WO2019057284A1 - Génération de capacités d'unités de production pouvant être automatisée - Google Patents

Génération de capacités d'unités de production pouvant être automatisée Download PDF

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Publication number
WO2019057284A1
WO2019057284A1 PCT/EP2017/073928 EP2017073928W WO2019057284A1 WO 2019057284 A1 WO2019057284 A1 WO 2019057284A1 EP 2017073928 W EP2017073928 W EP 2017073928W WO 2019057284 A1 WO2019057284 A1 WO 2019057284A1
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Prior art keywords
simulation
sdn
skn
simulation data
ski
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PCT/EP2017/073928
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German (de)
English (en)
Inventor
Rudolf Sollacher
Jan Fischer
Original Assignee
Siemens Aktiengesellschaft
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Priority to PCT/EP2017/073928 priority Critical patent/WO2019057284A1/fr
Publication of WO2019057284A1 publication Critical patent/WO2019057284A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/23—Pc programming
    • G05B2219/23448—Find optimum solution by simulating process with constraints on inputs
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32301—Simulate production, process stages, determine optimum scheduling rules
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32352—Modular modeling, decompose large system in smaller systems to simulate
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the invention relates to a method for the automated generation of capabilities of one or more production units.
  • the invention further relates to a simulation system, a production unit and an automation device.
  • Modern production systems are increasingly using production units that not only flexibly apply their abilities, but also offer them to other production units.
  • the production units develop capabilities by combining behavior patterns, whereby the production units effect at least one state transition of a workpiece by using capabilities.
  • Requirements describe one or more state transitions that the workpiece is to go through, and a first database has generic descriptions of behavior patterns of the production units.
  • the generic descriptions of the behavior patterns can u.
  • A. in the form of simulation models associated parameter sets for parameterizing the production units as well as other data structures that allow a description of the behavioral patterns.
  • Production units are to be understood as devices or software components in a production, which by
  • Skills are composed of behavioral patterns. Behavioral patterns include a subordinate and fine-grained description of individual functions that a production unit can perform. If, for example, a robot with gripper arm is to realize the ability to "position a workpiece" reindeer, so you can combine this example, the following behavioral patterns: open gripping the workpiece, lifting the workpiece, rotating the workpiece, to handle the workpiece, and closing ⁇ Lich gripper again. This shows how complex even the simplest skills are. Behavior pattern can include both the actual design parameters for the respective production unit, as well as a sufficiently exact Modellie ⁇ tion of a model can be simulated precisely this parameterized production unit. This dichotomy is also described in technical jargon as a digital twin.
  • Behavior patterns can thereby be split into its digital model and the actual execution of the behavior pattern on a phy ⁇ sical terminal. Similarly, abilities can be described by the composite digital models of behavioral patterns as well as actual performance of the capability on the production unit.
  • Production units can also consist of several subunits with further behavioral patterns, such as, for example, an industrial robot for handling the workpiece, as well as another industrial robot, which takes over the welding at certain points of the workpiece.
  • production units can also be programming devices that do not physically process the workpiece, but rather, for B. carry out a firmware programming of the workpiece.
  • Workpieces are precursors of the product in the final state, in each case with respect to the current production process, and may be the materials DA at any stage of a product incl. ⁇ to use.
  • the workpiece in the final state becomes the product.
  • workpieces are, for example, materials and raw materials in any state of aggregation, individual components, software components or entire assemblies in question.
  • the workpiece undergoes conditions in the production process.
  • the ⁇ se states can be obtained and characterized by any actual production ⁇ processes. It can for example, it can also be a simple bore in the body of the workpiece, such as a surface coating, or even the parameterization of a component on an electrical part of the workpiece. States can be described or limited by their necessary previous states. For example, it is necessary to provide a bore before a bolt can be passed through the hole.
  • States are merged by state transitions. Examples of state transitions are Materialentfer ⁇ tion, coating of the workpiece and also quality checks that do not change the workpiece per se but only provide information about the workpiece and its further whereabouts.
  • Requirements also called “requirements” describe one or more state transitions that the work piece is to pass through, eg as a sequence of states that the work piece is to pass through in the production process It is also conceivable that other data is made available that must be converted into requests yet. for example, data, which merely represent the product in the final state, and should only by one or more further steps in requirements including a sequence of states must be converted.
