction is created (100) without an ML algorithm (AI / ML). Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to one or more of the preceding claims, characterized in that ML data are read in from a database (DB2) for the use (230) of the ML algorithm (AI / ML). Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 5, characterized in that the ML data are read in (210) from a separate database (DB2).Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 5 or 6, characterized in that the ML data originate from different manufacturing processes for additive manufacturing. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to one or more of the preceding claims, characterized in that. for the use (230) of the ML algorithm (AI / ML) experimental data and / or simulation data are used.

9. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 8, characterized in that the experimental data include the local temperature, power of the energy beam, irradiation time of individual vectors, pause time between the irradiation times of individual vectors and / or the travel speed of the energy beam.

10. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 8 or 9, characterized in that calculated data are determined from the experimental data.

11. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to one or more of the preceding claims, characterized in that experimental data are recorded during the execution of a first part of the process instruction for the manufacturing (M) of a first section of the component.

12. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 11, characterized in that the experimental data are recorded in-situ. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 11 or 12, characterized in that, during the execution of the process instruction, a second part of the process instruction for the manufacturing (M) of the component is created from the experimental data acquired in situ. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 13, characterized in that the second part of the process instruction is created for the manufacturing (M) of a second section of the component. Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 13 or 14, characterized in that the second part of the process instruction has modified parameters for the manufacturing (M) of the same section of the component compared to the process instruction.Method (400) for providing a process instruction for the additive manufacturing (M) of a component according to claim 15, characterized in that the changed parameters comprise one or more parameters from the group consisting of power of the energy beam, irradiation time of individual vectors, pause time between the irradiation times of individual vectors, travel speed of the energy beam, change in the hatch distance between the vectors, the vector sequence, the vector length, vector orientation and / or changed geometry of the support structure.

17. Method (400) for providing a procedural instruction for additive Manufacturing (M) of a component according to one or more of the preceding claims, characterized in that the initial process instruction was created on a first computer unit (COMP1) and the process instruction is created on a second computer unit (COMP2), wherein the first computer unit (COMP1) is different from the second computer unit (COMP2).

18. Method (400) for providing a process instruction for additive Manufacturing (M) of a component according to one or more of the preceding claims, characterized in that the initial process instruction was created with a first software program (PROG1) and the process instruction is created with a second software program (PROG2), wherein the first software program (PROG1) is different from the second software program (PROG2).

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