Integrated control frame for self-adaptive assembly of micro parts and design method of integrated control frame

Abstract

The invention relates to an integrated control framework for self-adaptive assembly of micro parts and a design method of the integrated control framework. The design method comprises a self-defined heterogeneous equipment information model, an assembly robot, an assembly platform, a collaborative robot OPC UA server and an OPC UA client of integrated heterogeneous equipment. The OPC UA client comprises data and method interfaces provided by the equipment server, the assembly sequence is sorted according to a heuristic algorithm, and then automatic assembly is carried out by using an automatic assembly algorithm based on steps. Assembling quality and information are transmitted to the client side through the server in real time and displayed in the client side, the assembling state is detected according to the information, faults are predicted, and intelligentization and automation of the assembling process and whole-course tracking of the assembling quality are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an integrated control framework for adaptive assembly of tiny parts and a design method thereof. Background Art

[0002] With the development of science and technology, people's performance requirements for electromechanical products are getting higher and higher. Assembly is an important part of electromechanical product production. With the miniaturization, micro-miniaturization and precision of assembly devices, the assembly and integration of tiny parts with different manufacturing processes through a certain connection method to ensure the quality and accuracy of their assembly requires micro-assembly technology and micro-assembly systems. Many scholars and researchers have conducted in-depth research on this. For example, Xu Zheng and others from Dalian University of Technology used a visual system for human-computer interaction and realized the assembly of a micro-flexure swing system through a look-before-move mode. Cao Yang and others established a parallel robot digital R&D software platform, combined with digital twins to complete the full-cycle management and maintenance of the R&D and operation process. Huang Xiang and others from Huazhong University of Science and Technology designed control software to address the problem of low assembly accuracy and efficiency of sub-millimeter micro parts, and proposed a general "four-layer architecture" design model based on the .NET architecture, which greatly improved assembly accuracy and efficiency.

[0003] These research results have laid the foundation for the realization of automatic assembly of micro devices, but there are still the following problems in the realization of automatic assembly of multi-variety and small-batch micro devices: 1. Existing automatic / semi-automatic assembly systems for micro devices can often only realize the automatic assembly of one type of micro device or a certain assembly process, lacking automatic assembly processes for variable products, and the efficiency of variable product assembly is low.

[0004] 2. Lack of a standardized control framework for micro-assembly systems with high versatility.

[0005] 3. There is a lack of reference process for establishing interconnection and interoperability between micro-assembly equipment.

[0006] Therefore, the existing micro-assembly system needs to be matched with an integrated control method that can complete the high-precision, flexible automatic assembly of micro devices. This control method should allow the micro-assembly system to freely add and delete modules and functions, and the functions of the original assembly system should not be lost when adding or updating new equipment. The overall assembly system should be created with a modular concept and data communication between modules should be guaranteed to improve the applicability of the micro-assembly system to different tiny products. Summary of the invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to provide an integrated control framework for adaptive assembly of tiny parts and a design method thereof, which is conducive to the interconnection and unified management of information of various heterogeneous equipment, and adapts to multi-variety and variable batch assembly production operations.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: An integrated control framework for adaptive assembly of tiny parts mainly includes the following four layers: Micro-assembly equipment layer: mainly includes a set of micro-parts assembly systems developed by the team, including three main modules such as micro-assembly robots, micro-assembly platforms, and micro-assembly collaborative robots. The main modules include corresponding actuators with multiple degrees of freedom. The methods of communication between the equipment and the host are different. During the debugging stage, different personnel program and control them in the sub-host computer. Therefore, after the team has developed their own modules, it is necessary to design a total host computer control system compatible with multiple modules to complete the automatic call of each module. This modular development feature enables the entire assembly system to quickly expand new modules when adding and deleting modules due to assembly products and assembly process adjustments in the future, without affecting the functions of the existing modules. In addition, it also includes four industrial cameras, loading platforms, unloading platforms, clamp libraries, tool change libraries, parts libraries, slide rails and other auxiliary modules. The auxiliary modules that need to be controlled use STM32 microcontrollers to connect to the host computers of the main modules that are close to them through the serial port RS232. As part of the main module, they are not divided into modules separately.

