• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The present disclosure relates to a computer-implemented method for customizing an automated product manufacturing process. It further relates to a robotic manufacturing system for manufacturing a product based on a fabrication model generated by means of the aforementioned computer-implemented method. Related Patent Application This application claims the priority of PCT Pending Application No. PCT/EP2020/060492, filed April. 14, 2020 and entitled “Smart Manufacturing Framework”. Background of the invention Despite significant research efforts spanning more than four decades, the use of robotic manufacturing in global construction has to date seen no large-scale industrial adoption. Due this absence of automation, global construction productivity has remained stagnant over four decades, while other sectors – such as the manufacturing industries, which have seen pervasive penetration of automation technologies – have experienced a quadrupling of productivity in the same timeframe. One central challenge preventing wide-scale adoption is the circumstance that automating construction work requires the close integration of disciplines that have historically worked un-related. The fields required to collaborate are 1) User Experience Design, 2) Computational Design, 3) Manufacturing Engineering; 4) Construction manufacturing; 5) Computer Science; 6) Robotic Engineering. Bridging the knowledge of these domains to establish automatic, file-to-factory workflows requires the development of highly specialized technical knowledge, that remains widely unavailable throughout the traditionally crafts-based, global construction industry. Prior Art Recent developments in construction automation have seen an increasing body of research explore the paradigm of ‘parametric manufacturing’ in which parametric CADmodels of a target product is manufactured through an associated robotic manufacturing unit. This approach enables a partial solution to a second central challenge within construction automation, which is that while production in the manufacturing industry can operate under a mass-manufacturing paradigm of repeated production of identical products, construction manufacturing is project specific, requiring one-off or small-series production runs. The solution offered from parametric manufacturing is to drive a digital production line from a customizable CAD-geometry, which enables flexibility of creating products that are unique within the bounds of predetermined variables. One aspect of this development is the increasing use of Visual Programming environments, such as McNeel Grasshopper or AutoDesk Dynamo to create graph-based parametric models, which relates to the general use of such models for project designs within the practise of the Architectural, Engineering and Construction industry. However, as previously indicated, this approach requires highly specialized knowledge to establish and operate, while simultaneously drawing on a cross-disciplinary domain knowledge, which has so far emerged only within a research context. It further entails the development of a unique specialization of this knowledge to each sub-segment of construction manufacturing. Since the construction industry is segmented into a vast number of niche areas of manufacturing, due to the high degree of variability in design, construction materials used and assembly sequences deployed, this implies a prohibitive overhead in expertly development of such digital manufacturing workflows. Summary of the invention The present disclosure uniquely addresses the above challenge by offering a novel model of knowledge encapsulation, which enables a standardization of digital construction workflows across highly variable and seemingly unrelated applications. Hereby, it significantly reduces the software development effort required to establish a particular digital production workflow; as well as significantly reducing the degree of specialist knowledge required to do so. Specifically, one aspect of the invention is a method of defining and subsequently controlling a digital production workflow by means of an n-layered Directed Acyclic Graph (DAG). It is the finding of the inventors that any digital construction workflow may be abstracted to the following five constituents: a) a parametric product model with associated control variables; b) a parametric fabrication model, which is a derivative of a) and computes toolpaths as a function thereof; c) an execution entity, which comprise a numeric representation of a physical production system and utilize this representation to calculate motion instructions and validations which can be transmitted to d) a cyberphysical production unit, which entail a digitally actuated entity with an associated processing tool, the totality hereof controlled through a computer-implemented control module; and e) a Human Machine Interface, which enables a production user to interact and control the entirety of the system through the means of a Graphical User Interface (GUI). In one further aspect of the invention, the first layer of the n-layered DAG represents these five constituent through five nodes with associated edges. Hereby, a high-level, operational entity is abstracted, which remain invariant throughout a plurality of embodiments of digital construction workflows. The nodes of the first layer are constituted by sub-graphs on a second layer, which holds a non-bound variability of node content, node configuration and edge