  • This sequence of states as ⁇ independently at ideally be defined by the production units. it may make sense to describe his individual states directly, using the skills of a particular production unit, such as finishing off yet is no more appropriate production unit or Kay ⁇ ability to achieve this state.
  • Requirements can be assigned to the abilities or behavior patterns by comparing their input and / or output states.
  • state ⁇ previously had to be represented by multiple skills transitions, now to be imaged by a single new ability. This may be the case when evaluating whether a new production unit with extended functionality is viable.
  • the generic descriptions of the behavioral patterns include or refer to simulation data of the behavioral patterns. These may be physical electrical thermal me- chanical as well as any further necessary simulation data for the respective application.
  • the present method is most efficiently practiced when certain data structures already exist that are relevant to modeling various aspects of a production unit, workpieces, and tools.
  • digital twin In today's parlance and in the context of industrial 4.0 in English also "digitally twin", the talk is often of ei ⁇ nem so-called digital twin.
  • Optimization criteria are any criteria that are suitable for assessing the suitability of a simulated result for actual use in a production unit.
  • the method comprises the step of: determining the parameters of the simulation data record which can be optimized by the simulation. This can be done in advance z. B. a sensitivity analysis can be performed.
  • tools are taken into account when creating the simulation data record.
  • Next ⁇ out can be selected on the basis of tools for creating the simulation data set Verhal ⁇ least pattern.
  • the first database includes generic descriptions of tools whose application forms patterns of behavior.
  • the step of simulating is performed using constraints. Boundary conditions can include so-called
  • boundary conditions are already applied during the step of creating the simulation data set, it is possible to rule out from the outset unnecessary simulations that violate certain boundary conditions from the outset (eg very long duration of the process for time-critical workpieces).
  • the capabilities of the production units are modular. This makes it possible to further use optimized and simulated basic modules and to adapt them to new capabilities which, for example, should fulfill new boundary conditions, such as increased quality requirements.
  • At least one of the steps is carried out fully automatically by the simulation system.
  • the structured design of the process made it ⁇ light-efficient and automated implementation of the method. This is true even if two, three or all steps are fully automated.
  • the object is further solved by a simulation system, the at least one communication interface for communica tion ⁇ with at least one of the databases and a simulation onsaku, which is configured at least to execute the following ⁇ steps:
  • a production unit that is designed to combine behavior patterns with capabilities and with the application of skills to workpieces, comprising:
  • a communication module that is configured to communicate with at least ⁇ a database
  • a processor which is at least configured to execute capabilities created by a method according to the invention
  • the object is further by a programmable controller for use with an inventive production unit, comprising at least one processor, which is configured at least to the off ⁇ management and / or control capabilities that were created by a method according to he invention.
  • 3 shows a further embodiment of a simulation system
  • 4 shows a further embodiment and spatial distribution of a simulation system
  • FIG. 8 shows a third step of the method in the configuration from FIGS. 5 to 7 and FIG
  • FIG 9 shows a fourth step of the method in the configura ⁇ tion of FIG 5 to 8.
  • FIG. 1 shows a workpiece W that undergoes a production process in its various states StO, Stl, St2 and St3.
  • the production process is too
  • a bore B into the blank of the workpiece W ⁇ is intended to, the workpiece W, a surface treatment C conservation th to (for example, a paint or a passivation - approximately layer) and finally as part of the production process ⁇ a quality assurance measure carried out who is ⁇ .
  • the workpiece W is to during the production process through from ⁇ continuously from the original state StO, the following states Stl, St2, St3:
  • the workpiece W now has a surface coating C in addition to the hole H.
  • the surface coating C can be a coating or a further surface coating.
  • State St3 The workpiece W has no longer changed its physical properties but has been subjected to a qualitative Quality control Q, here represented by a camera, un ⁇ terzogen. The physical properties of the workpiece W have thus not changed, but it is now ensured that the workpiece W meets certain quality requirements, which is reflected in a change of state. Such state transitions can also be taken into account by the present method.
  • Descriptions of the physical parameters such as shape, weight, composition, maximum possible acceleration, maximum possible pressure, minimum and maximum temperature and other parameters can be used to model the workpieces W. If an ability causes a state transition on one or more workpieces, a description of the process parameters is also part of the digital description or modeling of the workpiece.