[0009] Data transmission layer: OPC UA communication protocol is mainly used to complete the communication between each module and the overall host computer, including OPC UA server and client. In the above-mentioned sub-host computer unit of the micro-assembly device layer, according to the fixed attributes given by the underlying device manufacturer and the dynamic data returned during operation, a tree diagram information modeling method proposed in this paper is used to create information models and sub-servers for the sub-modules. Based on this method, the nodes in the information model in the server are bound to the data of the underlying device. Changing the node data or calling the method can directly act on the underlying device actuator. In the overall host computer, a client that can connect to the server needs to be created. The OPCUA communication protocol allows trusted clients to complete various types of operations on the nodes in the server by accessing the information model in the server, using method calls, variable reading and writing, subscription and publishing, and other methods, thereby completing the control and data monitoring of the underlying device actuator.

[0010] Control system layer: It is built in a master control computer with good performance and is used to monitor and control all micro-assembly equipment. The main body is the assembly system integrated control software, which includes the client in the data transmission layer, the assembly control module, and the data processing module. First, the control system software allocates memory in the master control computer for the data obtained after the client connects to the server. During operation, the memory can be read through the human-computer interaction interface to display the current equipment data or send control instructions to the operator. The assembly control module is the basis for the automatic assembly of tiny parts, including sequence planning before assembly, process generation, automatic execution during assembly, process monitoring, quality monitoring after assembly and other functions, to realize the intelligent and automated assembly operation of the whole process. The data processing module is mainly used to deal with possible faults during the assembly process and store log information and underlying equipment operation data during software operation, etc., to assist operators in inspecting and repairing equipment.

[0011] Digital twin layer: The client in the control system layer obtains memory information as information, and establishes a server that aggregates multiple device modules. This server can provide device data and control methods for other applications. The assembly system has intelligent control functions. Because the newly developed mechanical system can establish a digital twin and achieve virtual and real integration, the present invention uses Unity to complete the creation of digital twins. Unity is based on .NET framework programming and allows the use of TCP communication, so the server / client mode based on OPC UA is used to open up the data connection between the underlying physical entity device and the virtual twin of the software layer. Under normal circumstances, the connection between the server and the client of OPC UA requires that both are in the same local area network. For example, all the above connections are carried out in the same local area network, but in order to fully understand the product assembly status when the operator and the developer are not in the factory field, it can also be based on Web applications and use Internet transmission methods. Under the premise of ensuring safety, it can be integrated into the production management system (Manufacturing Execution System, MES) and enterprise resource planning (Enterprise Resource Planning, ERP), so that the automatic assembly system and the MES system can interact with each other.

[0012] The implementation process of the above four-layer architecture integrated control framework is as follows: S1. An OPC UA server is built into the sub-host computer of each device to determine the unstructured data generated by the device during the production process of the micro-parts assembly system, classify the data and build the corresponding OPC UA information model; S2. Instantiate the information model of the corresponding equipment in the assembly system and configure it in the OPC UA server, bind the assembly system equipment data to the corresponding nodes in the OPC UA information model, and complete the mapping from the physical model to the data model; S3. Use the OPC UA client to access the information model in the OPC UA server in the sub-host computer. By querying the nodes in the information model, the underlying equipment data of the assembly system is monitored in real time to understand the system operation status.

[0013] S4. Add assembly sequence planning, step-based automated assembly process, and assembly quality monitoring method to the control interface to make the entire assembly process intelligent, automated, and efficient.

[0014] S5. Integrate the OPC UA client on the control interface of the master control computer to display the monitoring information in real time; retain the data, and predict the stability of the equipment during operation based on the relevant data according to the effective BP neural network.

[0015] S6. Based on the extensibility of OPC UA, the control interface in the master control computer can also be integrated with the OPC UA aggregation server as a transit point for data transmission. Through the cross-platform compatibility of OPC UA, control systems under different platforms can be developed, or the client can be embedded in the digital twin to complete data transmission and reception, thereby achieving a combination of virtual and real.

