3D digital twinning system based on silicon steel production line and control method

Abstract

The invention discloses a 3D digital twinning system based on a silicon steel production line and a control method, and the system comprises a physical layer which obtains the real-time production data of the actual production equipment of the silicon steel production line through a sensor; a data interface of the data acquisition layer is connected with a sensor, and data of the physical layer is transmitted to the virtual twin layer; virtualizing a twinborn layer, constructing a 3D digital twinborn model of the silicon steel production line, and mapping a production process in real time; and the control decision layer is used for carrying out data analysis and logic judgment, calculating the thickness deviation of the silicon steel, carrying out comprehensive evaluation, making an intelligent decision on acid rolling and edge cutting, and feeding back optimal production parameters and edge cutting control signals through a 3D digital twinborn model. According to the system and the control method, real-time monitoring and dynamic analysis of production equipment, process parameters and product quality can be achieved, accurate evaluation of cold rolling thickness deviation and intelligent control of acid rolling trimming are organically combined, quality monitoring and process optimization of a silicon steel production line are achieved, and production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of silicon steel production and digital twin technology, and in particular to a 3D digital twin system and control method based on a silicon steel production line. Background Art

[0002] With the development of Industry 4.0 and intelligent manufacturing technologies, 3D digital twin systems have been widely used in the manufacturing sector. By building 3D digital twin systems within production lines, real-time monitoring of production equipment and process parameters, and interacting with virtual models, the level of intelligence and decision-making capabilities of production lines can be effectively improved.

[0003] In the current silicon steel production process, although standardized software is available to collect and organize production data, the data is relatively scattered, making it difficult for personnel to operate and query, and the data display still remains on a two-dimensional plane, lacking intuitiveness.

[0004] On the other hand, thickness uniformity and edge quality of cold-rolled sheet are crucial performance indicators during silicon steel production. Traditional process control methods lack the ability to dynamically evaluate and optimize real-time production data. Furthermore, during the pickling stage, the decision to perform edge trimming is often based on empirical judgment, potentially leading to material waste and reduced product quality.

[0005] Therefore, building a silicon steel production line system based on 3D digital twin technology, realizing intelligent decision-making on thickness deviation assessment and pickling edge trimming, centralizing important production data, combining it with a three-dimensional model, replicating the real workshop scene, and visualizing the data are key technical directions for improving silicon steel production efficiency and quality. Summary of the Invention

[0006] The problem to be solved by the present invention is to provide a 3D digital twin system and control method based on a silicon steel production line, which integrates thickness deviation evaluation and pickling edge trimming control functions to achieve real-time monitoring and dynamic analysis of production equipment, process parameters and product quality.

[0007] The present invention adopts the following technical solution: a 3D digital twin system based on a silicon steel production line, comprising:

[0008] Physical layer: includes the actual production equipment and several sensors of the silicon steel production line, which obtain real-time production data of the actual production equipment during the production process through sensors;

[0009] Data acquisition layer: The data interface is connected to the sensor, collecting the process parameters obtained by the sensor equipment in real time and transmitting the physical layer data to the virtual twin layer;

[0010] Virtual Twin Layer: Build a 3D digital twin model of the silicon steel production line to map the production process in real time, including virtual mapping of equipment status, process parameters, and product quality;

[0011] Control decision-making layer: Through data analysis and logical judgment, the silicon steel thickness deviation is calculated and comprehensively evaluated, intelligent decisions are made on pickling and trimming, and the optimal production parameters and trimming control signals are fed back through the 3D digital twin model.

[0012] Preferably, in the physical layer, the actual production equipment includes: a hot rolling mill, a pickling mill, and a cold rolling mill; and the sensors include several measurement sensors, operation side sensors, and transmission side sensors.

[0013] Preferably, the 3D digital twin model of the silicon steel production line is constructed in a modular manner, including:

[0014] Overview module: Build a 3D model of the workshop to display an overview of actual production equipment and production data;

[0015] Process introduction module: explaining the process flow of silicon steel through 3D animation;

[0016] Data dashboard module: displays the real-time production data of the actual production equipment in the workshop;

[0017] Video surveillance module: combines on-site monitoring with the 3D model of the workshop to provide intuitive on-site perception of the workshop;

[0018] Point inspection module: provides staff with point inspection reports and operation and maintenance suggestions;

[0019] Unmanned storage area module: simulates real driving motion through a three-dimensional driving model;

[0020] Equipment management module: combines the 3D model of the equipment with the actual operating status and production data of the actual production equipment to store real-time production data;

[0021] Safety management module: performs access control management and gas monitoring to ensure safe production in the workshop.