  • the creation of the cylindrical bore H in the workpiece W should be mentioned here.
  • boundary conditions such as, for example, the maximum temperature of the workpiece during the drilling process must be taken into account.
  • the state transition StTOl here describes a Mate ⁇ rialentfernung, in this case, to create a bore.
  • This state transition StTOl can be characterized is that already specific tools can be specified bring about one of the ⁇ -like state transition, such as. A drill or a laser cutter.
  • the state transition StTO1 can furthermore be described via the input and output states, ie the workpiece W in the state StO without bore and the workpiece W in the state Stl with bore H and possibly a delta between the input and output states.
  • state transitions StTO1, StT12, StT23 can be described in various ways.
  • the state transition StTOl is described by the capabilities SK011, SK012, SK013.
  • StT12 is described by behavior patterns B121, B122, B123.
  • the state transition StT23 is described by boundary conditions CON231, CON232, CON233. It is also possible to describe the state transitions through combinations of abilities, behavior patterns and / or boundary conditions as well as other modeling forms not mentioned here.
  • the single-variety description in the present case is merely exemplary. If an alternative for one of the SK011, SK012, SK013 abilities is to be generated by the present method, then it might be necessary to analyze the abilities in advance with regard to their input and output states. But it is also conceivable that the state transitions in addition to the necessary capabilities also have other boundary conditions that simplify the creation of a simulation model. The broader the database available to the simulation system, the easier new SKI capabilities,
  • SKn be created.
  • a meaningful variant is that not only SKI, SKn, behavior patterns B1, Bn or boundary conditions CON1, CONn are provided, as shown here for reasons of clarity, but, depending on the state transition to be executed, a mixture of existing capabilities SKI, SKn, in the production system Available behavior patterns Bl, Bn and / or boundary conditions CON1, CONn are stored. This is performed in each case depending on the production step ⁇ .
  • the surface coating C is specified and described. Is this at ⁇ play, be a lacquer which are a curing underzo ⁇ gen must, as are appropriate boundary conditions such as the curing temperature, the paint texture and -färbe and possibly a prior surface treatment to hinterle ⁇ gen here.
  • the state transition StT23 includes the quality measures Q to be performed on the workpiece W. This may be, for example, an optical quality equalization of both the surface coating C and the geometric properties of the bore H as well as their position. It is particularly advantageous that such quality measures Q can be carried out not only in a separate step, but by matching each state Stl, St2, St3 with the stored simulation model or the digital twin. In this way, the quality of each individual state transition StTOl, StT12, StT23 can be assessed and already at an early stage
  • FIG. 2 shows a possible configuration of a Simulationssys ⁇ tems simulation systems, comprising a processor CPU and a memory HDD.
  • the selection of CPUs depends on the SIMSYS simulation system and is subject to consideration of performance requirements and cost-effectiveness, whereby commercial PC processors up to specialized processors (eg Graphics Processing Units / Physics Processing Units) are also used in combination with corresponding computing units can.
  • the memory HDD can be designed as a nonvolatile memory in the sense of a hard disk, a cloud memory or other local or decentralized memory options.
  • a communication interface COM enables the simulation system SIMSYS to communicate with a first database
  • DB1 having behavior patterns Bl, Bn.
  • This communication is carried out via a communication network NET.
  • a production unit Ul can be seen, which the Possibility to be equipped with a first tool Tl or a second tool T2.
  • the production ⁇ unit Ul is designed as a robot arm, the tool Tl is designed as a gripper, the tool T2 as a welding tip.
  • the base of the production unit U1 indicates that it has different behavior patterns B1, B2, B1l, B12, B13, B22, B21.
  • behavior patterns B1, B2 are to be regarded as basic behavior patterns which, for example, enable a movement of the robot without its tools T1, T2.
  • the behavior patterns Bll, B12, B13 are assigned to the first tool T1
  • the behavior patterns B21, B22 are assigned to the second tool T1.
  • the display of the simulation system simulation systems, a Simula ⁇ tion model Ul can be seen ⁇ of the first production unit Ul.
  • the graphical representation shown here should not necessarily mean that it is necessarily an SD-CAD representation on an HMI, but rather should represent that by means of the simulation system SIMSYS a simulation model Ul ⁇ in the necessary and best for the application Shape can be displayed and simulated.