[0016] Furthermore, the unstructured data described in step S1 specifically includes the position and angle information of each axis in the five-degree-of-freedom assembly robot, collaborative robot arm and assembly platform, movement speed, motor temperature, and images and sensor signals for visual servoing and position detection, as well as voltage information of piezoelectric ceramics in the micro-motion platform. Data classification can be understood as dividing the unstructured data of mechanical equipment into static data sets and dynamic data sets; static data sets store the inherent information of the equipment, including the manufacturer, product number, and equipment size; dynamic data sets store the constantly changing information of the equipment during the production process, including equipment operation mode, power, and actuator operation status, and abstract the common data of the equipment into classes, and then instantiate the classes into objects according to actual requirements, and store the created objects in the address space according to a specific structural hierarchy to generate an information model.

[0017] Furthermore, step S2 describes building specific OPC UA information models through different devices, exposing the interface for collecting data from the device host computer directly to the corresponding node in the information model, completing the mapping of device physical data to server information model data, and binding the data in the nodes of the information model in real time, which is convenient for subsequent client and aggregation server subscription and reading and writing.

[0018] Further, step S3 specifically includes: embedding the OPC UA client in the control interface of the integrated system in the master control computer, connecting the OPC UA servers of all underlying devices, thereby obtaining the physical data information exposed by each device through the information model, placing the information to be monitored on the control interface, and completing the monitoring of the entire assembly system. The data of each device is finally integrated in the master control computer to achieve interconnection between devices.

[0019] Further, step S4 specifically includes that the assembly sequence planning algorithm developed by the control interface is an improved flower pollination algorithm, which converts the problem-solving process into a process that simulates the pollination of flowers of flowering plants in nature. It is widely used in optimization problems and has the characteristics of fast convergence speed. According to the needs of sequence planning, the assembly relationship parameters of the products to be assembled are selected as parameters in the solution process, including the priority relationship between parts, stability, assembly direction and tool transformation. These parameters will affect the calculation of the cost function, and the cost includes assembly time, connection stability, direction and number of tool transformations, etc. The minimum cost is obtained through iteration, and the corresponding sequence is the optimal assembly sequence.

[0020] Based on the step-based automated assembly process, when assembling a product for the first time, each module is first debugged using a human-computer interaction method according to the process. The process parameters and calculation data obtained during the debugging process are saved in the micro-device assembly database, so that each process has corresponding parameters and equipment method calls. In the subsequent assembly of the product, there is no need for manual debugging. The corresponding equipment method is directly called according to the process, and a process detection module is added to check the assembly quality of each process. For steps that do not meet the standards, the parameters are readjusted or the operator is notified to intervene, completing the automated assembly with feedback function.

[0021] Further, step S5 specifically includes that the prediction of equipment stability requires the OPC UA client to collect historical data for a period of time into a database to form a training set, and after training, the real-time collected data is put into a BP neural network for calculation to predict the stability of the equipment in operation.

[0022] For step S6, the specific implementation is based on the rich semantics of the OPC UA information model. After the unified information model is created in steps 1-5, data transmission becomes simple. As an extension of the control system, the creation of a digital twin can perform virtual assembly simulation, verify the rationality of planning results such as assembly sequence, assembly path planning, motion interference, etc., and play a guiding role in assembly work. Similarly, the developed system should be easy to integrate. The integrated control framework is production-oriented and should be able to be integrated with its own existing MES system to provide the data required for its product quality management, material management, and production process management.

[0023] In general, the present invention has the following advantages: 1. Based on the OPC UA protocol, a modular equipment information integration framework for assembly systems is proposed, which is conducive to the interconnection and unified management of information of various heterogeneous equipment.

[0024] 2. Develop a reconfigurable information model node editor, embedded in the device server, and use a tree diagram modeling method to simplify the OPC UA information model creation process, which is convenient for operators to use.

[0025] 3. An information integration method for industrial assembly equipment is proposed. While the modules are highly correlated, the data information is decoupled and the information is independent of each other. There is no need to change the overall framework when replacing modules, which can adapt to multi-variety and variable batch assembly production operations.

[0026] 4. An automatic assembly method is proposed. The integrated system provides an assembly method that automatically runs the entire process from assembly sequence, assembly process, assembly instructions, assembly equipment operation, assembly quality inspection, etc., to achieve repeated assembly and diversified assembly.

[0027] 5. A visualized digital twin based on the assembly process was designed. The assembly process was realized through the digital twin, which provided macro-coarse positioning coordinates before physical assembly, detected interference, and monitored the operation of equipment during assembly.