[0022] Furthermore, the 3D digital twin model of the silicon steel production line is built using the UGUI framework on the front end and the C# language is used for page development; the back end is built using the .NET framework, and the script back end uses Mono through JIT compilation; the interface unit is developed using the C# language, including the back-end business logic interface and the front-end access interface.

[0023] Preferably, the 3D digital twin model of the silicon steel production line collects and processes silicon steel production data, access control data, driving data, energy data and production equipment data in real time, stores them in the server database through the interface unit, analyzes and optimizes the data, and publishes the results in the form of a client.

[0024] Furthermore, in the control decision layer, the silicon steel thickness deviation is calculated as follows:

[0025] Calculate real-time cold-rolled crown based on cold-rolled plate measurement data:

[0026]

[0027] Among them, h c Indicates the average center thickness, h mid Respectively represent the target value of center thickness, h O25 、h O50 Respectively represent the thickness deviation at the 25mm and 50mm positions on the operating side, h D25 、h D50 They represent the thickness deviation at the 25mm and 50mm positions on the transmission side respectively, and a and b represent the trimming compensation coefficients;

[0028] Calculate real-time cold rolling wedge:

[0029] W={[h D25 +(h D50 -h D25 )*b+h mid ]-[h O25 +(h O50 -h O25 )*a+h mid ]}*1000

[0030] Calculate the same plate difference:

[0031]

[0032] Among them, C represents the real-time cold-rolled convexity, W represents the real-time cold-rolled wedge, and TBC represents the same-plate difference.

[0033] Preferably, in the control decision layer, an intelligent decision is made on the pickling edge trimming, comparing the nominal width W of the hot rolled coil hot And the actual width of the cold rolled coil W cold , real-time decision on whether to perform pickling and trimming:

[0034] If W hot >W cold , then perform the trimming operation;

[0035] If W hot <W cold , then skip the trimming process.

[0036] Furthermore, in the control decision layer, the silicon steel thickness deviation assessment results and the intelligent decision results of pickling and trimming are fed back to the 3D digital twin model of the silicon steel production line, and the key process parameters are dynamically adjusted in combination with real-time production data to optimize the cold rolling and pickling processes.

[0037] Furthermore, key process parameters include: plate thickness, width, edge drop value, and target value.

[0038] The technical solution of the present invention also provides a 3D digital twin system control method based on a silicon steel production line, which is applied to any of the above-mentioned 3D digital twin systems and includes the following steps:

[0039] Step 1. Open the 3D digital twin system client, enter the overview interface, and view the actual production equipment and production data status in real time;

[0040] Step 2: Select the production control that needs to be performed, process the production control request data through the back-end business logic interface, call the data access function and access the server database to obtain data, and return the data to the front-end;

[0041] Step 3: Perform data analysis and logical judgment, calculate the silicon steel thickness deviation and conduct a comprehensive evaluation, make intelligent decisions on pickling and trimming, and feedback the optimal production parameters and trimming control signals through the 3D digital twin model;

[0042] Step 4: The front-end access interface receives the data returned by data analysis and logical judgment, and presents it on the client page.

[0043] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0044] 1. The present invention constructs a 3D digital twin system based on a silicon steel production line, which can realize real-time monitoring and dynamic analysis of production equipment, process parameters and product quality, and combines actual physical entities with their virtual models to help operators comprehensively grasp the production status of the production line, thereby improving workshop production efficiency, optimizing safety management, promoting innovation and development, and thus realizing intelligent production in the workshop.

[0045] 2. The 3D digital twin system of the present invention integrates thickness deviation assessment and pickling trimming control functions, and provides a cold rolling thickness deviation calculation method based on the digital twin model, which can accurately evaluate quality parameters including convexity, wedge shape and plate difference; at the same time, it provides intelligent decision rules for pickling trimming based on width data, which can effectively improve the automation and optimization capabilities of the pickling process.