  • a graphical representation is not necessarily necessary, but can be very helpful for monitoring the simulation. Such representations can also be streamed over long distances and are not tied to the location of the simulation system SIMSYS.
  • FIG. 3 shows a further embodiment of a Simulationssys ⁇ tems simulation systems that communicate over a first communication network NET1 having a first and a second database DB1, DB2 can.
  • the simulation system SIMSYS can communicate with a third database DB3 and a first and a second production unit U1, U2 via a second communication network NET2.
  • the second communication network NET2 can be an industrial network based, for example, on PROFIBUS, PROFINET or OPC UA.
  • the third database DB3 has capabilities SKI, SKn, which can be created by the method according to the invention by the simulation system SIMSYS and stored there. It is also possible that the third database DB3 pre ⁇ made skills SKI, SCn for further use in the production unit Ul and / or simulation system
  • SIMSYS includes.
  • existing abilities are analyzed SKI, SCn and used as a basis for new Desi ⁇ skills SKI, SCn. This can be done by their structure or exchange of individual behavior patterns (new tool, with improved properties, new drive with increased travel speeds, etc.).
  • the simulation system SIMSYS not only generates capabilities SKI, SKn, but also plays the capabilities SKI, SKn directly after the generation on one of the production units U1, U2.
  • the configuration of the production units U1, U2 can also be left to further configuration units (not shown). It is conceivable that the Simulati ⁇ onshim simulation systems is completely separated from the production units Ul, U2.
  • FIG. 4 shows a further embodiment and spatial distri ⁇ development of a simulation system simulation systems, as it is already known from the previous figures.
  • a decentralized infrastructure 100 which may be designed, for example, as a cloud solution, is provided.
  • the simulation system In the decentralized infrastructure 100 is the simulation system
  • the decentralized infrastructure 100 is available over a wide area network WAN, in English "Wide Area Network", connected to an industrial plant PLANT, which can be the Internet using industry-standard tunnels (VPN) and / or encryption mechanisms.
  • WAN wide area network
  • PLANT which can be the Internet using industry-standard tunnels (VPN) and / or encryption mechanisms.
  • VPN industry-standard tunnels
  • the three databases DB1, DB2, DB3 also can be for purely logical instances, and the actual physical In ⁇ mentation always on the application area and z.
  • the databases DB1, DB2, DB3 are connected via a communication network NET by means of their communication interfaces COM.
  • the simulation system SIMSYS thus has access to behavior patterns Bl, Bn stored in the first database DB1, requests REQ1, REQn stored in the second database DB2, and to a third database DB3 in which capabilities SKI, SKn can be stored , Prefabricated capabilities SKI, SKn can be available in the third database DB3 for simulation in the SIMSYS simulation system.
  • SIMSYS has loaded a current request REQ, which aims to transfer a workpiece W in the state StO in a workpiece W in the condition Stl. It can be seen that the already known from FIG 1 bore H in the workpiece W is to be ⁇ brought. This is done by means of a state transition StTOl. Necessary information / data regarding the state transition StTO1 are contained in the REQ requirements, whereby, in addition, boundary conditions, eg regarding permissible process temperatures, machining times, surface finishes, tolerances, ... can also be included in the REQ requirements.
  • the method according to the invention now makes it possible, by means of the simulation system SIMSYS for the state transition StTO1, to generate new capabilities SKI, SKn. It has been found to be advantageous to equip the Si ⁇ simulation models with parameters interfaces as they exhibit the production units Ul, Un, in order to use the simulation data directly as parameter sets for the production units.
  • the state transition StTOl having here exemplified three Ver ⁇ hold pattern Bl, B2, B3, corresponding behavior patterns Bl, B2, B3 of the production units Ul, U2, U3 associated as a possible pattern of behavior.
  • the possible assignments of the behavior patterns Bl, B2, B3 are shown here in a part of the first database DBl ⁇ . This is intended to indicate that the entire database DB1 is not required for creating a simulation data record SD1, SD2, but of course a pre-selection can be made in order to minimize the traffic between the database DB1 and the simulation system SIMSYS.
  • the behavior pattern Bl is the positioning of the workpiece W
  • the behavior pattern B2 the introduction of a hole in the workpiece W and
  • the pattern B3 the removal of the waste products created by the drilling and, if necessary, burrs. It is indicated in each case that the behavior pattern Bl is offered by the production units U1, U3 and the behavior patterns B2, B3 are each offered by the production units U2 and U3. Exemplary are now for the Zu- State transition StTOl two simulation data sets SD1, SD2 he ⁇ been.