[0028] (1) Compared with traditional assembly, micro-assembly usually requires multiple modules and devices to collaborate to complete the assembly process in order to meet high-precision requirements. The present invention divides various main devices into modules according to the functions of the micro-assembly system. The modules are independent of each other, have no obvious coupling, and are fixed in the system software framework. When developers have the need to update software and hardware, they only need to update the code of the module that controls the hardware without changing the code of other parts, making the code clearer and easier to optimize, and able to quickly respond to hardware updates and changes in market demand. In order to meet the requirements of module integrated control, each sub-module creates a corresponding OPC UA information model and server, and the control system is embedded in the client to complete the communication with the sub-module through the OPC UA communication protocol. The integration and informatization level of micro-parts assembly equipment is improved.

[0029] (2) A set of micro-parts automatic assembly control software was developed to achieve the integration of various parts. This system software can realize high-precision assembly of various complex micro-devices, solving the automation and flexibility problems of micro-device integrated assembly control. After the assembly debugging is completed, this control system software can save the assembly path, assembly sequence and assembly process parameters through the database to achieve automatic repeated assembly of different micro-devices. This software has good human-computer interaction. It greatly improves the assembly efficiency, has good stability, runs without abnormalities, and improves the flexibility and automation level of micro-parts assembly equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of an integrated control method for a tiny parts adaptive assembly system based on OPC UA.

[0031] Figure 2 A schematic diagram of the device OPC UA information model and server creation software in the present invention.

[0032] Figure 3 The figure is a schematic diagram of the information model of the robot module assembly according to the embodiment.

[0033] Figure 4 Flowchart for creating an OPC UA node for the present invention.

[0034] Figure 5 The following is a flowchart of automatic assembly of an embodiment. DETAILED DESCRIPTION

[0035] The present invention will be described in further detail below.

[0036] Example 1 This example provides a method for modeling tree diagram information.

[0037] First, the OPC UA information model has eight basic node types, including object type node, object node, variable type node, variable node, data type node, method node, reference type node, and view node. For this application, reference type nodes and view nodes use the nodes included in the SDK and are not created separately. The remaining six nodes need to be created according to needs.

[0038] Secondly, it is necessary to determine the data that the upper computer of the assembly system can get from each underlying device. According to the type, it is divided into static data, dynamic data and calling methods, and the information model is drawn using a legend. Since the information model has object-oriented functions, when creating the information model, it is necessary to first abstract the specific objects into classes with common characteristics, and then reference and instantiate the classes to create new objects. For separate special objects that are not universal, objects can be created directly to reduce the complexity. This operation has good adaptability and extensibility when managing information models.

[0039] Then, according to the information model drawn in the legend, the static data is supplemented with text. The static data includes the manufacturer of the equipment, product number, key dimensions, working space, working conditions and other inherent attributes set at the factory, which will not change when the equipment is started. The dynamic data is mapped with the physical entity. The dynamic data includes the data that can be read by the data interface based on the gateway, Ethernet and other reprogrammable control that the manufacturer exposes to the customer when the equipment leaves the factory. For example, the data packet sent back to the host computer by the device through the RS485, RS232 and other serial ports is parsed through the protocol supported by the device to complete the reading of the parsed packet data, so as to bind it to the dynamic data node and complete real-time monitoring.

[0040] Then, for the method of controlling the device in the host computer, a corresponding Bool type identifier is provided to represent it, and the default value is False. A new thread is opened in the host computer to monitor whether the identifier changes. When the identifier changes from False to True, the method is called. In the information model, the identifier is bound to the input variable in the corresponding method node. In the OPC UA server and client, the device method can be called by calling the method node and changing the value of the input variable.

[0041] The data supplemented by the above text belongs to structured data, which can be supplemented by a database. Using the device number as the keyword, the remaining keys can be derived, effectively reducing the programming pressure. Since the data in the above mapping is only generated at runtime and has high real-time requirements, the upper computer stores the read data in a global variable, and then the dynamic data node in the information model creates a pointer to the global variable address relative to it. When the global variable changes by reading serial port data, the dynamic data node can also undergo the same change because it points to the same storage space.