topology. As a result of this non-bound variance, sub-graphs cannot be directly projected into the nodes of the first layer. Instead, in one further aspect of the disclosure, a novel method of projection is deployed, the method comprising the steps of: a) Appending a constraint to a process routine of the projection b) Extracting an ordered point set from the projected content using functional composition c) Specifying the constraint as a numerical operation on the on the obtained,ordered point set d) Compare the generated values with a pre-decided threshold to evaluate a Boolean output e) Determine the state of the constraint evaluation based on the output Hereby, the validation of sub-graphs to be projected is abstracted, enabling the following functional advantages, which jointly reduce the complexity and workload of establishing custom manufacturing workflows: - The first layer graph can be repeatedly utilized a general control model for any custom workflows, hereby standardizing the workflow establishment process. - The abstracted representation hereby established enables a simplification of the workflow, enabling less specialized users to customize and change workflows - Through the specialization of sub-graphs, a separation of concern is achieved, in which only a single domain of knowledge is required to create each subgraph, as opposed to current methods, in which the generation of one, comprehensive graph requires extensive, multidisciplinary domain expertise. - Through the abstraction of the sub-graph validation, error-checking is simplified. Hereby, implementation and stabilization of new workflows is eased. - Finally, through the standardization implied by the disclosed method, automatic generation of Graphical User Interfaces for custom workflows are enabled, through exposing product model variables within a GUI template. Brief description of drawings. Fig. 1 depicts an embodiment of the presently disclosed method, wherein the method is represented as a sequence of 7 consecutive steps. Fig 2. displays an embodiment of the disclosed methods, in which a sequence of projection between 2 layers of a graph is detailed as 5 consecutive steps. Fig. 3 shows an embodiment the presently disclosed method, wherein the method is represented as a directed acyclic graph (DAG) comprising five main nodes. Fig 4. Display an embodiment of the disclosed method, wherein the method is represented as an n-layered graph, exemplified with 2 layers. Fig. 5. Display a structure of a function composition node according to an embodiment of the present disclosure. Fig. 6. Shows a workflow of matrix transformations according to an embodiment of the disclosure. Fig. 7. Display one embodiment of a constraint checking node according to an embodiment of the disclosure. Fig. 8. Shows an example Projected Product Model sub-graph for a hemisphere product according to an embodiment of the disclosure. Fig. 9 Displays an example Projected Fabrication Model sub-graph related to fig. 8 according to an embodiment of the disclosure. Fig. 10. Display the geometric output of the Projected Fabrication Model sub-graph of fig. 8 according to an embodiment of the disclosure. Fig. 11. Displays an embodiment of a Robotic Production Unit, created to support the manufacturing of the product types produced by fig. 8. Fig. 12. Shows an example Projected Product Model sub-graph for a helical product according to an embodiment of the disclosure. Fig. 13 Displays an example Projected Fabrication Model sub-graph related to fig. 12 according to an embodiment of the disclosure. Fig. 14. Display the geometric output of the Projected Fabrication Model sub-graph of fig. 12 according to an embodiment of the disclosure. Fig. 15. Displays an embodiment of a Robotic Production Unit, created to support the manufacturing of the product types produced by fig.12.
    公开号:DK202001160A1
    申请号:DKP202001160
    申请日:2020-10-11
    公开日:2021-11-16
    发明作者:Neythalath Narendrakrishnan；Søndergaard Asbjørn
    申请人:Odico As；
    IPC主号:
    专利说明:

    DK 2020 01160 A1 q Graph-based control model for digital manufacturing Detailed description of the invention In the present disclosure, a robotic application is expressed with the help of a n-layered graph for abstraction (ref.
    Fig. 4). Each layer of the graph differs in the level of abstraction.
    The graph in layer 1 has an invariant set of nodes which can be used to completely define various robotic applications.
    Due to the invariant nature, it is suitable for a low skilled production user to work with it.
    The production worker will be able to manipulate the parameters to achieve a specific valid product configuration, and further instruct the robotic system to manufacture the product confirming to the specified parameter values.
    The details of the graphs present in both the layers are given below Layer 1 Layer 1 constitute a meta-graph, which contain a set of 5 nodes viz.
    Product Model (PM), Fabrication Model (FM), ExecutionNode (EN), Physical Robot Cell (PRC) and a Human Machine Interface (IN). The topology of this layer may be given by a Steering Model (SM) as shown in Fig.3. Depending on the current state of the application, a particular route gets active.
    A route is an event handling pipeline comprising of a subset of nodes and connections in the SM.
    For example, when a production user is initiating a request to fabricate a piece to the robot, the active route is indicated in bold At any given time, there will always be only one active route.