  • the simulation data record SD1 combines the behavior patterns B1, B2, B3 excluding the production unit U3. This could be, for example, a specialized drilling device, which offers the positioning of the workpiece, the introduction of the bore and the removal of the waste itself.
  • the simulation data set SD2 combines the behavior patterns Bl, B2, B3 of the production units Ul and U2, where Ul only provides the behavior pattern Bl and U2 provides the behavior patterns B2 and B3.
  • Ul only provides the behavior pattern Bl and U2 provides the behavior patterns B2 and B3.
  • a complete sublimation of the material to be removed could take place, which is why the behavior pattern B3 is implicitly met by the laser device U2, since the waste products are removed in gaseous form.
  • FIG 7 now shows the simulation of one of the Simulationsoires Kunststoff ⁇ ze SD1 using a simulation time TSIM, first boundary conditions CON1 and CON2 second constraints.
  • the workpiece W is simulatively transferred in the state StO in the workpiece W in the state Stl.
  • the SIMRES1 simulation results are symbolically represented here as two graphs which, for example, can represent relevant variables, also called "Key Performance Indicators" (KPIs) .Also here, a graphic evaluation of the simulation results is not necessary, but can be carried out . If the simulation time TSIM made in discrete steps available, can be for these discrete steps for all simulated own sheep ⁇ th and for all simulation participants calculate appropriate increments.
  • KPIs Key Performance Indicators
  • the simulation time TSIM can also be when he ⁇ eignisdiskrete Figure discrete event increments chosen .
  • the condition of the production unit, if necessary, of the work ⁇ zeugs, the state of the workpiece W as well as the state of environmental kind be in these increments (time or discrete values) as long as simulated until the desired result, that is state transition of the supply StTOl is completed or the workpiece to stand ⁇ Stl occurred or is other termination criteria for the simula- tion.
  • Other termination criteria can be given, for example, when exceeding a certain period of time, which can be stored in one of the boundary conditions CON1 or CON2.
  • the boundary conditions CON1, CON2 can also be maximum temperatures of the workpiece, the environment or the tool, as well as quality criteria and other boundary conditions that would apply the expert in a simulative environment.
  • FIG 8 shows the step S3 of the method, in which Simulationser ⁇ results of SIMRES1, SIMRES2 are compared SIMRES3 with definable criteria optimization ⁇ approximately OPT. Results 1, 2, 3 of the comparison are also shown.
  • the results of SIMRES1 Simulationser ⁇ do not meet the defined optimization criteria OPT, which is correspondingly gekennzeich ⁇ net in the result is 1, the results satisfy the SIMRES2 define Opti ⁇ m istskriterien OPT, so the result with a 2 Ha- If the simulation results SIMRES3 do not meet the optimization criteria OPT, then it is indicated in the results 3 that the simulation with adjusted parameters must be carried out again.
  • FIG. 9 shows step S4, in which simulation data sets SD1, SD3 with the associated parameter sets PARAM1, PARAM31 are respectively stored as a capability SKI, SK31.
  • which parameters or which information is stored in the capability SKI, SK31 ⁇ the.
  • the respective parameter sets the simulation results and the simulation models are stored with the skills to enable a later review or optimization. Gradations are possible. This is advantageous at low cost, particularly in times of high availability memory system, ⁇ men.
  • the capabilities SKI, SK31 are stored in the database DB3.
  • Storing the best parameters PARAM1, PARAMn and storing the parameters PARAM1, PARAMn in a new capability SKI, SKn and / or storing the determined sequence of behavior patterns B1, Bn in a new capability SKI, SKn can be done efficiently and efficiently with the method according to the invention be carried out fully automatically.
  • the selection can also be left to an expert and appropriate evaluations, for example, automatically made available by selected KPIs.
  • the invention relates to a method for Ge ⁇ nerieren skill SKI, SKn one or more production units Ul, Un, wherein the production units Ul, Un by a combination of behavioral patterns Bl, Bn capabilities SKI, SKn form, wherein the production units Ul, Un by application of skills SKI, SKn cause at least one state transition STTL, STTN a tool piece W, wherein a first database DB1 comprises behavior ⁇ pattern Bl, Bn of the production units Ul, Un, wherein a second database DB2 requests REQ1, REQn has the describe one or more state transitions StTl, StTn, which the workpiece W should pass through.