[0042] The above data supplement and binding method is implemented using a self-developed demo based on the cross-platform software development framework QT programming, as shown in the attached Figure 2The information model creation framework includes the add type module, which is used to encapsulate the objects and variable types required for device modeling; the node creation module, which is used to create corresponding variables, objects, collections, methods and event nodes in sequence according to the drawn information model; the data source binding module, in which the static variable node is bound to the data in the database, the dynamic data node is bound to the data source given by the device, and the method variable node is bound to the identifier given by the device; the node tree module, which displays the created node installation references and hierarchical relationships in a tree diagram, similar to the address space in UaExpert, and allows users to instantiate similar objects and variables by copying nodes in the node tree, reducing the workload of creating nodes. Finally, the server creation module creates an address space, assigns a unique NoldId to the node, which can be set as a keyword, and saves the node tree in the form of a database, which is equivalent to saving the information model. When the software is opened later, the server can be quickly deployed by directly loading the database without creating the information model again.

[0043] After the OPC UA server forms the address space, the OPC UA server can add monitoring, events, historical data and other functions based on user needs. After the OPC UA server is turned on, any client with the server security policy can access the address space in the server, and then access the data in the information model through the nodes in the address space, so that the client can obtain the data and information of the device embedded in the server's host computer, and realize the information communication between the master control and each device.

[0044] Finally, based on the interfaces provided to the client in the OPC UA protocol, such as reading and writing node values, subscribing to monitoring items, calling functions, etc., the assembly system control interface is created in the master control computer based on QT, which includes visually viewing device information, changing the underlying device dynamic data, and encapsulating the underlying device methods into controls. This completes the construction of the communication module between each device and the master control computer.

[0045] Example 2 This embodiment takes the three-dimensional acceleration sensor assembly system as the main research object. The assembly system is mainly composed of a five-degree-of-freedom assembly robot (Assembly Robot), a six-axis collaborative robot (Co-bot), a nine-degree-of-freedom redundant assembly platform (AssemblyPlate), an industrial camera group (Vision A) in the clamping parts area, a platform macro-micro combined industrial camera group (VisionB), and a slide rail (Slide rail). To achieve precision assembly, each module can be decomposed into multiple units. For example, the assembly robot can be divided into three linear motors produced by NATORS, one rotary motor, and a domestic brand servo rotary motor used to increase the load to form the five degrees of freedom of the assembly robot. The end of the assembly robot is a variety of constant force grippers developed in the laboratory, which are used to clamp different types of parts for assembly. In the process of replacing the gripper and clamping the parts, two telecentric industrial cameras, namely VisionA, are used for visual servoing. The collaborative robot is a mature six-axis robot purchased, but in order to complete the collaborative function, tool changing operations and collision detection functions are designed. The assembly platform is composed of parallel mechanisms, which can achieve small displacement, large posture, large rotation angle, and high-precision movement. The Beckhoff controller is used to control the nine input motors of the parallel mechanism. The assembly platform is also the place where fixed parts and clamped parts are assembled. Vision B is used to provide visual guidance for the assembly process.

[0046] First of all, in the process of R&D, different members are responsible for different modules of the 3D acceleration sensor assembly system. The assembly system actuators can be divided into assembly platform module, collaborative robot module, assembly robot module, and the remaining simple mechanical structures, sensors and vision modules are embedded in the corresponding modules as needed. Therefore, this system should include 3 independent host computer systems when it is not integrated. During integration, the modules need to be classified to prevent the system data from being messy and redundant. Based on the different programming languages ​​used for the host computers of different module devices and the different interfaces used for data collection, a distributed integration method is adopted, that is, the OPC UA server is embedded in the host computers developed by each member, and finally the OPC UA client is embedded in the master control computer. Three independent host computer servers are managed and monitored.

[0047] Then, to embed the server in the host computer, the first step is to create the corresponding information model. In this case, although the main actuators contained in each module are different, they are essentially multi-input mechanical structures guided by the visual system. The case takes the assembly robot module as an example, and the drawing process of its information model legend is shown in the attached figure, and the rest will not be repeated.