[0046] 3. The present invention uses 3D digital twin technology to organically combine the precise assessment of cold-rolled thickness deviation with the intelligent control of pickling edge trimming, thereby realizing quality monitoring and process optimization of silicon steel production lines, improving production efficiency, and reducing material waste, with significant industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the architecture diagram of the 3D digital twin system based on the silicon steel production line of the present invention;

[0048] Figure 2 This is a block diagram of the 3D digital twin model of the silicon steel production line of the present invention;

[0049] Figure 3 This is a flow chart of the 3D digital twin system control method of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the application are further elaborated in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments involved in the present invention. All non-innovative embodiments of other researchers in this field on this embodiment fall within the scope of protection of the present invention. At the same time, the step numbers in the embodiments of the present invention are only set for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0051] The present invention provides a 3D digital twin system based on a silicon steel production line, such as Figure 1 As shown, it includes: physical layer, data acquisition layer, virtual twin layer and control decision layer.

[0052] Physical layer: includes the actual production equipment and several sensors of the silicon steel production line, which obtain real-time production data of the actual production equipment during the production process through sensors;

[0053] Data acquisition layer: The data interface is connected to the sensor, collecting the process parameters obtained by the sensor equipment in real time and transmitting the physical layer data to the virtual twin layer;

[0054] Virtual Twin Layer: Build a 3D digital twin model of the silicon steel production line to map the production process in real time, including virtual mapping of equipment status, process parameters, and product quality;

[0055] Control decision-making layer: Through data analysis and logical judgment, the silicon steel thickness deviation is calculated and comprehensively evaluated, intelligent decisions are made on pickling and trimming, and the optimal production parameters and trimming control signals are fed back through the 3D digital twin model.

[0056] Specifically, in one embodiment of the present invention, the actual production equipment at the physical layer includes: a hot rolling mill, a pickling rolling mill, and a cold rolling mill; and sensors include a number of measurement sensors, a number of operation-side sensors, and a number of transmission-side sensors.

[0057] The variable interface positions of the sensor in this embodiment are shown in Table 1 below, which shows the core variables required for cold-rolled plate thickness deviation evaluation and pickling edge trimming control, as well as their sources, types, and functions.

[0058] Table 1 Variable interface location

[0059]

[0060]

[0061] The specific values ​​of the rewinding trimming compensation coefficients a and b corresponding to different silicon steel grades are shown in Table 2 below. The coefficients in the table are calculated based on the current trimming amount. If the trimming amount changes, the coefficients should be corrected in principle.

[0062] Table 2 Values ​​of compensation coefficients a and b for rewinding trimming corresponding to different silicon steel grades

[0063]

[0064] Specifically, in one embodiment of the present invention, the thickness deviation of the cold-rolled sheet is evaluated by the following method:

[0065] In this embodiment, the measurement data of a silicon steel coil is:

[0066] Center thickness target value h mid =2.0mm;

[0067] Operating side thickness deviation h O25 =0.02mm,h O50 =0.03mm;

[0068] Transmission side thickness deviation h D25 =-0.01mm,h D50 =0.01mm;

[0069] Average center thickness h c =2.01mm;

[0070] Compensation coefficients a=0.3, b=0.4.

[0071] Calculated:

[0072] Cold rolled crown:

[0073]

[0074] Wedge:

[0075] W={[h D25 +(h D50 -h D25 )*b+h mid ]-[h O25 +(h O50 -h O25 )*a+h mid ]}*1000

[0076] ≈20 μm;

[0077] Difference from the same board:

[0078]

[0079] Specifically, in one embodiment of the present invention, the pickling edge trimming determination is performed as follows:

[0080] Nominal width W of a hot rolled coil hot =1200mm, actual width of cold rolled coil W cold =1195mm, by comparing the nominal width of hot rolled coil W hot And the actual width of the cold rolled coil W cold , real-time decision on whether to perform pickling and trimming:

[0081] In this embodiment, according to the system judgment: W hot >W cold , acid rolling and trimming are required.