    During the fabrication process, if the robot encounters an error, the current route will be deactivated, and an error handling route defined by the system developer will be activated.
    This route may be defined by another ordered subset of nodes from the SM.
    Novice users who aren't proficient in robotic programming can easily some new event handling routes by connecting nodes in Layer 1 and 2. Layer 2 There is a projection associated with each one of the nodes in layer 1. Projections are DAGs used for the purpose of computing the output of each of these nodes.
    Single domain expert user knowledge is expected to model projections.
    For example, a Computational Design Specialist (CDS) is required to provide the projections for the Product Model node.
    The Fabrication Model node will be modelled by a manufacturing engineer or Computer Aided Manufacturing (CAM) specialist, whereas a robotic engineer is required for modelling the Execution Node.
    Every projection has a predefined input/output type.
    Since relationships between the output and input can rarely be described within a single function, it is expressed using function composition (fig. 5). Function composition is an operation that takes two functions, f and g, and produces a function h such that h(x) = g(ff(x)). In this operation, 40 the function g is applied to the result of applying the function f to x.
    That is, the functions 7: X7—Y and g: Y7—Z are composed to yield a function that maps x in X to g(f(x)) in Z.
    In most cases, all the contributing functions will be invertible which will allow us to back calculate the inputs to the projection given the corresponding output. 45 Projections Projections involve a special set of nodes called constraint nodes.
    Formally, a constraint can be defined by two quantities:
    DK 2020 01160 A1 2
    1.X=X7,…,Xn is a set of variables,
    2.D=D1,…,Dn is a set of their respective domains. Each variable, Xi, can take on the values from its respective non-empty do-main Di. Every constraint C is in turn a pair (zR) , where tcX consisting of k variables and R is a k-ary relation on the corresponding subset of domains. R should evaluate to true or false. An evaluation of a projection is said to be valid, if and only if all the constraints attached to the projection returns true. The sensitivity to a change in particular input can also be calculated. The details of projections associated with each of the nodes in layer 1 is provided below: Projection for PM (PPM) Input type: Numeric or boolean parameters Output type: A Closed NURBS model. The NURBS model can easily be converted to a matrix with dimensions AxB which corresponds to the topology of B points belonging to R" Example constraint:
    1. Are the parameters within the specified bounds It is the parametric model of the product expressed in the form of a Directed Acyclic Graph. Cycles are prohibited. Each node contains an operation, whereas the edges denote the relationship between any two nodes. The number of parameters determine the dimensionality of the configuration space. The production user can change the value of parameters, to obtain a different configuration. PPM aids the user to visualize the final product and follows a What You See Is What You Get (WYSIWYG) methodology. It abstracts away the details related to the underlying manufacturing process.
    Projection for FM (PFM) Input type: Closed NURBS model, Tool profile curve, cutting speed Output type: Tool trajectory in Cartesian space which is expressed as a matrix with dimensions CxD which corresponds to the topology of D points belonging to r¢ Example constraints:
    1. Does the surfaces satisfy required topology
    2. Does the tool profile curve fit the isocurves along the cutting direction As the PPM, it also is a parametric model expressed in the form of a DAG. There might be one or more associated PFMs depending on the number of robotic processes required to arrive at the final product. For example, it may be a requirement to first perform a cutting operation using a hot or abrasive wire on the work piece followed by a coating operation. The PFM is responsible for the generation of tool trajectory required for physically realizing a given process on a work piece. Unlike the PPM, it is very much tied to the set of underlying manufacturing processes. The sequence in 40 which these processes are executed may or may not be important. Though PFM is tied to a process, it is robot agnostic. Hence, the output of PFM is a tool trajectory expressed in Cartesian space which belongs to RC.