[0048] For this module, it can be divided into three units. The first is a five-degree-of-freedom assembly robot with five inputs, which is the core unit of this module. Use static data collection nodes to record and describe the manufacturer, product number, key dimensions, workspace, working conditions, etc. of the equipment. The node variables of String type are used in the collection, and both English and Chinese can be entered. Use dynamic data collection nodes to record data generated when the equipment is running, such as the position, angle, speed, voltage, current, power information of motor feedback, and the position information of the end mechanism calculated by the host computer. For a single variable, use a node variable of Double type to record its value. For variables composed of multiple data such as coordinate information, use a custom type node variable to describe it, which can effectively reduce the number of nodes and reduce the system load. Use method collection nodes to record the methods of calling the equipment that have been created in the host computer, and create method nodes based on the input parameters required by the method. Use event sets to record the state changes of a component. The event class is the parent class of the condition class, and the condition class is the parent class of the warning class. The created events will trigger warnings and events according to the conditions, which is the main measure to ensure the safe operation of the system.

[0049] Then, after creating the information model, you can start to create an OPC UA server in the host computer. Take the method in Implementation Case 1 to create specific nodes for the information model in the above figure and bind specific device data sources to the variables. After this step, you can get the address space of the server. The address space contains the unique node number of each node. You can access the node through the node number and read the node information. Users with modification permissions can also modify the node after accessing it. Finally, you need to call the security policy supported by the OPC UA protocol. This example uses base256; signature and certificate, and create an account and password to complete the creation and startup of the server on the host computer.

[0050] Finally, it is necessary to create an OPC UA client in the master control computer. The specific steps are to first add nodes of custom types in the three information models to the client code to avoid the inability to read and modify custom type variable nodes due to undefined types. Subsequently, create an internal index to match the nodes in the information model, and then get the corresponding node number based on the node name. With the node number, you can access the objects, variables, and method information stored in the address space. Not only can you display the required information through the visual interface, you can also bind the call of the method node to buttons or other event controls for user convenience. Realize the monitoring and method call functions of the control system.

[0051] To build a complete automated control system, the following automated assembly strategies are also required.

[0052] First, before assembly, the sequence of part assembly needs to be obtained. Assembly Sequence Planning (ASP) is an NP problem. Using various methods and techniques to find the optimal assembly sequence plays an important role in generating a satisfactory assembly sequence for the assembly process. With a large number of studies introducing swarm optimization algorithms into the optimization of practical engineering, such as ant colony algorithm, fruit fly optimization algorithm, particle swarm algorithm, genetic algorithm, etc., which have been proven to effectively give the optimal sequence of product assembly. However, for cases similar to this one, there is less research on the sequence planning of multiple actuators operating in parallel. In this example, an improved flower pollination algorithm is used for the micro-assembly system in this example. The algorithm calculation process is as follows: (1) According to the assembly relationships given by the engineering modeling SolidWorks, the assembly relationships and constraint conditions can be extracted and expressed in mathematical models as an assembly precedence matrix, interference matrix, connection matrix, etc. to obtain the initial parameters. Then, the initial population, that is, the assembly sequence, is obtained by the opposition-based learning method. The transition probability p ∈ (0, 1) affected by the number of iterations.

[0053] (2) Calculate the fitness value of each population and solve the current optimal solution and optimal value.

[0054] (3) Obtain a random number rand in the range of [0, 1]. If p > rand, update the next generation according to the global pollination formula.

[0055] Since the formula will generate decimals and may be greater than the part number and the number may be repeated, it is necessary to integerize and uniquify the numbers in the newly generated population, which is called population repair.

[0056] (4) If p < rand, update the next generation according to the local pollination formula and also perform population repair.

[0057] (5) Calculate the fitness value corresponding to the new population obtained in (3) or (4). If it is better than the currently stored optimal fitness, replace the current optimal solution and optimal fitness.

[0058] (6) Judge the end condition. If it is satisfied, output the optimal solution and optimal fitness; otherwise, go to (3).