[0082] Furthermore, the thickness deviation assessment results and trimming determination results are fed back to the digital twin system of the present invention, and the key process parameters are dynamically adjusted in combination with real-time production data to optimize the cold rolling and pickling processes.

[0083] Specifically, in one embodiment of the present invention, the 3D digital twin model of the silicon steel production line adopts a modular design to realize functions, such as Figure 2 As shown, it includes: overview module, process introduction module, data dashboard module, video monitoring module, point inspection module, unmanned storage area module, equipment management module and safety management module.

[0084] Among them, the overview module displays an overview of the workshop's three-dimensional model and some production data; the process introduction module systematically explains the process flow of silicon steel through three-dimensional animation; the data dashboard module displays the production data of concern to the workshop in detail and centrally; the video monitoring module combines on-site monitoring with three-dimensional models to provide more realistic and intuitive situational awareness; the point inspection module provides users with point inspection reports and operation and maintenance suggestions; the unmanned warehouse area module simulates real movement conditions through a three-dimensional driving model; the equipment management module combines the three-dimensional model of the equipment with the actual operating status and data of the equipment; and the safety management module safeguards the safe production of the workshop from the two aspects of access control management and gas monitoring.

[0085] Furthermore, in the 3D digital twin model of the silicon steel production line in this embodiment, the front end is built using the UGUI framework, the page is developed using the C# language, the back end is built using the .NET framework, the script back end uses Mono to use just-in-time (JIT) compilation, compiles the code on demand at runtime, and the C# language is used to develop the interface.

[0086] In terms of human-computer interaction design, the model of this embodiment can achieve automated and convenient display to the greatest extent. After collecting and preliminarily processing the production data, access control data, driving data, energy data and equipment data of the silicon steel production line, it uses a self-developed computer program to analyze and optimize the data, and finally publishes the relevant results through the client to ensure that the model display effect is more delicate and realistic.

[0087] Specifically, in one embodiment of the present invention, a 3D digital twin system control method based on a silicon steel production line is provided, wherein the process is as follows: Figure 3 shown.

[0088] First, the user double-clicks to open the 3D digital twin system client and enters the virtual twin layer overview interface. The software starts requesting background data and the front end presents the overview page data. The user can view the actual production equipment and production data status in real time.

[0089] Then, the user selects the production control required according to the menu button, processes the production control request data through the back-end business logic interface, accesses the physical layer through the data acquisition layer, calls the data access function and accesses the server database to obtain data, and returns the data to the front end.

[0090] Next, the control decision layer performs data analysis and logical judgment, calculates the silicon steel thickness deviation and conducts a comprehensive evaluation, makes intelligent decisions on pickling and trimming, and feeds back the optimal production parameters and trimming control signals through the 3D digital twin model.

[0091] Finally, the front-end access interface receives the data returned by the control decision layer's data analysis and logical judgment, controls the physical layer to control key process parameters, and presents the real-time process parameters and control results on the client page.

[0092] It can be seen that the 3D digital twin system of the present invention can organically combine the precise evaluation of cold-rolled thickness deviation with the intelligent control of pickling edge trimming, thereby realizing quality monitoring and process optimization of the silicon steel production line, thereby improving the production efficiency of the production line and reducing material waste, and has significant industrial application value.

[0093] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A 3D digital twin system based on a silicon steel production line, characterized in that: include: Physical layer: includes the actual production equipment and several sensors of the silicon steel production line, which obtain real-time production data of the actual production equipment during the production process through sensors; Data acquisition layer: The data interface is connected to the sensor, collecting the process parameters obtained by the sensor equipment in real time and transmitting the physical layer data to the virtual twin layer; Virtual Twin Layer: Build a 3D digital twin model of the silicon steel production line to map the production process in real time, including virtual mapping of equipment status, process parameters, and product quality; Control decision-making layer: Through data analysis and logical judgment, the silicon steel thickness deviation is calculated and comprehensively evaluated, intelligent decisions are made on pickling and trimming, and the optimal production parameters and trimming control signals are fed back through the 3D digital twin model.

2. The 3D digital twin system based on the silicon steel production line according to claim 1 is characterized in that: In the physical layer, the actual production equipment includes: a hot rolling mill, a pickling mill, and a cold rolling mill; the sensors include several measurement sensors, operation side sensors, and transmission side sensors.