    Projection for EN (PEN) 45 Input type: Task Output type: Joint space trajectory which is expressed as a matrix with dimensions ExF which corresponds to the topology of F points belonging to RF. Example Constraints:
    1. Is the task within the reachable workspace of the robot system
    2. Does the task satisfy velocity and acceleration bounds of the robot system
    DK 2020 01160 A1 3
    3. Does the task satisfy all the dynamic constraints of the robot system
    4. Has the tool been calibrated EN receives the task from FM and passes onto PEN for computation of joint trajectories. A task comprises of a tool trajectory along with other specifications including Robot Model (RM), Environment Model (EM), Tool Model (TM) and Operation Sequence (OS). From the task, corresponding trajectories in the joint space of the robot/machine is computed respecting various constraint nodes. Projection for PRC (PPRC) Input type: Joint trajectory Output type: Example physical motion Constraints:
    1. Is the system in safe state to execute
    2. Is material placed properly on the worktable PRC consumes the joint trajectories received from EN and sends it to PPRC for further action. PPRC performs the operation in the physical world. Projection for IN (PIN) Input type: Human gestures Output type: Geometry, Viewport, System or I/O actions Example constraints:
    1. Are the gestures valid PIN enables the user to interact with the system in an intuitive manner. Two main functions of PIN are:1. Visualize data 2. Initiate actions. There are 4 types of actions that are relevant:1. Geometry actions — Produces numeric or boolean parameters to be passed onto PM.
    2. Viewport actions — Moves camera, updates mesh or insert help texts. The result of these actions is visible in the viewport.
    3. System actions — Permits the user to directly issue commands to EN/PRC.
    4. |/O actions — Allows the user to perform save and load operations.
    Examples The generality of the disclosed method is exemplified using two principal embodiments of robotic applications. Application 1: Additive manufacturing In this example, the target product is a thin-shell hemisphere. The corresponding PPM and PFM are given by Fig. 8 and Fig. 9. A serial chain robotic arm fitted with a 3D printing nozzle head constitute the robot cell. PEN, PPRC and PIN are not shown for brevity. Majorly, PEN will have a motion planning node which will decompose the tool 40 trajectory to collision safe motion of the robot actuators. The kinematic model of the robot will be passed in as a parameter to this node. The governing equations of the geometry are: x = Rsin(0)costø) 45 y = Rsin(0)sin(ø) z= Rcos(0) Application 2: Bending The product is in the form of a coil or spring with a rectangular cross section as shown 50 in Fig. 14. The PPM for this application can be obtained by slightly modifying the PPM of Application 1, validating the reusability aspect of the framework. The governing equations of the geometry are:
    DK 2020 01160 A1 4 x = Rsin(ø) y = Rcostø)
    ZF Detailed description of the drawings Fig.1 display an embodiment of the presently disclosed method, entailing 7 steps required to represent a robotic application as a bi-layered graph. The entire application logic is abstracted into 5 groups of nodes viz. Product Model (PM), Fabrication Model (FM), Execution Node (EN), Physical Robot Cell (PRC) and Human Machine Interface (IN). Based on this, projections are created for each of the 5 nodes by an expert user. Each projection will have a pre- processor as well as a post- processor section. Constraints can either be attached to pre-processing or post- processing units of the projection. If added to the pre-processing stage, they check whether the provided inputs to the projection are valid or not. On the other hand, if they are added to the post-processing unit, they will validate the generated outputs of the projection for correctness. To program the system, an event needs to be defined. For each of the declared events an event handling pipeline needs to be defined and assigned. An event handling pipeline can be created from a subset of nodes in the layer 1 of bi-layered graph. After creating such a pipeline, it needs to be associated with an event.
    Fig. 2 illustrates an embodiment of presently disclosed methods entailing the steps involved in the constraint checking procedure for a bi-layered graph representation of the system. Constraints can either be attached to pre-processing or post-processing units of the projection. The projection will generate an ordered point set of the geometry. A constraint has two parts: (1) Applying a numerical, optionally a Point Cloud Analysis (PCA) algorithm on the provided ordered point set. This will ideally produce a floating- point number as a result. (2) Comparing the result from (1) with a threshold to check whether the constraint has been violated. In case of a violation, (2) will output false. Otherwise, this step will generate a true.
    Fig. 3 shows an embodiment the presently disclosed method, wherein the method is represented as a directed acyclic graph (DAG) comprising five main nodes. The five main nodes represent the product model (PM) 301, the fabrication model (FM) 302, the execution node (EN) 303, the display unit model 304, and the production system 305, respectively.