  • a first database DB1 comprises behavior ⁇ pattern Bl, Bn of the production units Ul, Un
  • a second database DB2 requests REQ1
  • REQn has the describe one or more state transitions StTl, StTn, which the workpiece W should pass through.
  • SDn wherein in the simulation data set SD1,... SDn are assigned to the requirements REQ1, REQn for at least one state transition StT1, StTn behavior patterns B1, Bn, S2: simulation of at least one of the simulation data sets SD1,
  • SDn S3 selecting the simulation data records SD1,... SDn whose simulation results SIMRES1, SIMRESn fulfill definable optimization criteria OPT,
  • SDn as at least one capability SKI, SKn in at least one third database DB3.
  • the invention further relates to a simulation system

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Abstract

L'invention concerne un procédé de génération de capacités (SK1,,..., SKn) d'une ou plusieurs unités de production (U1,,..., Un), les unités de production (U1,,..., Un) formant des capacités (SK1,,..., SKn) par la combinaison de modèles de comportement (B1,,..., Bn), les unités de production (U1,,..., Un) entraînant, par l'utilisation de capacités (SK1,,..., SKn), au moins une transition d'état (SK1,..., SKn) d'une pièce à usiner (W). Afin de disposer d'un procédé permettant une génération de capacités efficace et pouvant être automatisée, l'invention propose les étapes suivantes : - la création d'au moins un ensemble de données de simulation (SD1,..., SDn), des modèles de comportement (B1,..., Bn) étant attribués aux exigences (REQ1,..., REQn) pour au moins une transition d'état (ST1,..., STn), dans l'ensemble de données de simulation (SD1,..., SDn) (S1), - la simulation d'au moins un des ensembles de données de simulation (SD1,..., SDn) (S2), - la sélection des ensembles de données de simulation (SD1,... , SDn) dont les résultats de simulation (SIMRES1,..., SIMRESn) satisfont des critères d'optimisation (OPT) pouvant être définis (S3), - l'enregistrement des ensembles de données de simulation (SD1,..., SDn) sélectionnés en tant qu'au moins une capacité (SK1,..., SKn) dans au moins une troisième banque de données (DB3) (S4). L'invention concerne en outre un système de simulation (SIMSYS), une unité de production (U1,... , Un) ainsi qu'un appareil d'automatisation, qui sont utilisé en relation avec le procédé.
PCT/EP2017/073928 2017-09-21 2017-09-21 Génération de capacités d'unités de production pouvant être automatisée WO2019057284A1 (fr)

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WO2021129922A1 (fr) * 2019-12-23 2021-07-01 Siemens Aktiengesellschaft Procédé pour faire fonctionner un système d'automatisation et système d'automatisation
DE102021130676A1 (de) 2021-11-23 2023-05-25 Dmg Mori Digital Gmbh Vorrichtung und Verfahren zur Verarbeitung eines digitalen Zwillings einer Werkzeugmaschine in einer Mehrbenutzerumgebung

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US7460920B1 (en) * 2006-02-22 2008-12-02 Advanced Micro Devices, Inc. Determining scheduling priority using fabrication simulation
US20170031354A1 (en) * 2015-07-29 2017-02-02 General Electric Company Methods, systems, and apparatus for resource allocation in a manufacturing environment

Patent Citations (2)

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US7460920B1 (en) * 2006-02-22 2008-12-02 Advanced Micro Devices, Inc. Determining scheduling priority using fabrication simulation
US20170031354A1 (en) * 2015-07-29 2017-02-02 General Electric Company Methods, systems, and apparatus for resource allocation in a manufacturing environment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021129922A1 (fr) * 2019-12-23 2021-07-01 Siemens Aktiengesellschaft Procédé pour faire fonctionner un système d'automatisation et système d'automatisation
DE102021130676A1 (de) 2021-11-23 2023-05-25 Dmg Mori Digital Gmbh Vorrichtung und Verfahren zur Verarbeitung eines digitalen Zwillings einer Werkzeugmaschine in einer Mehrbenutzerumgebung

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