[0059] Secondly, during assembly, closed-loop assembly needs to be completed. After having the address space provided by the client and the optimal assembly sequence in the first step, this example proposes a step-based automated assembly process, as shown in the appendix Figure 5 . First, according to the optimal assembly sequence, for the parts that need to be assembled, in each step, the device operation methods and parameters called and its previous steps need to be saved in the database during the first manual debugging. Let the i-th step be S i , which is a row vector, where: S i[1] represents the step number, S i [2] represents the execution module name, S i [3] represents the operation to be performed, S i [4] represents the current device status, S i [5,7...2n+1] represents the number of the previous step, S i [6,8,,,2n+2] represents the state of the previous step (there can be multiple previous steps). After the virtual execution is completed to automatically assemble the process, a column S is added. i [2n+3] represents the sequence of steps in this automatic assembly process.

[0060] During assembly, you first need to read the database and follow the instructions in the attached Figure 5 The process in the process finds operations without preceding steps and starts to create threads to execute these operations in parallel, such as S pre [3], these operations are just English or Chinese identifiers in the database, so the program also needs to find the node number of the corresponding method node for these operations according to the internal table, so that the method node can be accessed in the control system, and then the method function of the host computer where the server is located is called by calling the method node. The host computer then transmits the command to the device through the gateway to make the device perform the corresponding movement. If the device can accept the command, it will return feedback data to identify the current state of the device after completing the command. The feedback data has created a monitoring item on the OPCUA server, and the client will be notified of any changes. After the client receives the status change notification, it will perform subsequent processing based on the returned digital identifier. For example, returning status 1 means that the device has successfully completed this operation and allows subsequent operations to be performed. In addition, each line is related to S pre [3] The step status of the identifier S[5,7,,,2n+1] with the same name is also set to status 1, indicating that the previous step has been completed and there is no need to wait for this step; returning to status 2 means that the device has received the command and it has moved, but it cannot reach the preset position of the operation, and the parameters need to be adjusted to make the device or command; returning to status 3 means that the device has not moved, and a fault or warning pops up to remind the operator to check the device problem. If the command cannot be accepted, it is necessary to check the connection between the server and the client.

[0061] The above is the initial operation. The next operation needs to find the initial operation identifier in the pre-operation library, that is, find S next [5,7,,,2n+1]=S pre [3], then find the next operation corresponding to the row and start calling the method to execute S next [3] For operations with multiple predecessor operations, it is necessary to wait for all predecessor operations to return to a state that allows subsequent operations before execution can begin. This loop repeats until the identifier of the current operation is not found in the predecessor operation library, indicating that the thread ends, the thread is closed, and the control system is notified that the assembly is complete.

[0062] In the above assembly process, in addition to state changes, it is also possible to monitor equipment movement information, the start time and end time of each assembly step, information on parts used in assembly, and the quality of completed operations, providing all-round monitoring for assembly movement.

[0063] Finally, after completing multiple assemblies, the following analysis should be conducted to determine whether the assembly system is stable. First, during assembly, the information of each motion unit of the equipment should be collected, because the assembly action is basically fixed on the assembly platform, and the motion data of each unit of the equipment should not change too much, and the overall distribution should conform to the normal distribution. The data should be placed in a statistical chart for analysis. If it does not conform to the law, an early warning message should be provided to remind the staff that the equipment is unstable.

[0064] The above steps can realize the fully automatic assembly integrated control method of the three-dimensional acceleration sensor. In addition to the three-dimensional acceleration sensor, for tiny products that can be given assembly information, it can also generate an automated assembly process step by step through assembly sequence planning, and realize the monitoring of the assembly process.

[0065] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A design method for an integrated control framework for adaptive assembly of tiny parts, characterized by: The following steps are included: Configure an OPC UA server in the sub-host computer of each device in the micro-assembly device layer, determine the unstructured data generated by the equipment during the production process of the micro-parts assembly system, classify the data and build the corresponding OPC UA information model; Instantiate the information model of the equipment in the assembly system and configure it in the OPC UA server, bind the assembly system equipment data to the corresponding nodes in the OPC UA information model, and complete the mapping from the physical model to the data model; Use the OPC UA client to access the information model in the OPC UA server in the sub-host computer. By querying the nodes in the information model, the data of the underlying equipment of the assembly system can be monitored in real time to understand the system operation status. Add assembly sequence planning, step-based automated assembly processes, and assembly quality monitoring methods to the control interface; Integrate the OPC UA client on the control interface of the master computer to display monitoring information in real time and predict the stability of equipment operation; Based on the extensibility of OPC UA, the control interface in the main host computer is integrated with the OPC UA aggregation server as a transit point for data transmission. Through the cross-platform compatibility of OPC UA, control systems on different platforms can be developed, or the client can be embedded in the digital twin to complete data transmission and reception.

2. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: Unstructured data includes the position and angle information of each axis in the five-degree-of-freedom assembly robot, collaborative robot arm and assembly platform, movement speed, motor temperature, images and sensor signals used for visual servoing and position detection, and voltage information of piezoelectric ceramics in the micro-motion platform.

3. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 2, characterized in that: The unstructured data of mechanical equipment is divided into static data sets and dynamic data sets. The static data sets store the inherent information of the equipment, including the manufacturer, product number, and equipment size; the dynamic data sets store the constantly changing information of the equipment during the production process, including the equipment operation mode, power, and actuator operation status. The common data of the equipment is abstracted into classes, and then the classes are instantiated into objects according to actual requirements. The created objects are stored in the address space according to a specific structural hierarchy to generate an information model.

4. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: Building an OPC UA information model includes exposing the interface for collecting data from the device sub-host computer directly to the corresponding node in the information model, completing the mapping of device physical data to server information model data, and binding the data to the nodes of the information model in real time to facilitate subsequent client and aggregation server subscription and reading and writing.

5. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: Real-time monitoring of the underlying equipment data of the assembly system includes embedding the OPC UA client in the control interface of the integrated system in the overall host computer, connecting to the OPC UA servers of all underlying devices, thereby obtaining the physical data information exposed by each device through the information model, placing the information to be monitored on the control interface, and completing the monitoring of the entire assembly system. The data of each device is finally integrated in the overall host computer to achieve interconnection between devices.

6. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: The assembly sequence planning algorithm developed by the control interface is an improved flower pollination algorithm. According to the needs of sequence planning, the assembly relationship parameters of the products to be assembled are selected as the parameters in the solution process. The minimum cost is obtained through iteration, and the corresponding sequence is the optimal assembly sequence.

7. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: The assembly relationship parameters of the products to be assembled include the priority relationship between parts, stability, assembly direction and tool change, and the cost includes the assembly time, connection stability, direction and number of tool changes.

8. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: Predicting equipment stability includes collecting historical data for a period of time into a database by the OPC UA client to form a training set. After training, the real-time collected data is put into a BP neural network for calculation to predict the stability of the equipment in operation.

9. The method for designing an integrated control framework for adaptive assembly of micro parts according to claim 1, characterized in that: As an extension of the control system, a digital twin is created for virtual assembly simulation. The rationality of planning results such as assembly sequence, assembly path planning, and motion interference is verified, which provides guidance for assembly work. The integrated control framework is production-oriented and integrated with the existing MES system to provide the data required for product quality management, material management, and production process management.

10. An integrated control framework for adaptive assembly of tiny parts, characterized by: include Micro-assembly equipment layer: including micro-assembly robot module, micro-assembly platform module, and micro-assembly collaborative robot module. Each module is equipped with equipment, and each equipment is equipped with a corresponding sub-host computer; Data transmission layer: including OPC UA servers and clients configured corresponding to each sub-host computer, used to use OPC UA communication protocol to realize communication between each sub-host computer and the main host computer, determine the unstructured data generated by the equipment in the production process of the micro-parts assembly system, classify the data and build the corresponding OPC UA information model, instantiate the information model of the corresponding equipment in the assembly system and configure it to the OPC UA server, bind the assembly system equipment data to the corresponding nodes in the OPC UA information model, complete the mapping from physical model to data model, use the OPC UA client to access the information model in the OPC UA server in the sub-host computer, and monitor the underlying equipment data of the assembly system in real time by querying the nodes in the information model; Control system layer: including the overall host computer compatible with each module of the micro-assembly equipment layer, used to monitor the equipment of each module to achieve assembly sequence planning, automatic assembly process generation, assembly process monitoring, fault detection, data storage and analysis, and predict the stability of the equipment during operation; Digital twin layer: includes an aggregation server connected to the main host computer. Through the cross-platform compatibility of OPC UA, control systems on different platforms are developed, or clients are embedded in the digital twin to complete data transmission and reception.

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