    40 The nodes are connected by directed edges that represent data transfer between the nodes. Each of the five main nodes are further internally represented as two nodes, wherein a first node corresponds to an initial state of the model, and a second node (marked by an apostrophe) corresponds to a future state of the model. Thus, an initial state of the product model is denoted X, and a future state of the product model is 45 denoted X'. An initial state of the fabrication model is denoted F, and a future state of the fabrication model is denoted F’. An initial state of the Execution Node model is denoted P, and a future state of the planning model is denoted P’. An initial state of the production system is denoted Y, and a future state of the production system is denoted Y’. An initial state of the display unit model is denoted D, and a future state of the 50 display unit model is denoted D’. A human may interact with the display unit model via a GUI. The display unit model may comprise a model-view-controller (MVC), said model-viewcontroller preferably configured to enable a user to view a 3D rendering of
    DK 2020 01160 A1 the product defined by the product model. The 3D rendering of the product may be generated by transmitting a mesh translation of the product to a render node in the display unit model. The product model may be transmitted to the render node such that the GUI displays the product instance immediately after any user-induced changes to 5 the product model. The model-view-controller may be configured to inquire or receive the admissibility status of the targets from the high-level planner. The model-view- controller may be configured to display in the GUI an alert and/or a message to the user based on said status. The model-view-controller may be configured to inquire any changes to the fabrication geometry defined in the fabrication model, and transmit any of said changes to the render node if applicable. Using the GUI, the user may perform changes to the product model, thereby bringing the product model in a new state X”. The change of the product model from the state X to X' may preferably cause the fabrication model F to be updated to a new state F’ reflecting the changes to the product model. The updated fabrication model F” may be transmitted to a planning model P’, which may perform a number of planning operations such as determining the motion plan based on a received tool path and/or received targets from the fabrication model. The execution node may preferably be configured to generate machine instructions that may be executed by the production system upon receipt. The machine instructions may be transmitted to the production system, which is indicated by the edge from P’ to Y”. The production system may manufacture the product defined in the product model X' upon reception of the machine instructions. Fig. 4 display an embodiment of the presently disclosed method wherein an application is described via an n-layered graph, exemplified with 2 layers. Layer 1 abstracts the entire robotic application into a set of 5 nodes viz. Human Machine Interface (IN), Product Model (PM), Fabrication Model (FM), Execution Node (EN) and Physical Robot Cell (PRC) according to the structure detailed in fig. 3. An event handling route is shown in bold connecting the above 5 nodes. This event handling route pertains to the event - initiation of fabrication. Corresponding to each of the 5 nodes, there is a projection which encapsulates the associated computation with each of the node. In the above figure, such a projection (PPM) associated with PM is shown. Projections are on layer 2. Nodes marked | and O denote the input and output to the projection respectively.
    Fig. 5 depicts the structure of a functional composition used to model a projection in the bi-layered graph representation of the system. The function f accepts the inputs I; and L, performs a computation, and outputs the result (0,) which is subsequently passed on to the function g. The input of gis and is mapped to 0,. After receiving the input, g performs some calculations and produces the result (0).
    40 Fig.6 illustrates one embodiment of the disclosure relating to the transformations happening across the projections of the nodes FM and EN of the bi-layered graph. FM accepts the inputs as a set of parameters which is denoted as a N dimensional vector given by the components P; to Pv. After receiving the inputs, FM computes the tool path 45 for a given process. The tool path is represented as a matrix with the dimensions 6 x n. Each column of the matrix denotes a particular pose of the tool. To finish a process, tool has to follow a set of n such poses. On obtaining the tool path as an ordered point set, EN outputs the path in joint space following an inverse kinematics (IK) process. This output is represented as a matrix with the dimensions a x n. The number a stands for the 50 degree of freedom of the robot. Fig. 7 shows one embodiment of the disclosure relating to the constraint checking process wherein a node accepts two inputs I; and 32. These inputs correspond to ordered
    DK 2020 01160 A1 6 point sets. Additionally, it accepts as parameter a point cloud analysis (PCA) algorithm. The provided PCA algorithm is applied on /7 and I2 producing a numeric value as output which is passed onto another node which compares it against a threshold value. A typical comparison is less than or equal to operation. Based on the result of this operation, the output is set to true or false. Fig. 8 shows the PPM of a hemisphere. It is created by cutting a sphere into two halves by a trimming plane. Radius of Hemisphere (R), Angle 1 (theta) and Angle 2 (phi) are the parameters of the hemisphere.
    Fig. 9 illustrates PFM of an additive manufacturing process for a hemisphere. It accepts a PPM of the hemisphere as an input. It outputs the validated toolpath as an ordered point set which can be passed onto EN for further execution. The validations are done by Task space Constraint Checker node.