3. The 3D digital twin system based on the silicon steel production line according to claim 1 is characterized in that: The 3D digital twin model of the silicon steel production line is constructed in a modular manner, including: Overview module: Build a 3D model of the workshop to display an overview of actual production equipment and production data; Process introduction module: explaining the process flow of silicon steel through 3D animation; Data dashboard module: displays the real-time production data of the actual production equipment in the workshop; Video surveillance module: combines on-site monitoring with the 3D model of the workshop to provide intuitive on-site perception of the workshop; Point inspection module: provides staff with point inspection reports and operation and maintenance suggestions; Unmanned storage area module: simulates real driving motion through a three-dimensional driving model; Equipment management module: combines the 3D model of the equipment with the actual operating status and production data of the actual production equipment to store real-time production data; Safety management module: performs access control management and gas monitoring to ensure safe production in the workshop.

4. The 3D digital twin system based on the silicon steel production line according to claim 3 is characterized in that: The 3D digital twin model of the silicon steel production line is built using the UGUI framework on the front end and uses the C# language for page development; the back end is built using the .NET framework, and the script back end uses Mono through JIT compilation; the interface unit is developed using the C# language, including the back-end business logic interface and the front-end access interface.

5. The 3D digital twin system based on the silicon steel production line according to claim 3 is characterized in that: The 3D digital twin model of the silicon steel production line collects and processes silicon steel production data, access control data, driving data, energy data and production equipment data in real time, stores them in the server database through the interface unit, analyzes and optimizes the data, and publishes the results in the form of a client.

6. The 3D digital twin system based on the silicon steel production line according to claim 1, characterized in that: In the control decision layer, the silicon steel thickness deviation is calculated as follows: Calculate real-time cold-rolled crown based on cold-rolled plate measurement data: Among them, h c Indicates the average center thickness, h mid Respectively represent the center thickness target value, h O25 、h O50 Respectively represent the thickness deviation at the 25mm and 50mm positions on the operating side, h D25 、h D50 Respectively represent the thickness deviation at the 25mm and 50mm positions on the transmission side, and the surface drop compensation coefficients a and b; Calculate real-time cold rolling wedge: W={[h D25 +(h D50 -h D25 )*b+h mid ]-[h O25 +(h O50 -h O25 )*a+h mid ]}*1000 Calculate the same plate difference: Among them, C represents the real-time cold-rolled convexity, W represents the real-time cold-rolled wedge, and TBC represents the same-plate difference.

7. The 3D digital twin system based on the silicon steel production line according to claim 6, characterized in that: In the control decision layer, intelligent decision is made on pickling edge trimming, comparing the nominal width W of hot rolled coil hot And the actual width of the cold rolled coil W cold , real-time decision on whether to perform pickling and trimming: If W hot >W cold , then perform the trimming operation; If W hot <W cold , then skip the trimming process.

8. The 3D digital twin system based on the silicon steel production line according to claim 7, characterized in that: In the control decision layer, the silicon steel thickness deviation assessment results and the intelligent decision results of pickling and trimming are fed back to the 3D digital twin model of the silicon steel production line. Combined with real-time production data, key process parameters are dynamically adjusted to optimize the cold rolling and pickling processes.

9. The 3D digital twin system based on the silicon steel production line according to claim 8, characterized in that: The key process parameters include: plate thickness, width, edge drop value, and target value.

10. A 3D digital twin system control method based on a silicon steel production line, applied to the 3D digital twin system according to any one of claims 1 to 9, characterized in that: The steps include: Step 1. Open the 3D digital twin system client, enter the overview interface, and view the actual production equipment and production data status in real time; Step 2: Select the production control that needs to be performed, process the production control request data through the back-end business logic interface, call the data access function and access the server database to obtain data, and return the data to the front-end; Step 3: Perform data analysis and logical judgment, calculate the silicon steel thickness deviation and conduct a comprehensive evaluation, make intelligent decisions on pickling and trimming, and feedback the optimal production parameters and trimming control signals through the 3D digital twin model; Step 4: The front-end access interface receives the data returned by data analysis and logical judgment, and presents it on the client page.

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