    Fig. 10 illustrates the generated tool path by PFM for the additive manufacturing process of a hemisphere. The arrows in the diagram indicates the direction of the tool path. Fig. 11 displays an embodiment of a physical robotic production system, suitable to execute the disclosed method. A 6-axis industrial manipulator is equipped with an additive manufacturing end-effector and positioned in front of a work table. By execution of the toolpath derived from FM and under control of the Execution Node, the robotic system is able to produce the customized instance of the product from the Product Model Fig. 12 depicts the PPM for a helical spring with rectangular cross section. It is generated by sweeping a rectangular cross section along a spiral rail. Radius of Helix (R) and Angle (phi) are the configurable parameters of the model. Fig. 13 shows the PFM of the bending process. Pitch of the Helix (P) and Radius of Helix (R) are the parameters of the model. It outputs the desired pose for the robot end effector to the EN. Fig. 14 depicts the bending process for a helical spring. It shows the two cross section profiles CS 1 and CS 2 with their corresponding normals Ny and N> making an angle alpha with each other. Fig. 15 displays a further embodiment of a physical robotic production system, suitable to execute the disclosed method. A 6-axis industrial manipulator is equipped with an actuated gripping end-effector and positioned in front of a linear pipe extruder. By 40 execution of the toolpath derived from FM, the gripper is moved to a position, which upon initiation of the linear extruder will bend the extruded pipe into the desired angle. Through continued motion of the extruder, the pipe will revolve into a helical shape.
    权利要求:
    Claims (9)
    [1] DK 2020 01160 A1 1 Graph-based control model for digital manufacturing Claims:
    [1] A computer-implemented method of controlling a digital production workflow, the method comprising the steps of: a) Defining a data-structure consisting of a n-layered, Directed Acyclic Graph, in which: b) A first layer of the n-layered graph comprising a finite and invariant number of higher-level nodes, the edges between which represent pre-structured topology c) A second layer comprising a set of variable subgraphs d) Projecting the content of variable sub-graphs into one or more nodes of the first layer graph e) Through this projection transferring computed data between the nodes of the first graph along the topology of the connecting edges.
    [2] [2] The method according to claim [1], in which the projection between the 2 layers of the graph comprise the steps of: a) Appending a constraint to a pre-process or post-process routine of a projection b) Extract an ordered point set from the projected content using functional composition c) Specify the constraint as a numerical operation on the obtained ordered on the obtained, ordered point set d) Compare the generated values with a pre-decided threshold to evaluate a Boolean output e) Determine the state of the constraint evaluation based on the output
    [3] [3] the method according to [2], in which the numerical operation used to specify the constraint constitute a Point Cloud Analysis operation
    [4] [4] the method according to [1-3] in which the first layer graph is constituted by 5 nodes, entailing the following, higher-level functions: a) A first node representing a Human Machine Interface b) A second node representing a parametric product model c) A third node representing a parametric fabrication model as derivative of the product model d) A fourth node comprising a numeric representation of a digital production system e) A fifth node representing a digitally controlled, physical production system 40 [5] the method according to [1-4] in which the invention is utilized to control a robotic manufacturing process
    [5] DK 2020 01160 A1 2
    [6] [6] the method according to [1-5] in which the invention is utilized within a software framework for robotic control
    [7] [7] the method according to any preceding claims, in which the invention is used to partially auto-generate Human Machine User Interface configurations.
    [8] [8] the method according to claim [7] in which the interface is expressed using a mobile computing device.
    [9] [9] the method according to any of the preceding claims in which the invention is used for robotic construction manufacturing.
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    同族专利:
    公开号 | 公开日
    EP3953849A2|2022-02-16|
    WO2020208267A3|2020-11-19|
    WO2020208267A2|2020-10-15|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    DK3429810T3|2016-03-14|2020-08-17|Univ Danmarks Tekniske|Robotic system and method of manufacturing objects|
    法律状态:
    2021-11-16| PAT| Application published|Effective date: 20211015 |
    优先权:
    申请号 | 申请日 | 专利标题
    EP19168682|2019-04-11|
    PCT/EP2020/060492|WO2020208267A2|2019-04-11|2020-04-14|Smart manufacturing framework|
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