Forging process optimization system and optimization method based on digital twin

Abstract

A forging process optimization system and optimization method based on digital twin, which system and method belong to the technical field of forging. The forging process optimization system comprises a physical entity collection module and a digital twin system, wherein the physical entity collection module comprises a data collection module, and the data collection module is used for collecting information data of the physical entity collection module during operation. A digital twin technique is integrated into a forging process, so as to facilitate the formation of intelligent and networked production, facilitate manufacturing resource configuration and production technique sharing, and facilitate offline operation training and online production guidance. Thus, the yield of parts is increased, resource waste is reduced, and a relatively high economic benefit is achieved; and the blindness and the subjectivity of when a conventional optimization method is used are reduced, and the time for searching for an optimal process parameter combination is shortened, thereby effectively improving the process design efficiency and reducing the sample development cost.

Description

A forging process optimization system and optimization method based on digital twin

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 4, 2024, with application number 202410241420.2 and invention name “A Forging Process Optimization System and Optimization Method Based on Digital Twins”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of forging technology, and in particular to a forging process optimization system and optimization method based on digital twin. Background Art

[0003] Forging is a machining process that uses a forging machine to apply pressure to a metal blank, causing it to plastically deform, resulting in forgings with defined mechanical properties, shapes, and sizes. It is one of the two major components of forging (forging and stamping). The ratio of a metal's cross-sectional area before deformation to its cross-sectional area after deformation is called the forging ratio. Correctly selecting the forging ratio, appropriate heating temperature and holding time, appropriate initial and final forging temperatures, and appropriate deformation volume and speed are crucial for improving product quality and reducing costs. Existing forging processes still rely on prior experience and lack a sound simulation system. This leads to material waste, the inability to produce the most suitable forgings, and the inability to properly compare forgings produced from various ratios. Therefore, developing a forging process optimization system and method based on digital twins to effectively reduce manufacturing steps, shorten production time, and improve part forming accuracy and strength has become a key research topic for improving product production efficiency and quality and promoting technological transformation and upgrading in the traditional foundry industry.

[0004] Summary of the Invention

[0005] The purpose of this application is to overcome the deficiencies in the prior art and provide a forging process optimization system and optimization method based on digital twins to solve the problem that the existing forging process still uses the experience summarized by predecessors and does not have a reasonable simulation system, resulting in material waste during forging, inability to forge the most suitable forgings, and inability to perform reasonable comparison of forgings forged with various proportions.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a forging process optimization system based on digital twins, comprising a physical entity acquisition module and a digital twin system, wherein the physical entity acquisition module comprises a data acquisition module, which is used to collect information data during the operation of the physical entity acquisition module, and the physical entity acquisition module further comprises a material proportioning module, which is used for various material proportions in the forging process, and the physical entity acquisition module further comprises a network information acquisition and transmission module, which is used to collect and transmit forging process information on the network, and the various information in the data acquisition module and the material proportioning module is transmitted to the digital twin system through the network transmission system, a virtual simulation of the physical entity is established in the digital twin system, and a dynamic simulation of the forging process is performed to realize virtual-real interactive mapping. In one embodiment, the digital twin system includes a forging process simulation module, a forging information storage module, and a forging information comparison module. The forging process simulation module is used to intelligently optimize and adjust key process parameters for forging and feed back the optimization solution into the production process to guide actual production operations and improve the closed-loop optimization management of the production process. The forging information storage module is used to store data after forging process optimization simulation. The forging information comparison module is used to compare various data of the forged parts after actual forging with various data of the simulated forging parts and feed back the data into the digital twin system model. In one embodiment, the network transmission system includes a production workshop transmission network and corresponding network routing, data communication interface, human-computer interaction interface, and cloud database; the network transmission supports transmission protocols such as local area network, LAN, WiFi, Zigbee, Bluetooth, 5G, RFID, and GPS; through standardized network and interface access, various physical resources are virtualized as resource nodes in the network to achieve ubiquitous interconnection of physical entity information in the production workshop and interactive mapping with virtual space. In one embodiment, the digital twin system realizes the creation and dynamic display of forging twin models, data fusion analysis and intelligent optimization of process parameters; forms a mutual mapping relationship between all work-in-progress and manufacturing resources of the physical entity system through the network and interface of the network transmission system, realizes virtual simulation modeling and display of the forging forming process and its manufacturing resources, and data interaction with other modules of the digital twin system, realizes information exchange and closed-loop optimization; the virtual simulation of the forging process is processed using Procast and Deform software, predicts possible quality defects in the forming process, and provides corresponding improvement measures, realizes interactive mapping between virtual space and physical entities, and facilitates offline operation training and online production guidance.In one embodiment, the information in the forging information storage module and the forging information comparison module needs to be cleaned and integrated to remove data noise and interference and retain useful signals. Then, the data is standardized and discretized through data normalization processing to form a unified data system for subsequent data mining processing.

[0007] A forging process optimization method based on digital twinning includes the following steps:

[0008] Step 1: Load the metal to be forged and prepare to heat it. Heat the metal to be forged to the temperature required by the metal and then keep it warm.

[0009] Step 2: Preheat the mold required for forging by using one or more of the following methods: heat transfer from liquid metal, electric heating, and far-infrared heating. A laser thermometer is used to collect the temperature of the metal to be forged and the temperature of the mold in real time, and the data is transmitted to a data acquisition module.

[0010] Step 3: Place the heated forging into a forging die, and then forge the forging inside the die using a press. The forging pressure uses a hydraulic system, a pneumatic system, or a combination of the two.

[0011] Step 4: heat treating the forging;

[0012] Step 5: Surface treatment is performed on the forged metal. The forged metal parts are then inspected and various performance data of the metal are tested. The tested data are then entered into the forging information storage module for storage.

[0013] Step six, compare the data detected in step five with the data simulated by the digital twin system model, and then transmit the information to the digital twin system for analysis and processing, and combine the parameter information to quickly optimize the forging process parameters to obtain the best process parameter combination, and feed the parameter combination back to the production equipment and process control system to achieve dynamic adjustment and precise control of the production process, so as to improve the performance of the forgings and increase production efficiency. In one embodiment, after the forging in step three is completed, cooling water is injected into the upper water tank of the upper mold and the lower water tank of the lower mold to quickly cool the forgings; during the forging process, the real-time slider speed, impact force and holding time are collected by relevant sensing equipment and transmitted to the digital twin system for analysis and processing. In one embodiment, five parameters are selected, namely metal forging temperature, mold temperature, slider speed, impact force and production rhythm, and three parameters, namely hardness, tensile yield and elongation, are used as optimization targets to construct an optimization model for process parameters as follows:

[0014] Where x1 is the metal forging temperature, x2 is the die temperature, x3 is the slider speed, x4 is the impact force, and x5 is the production cycle; b i and a i They correspond to the input variables x i The upper and lower limits of the value are i=1,2,...,5; f1(x) is the objective function of forging hardness, f2(x) is the objective function of forging tensile yield, and f3(x) is the objective function of forging elongation. The weighted combination method is used to assign different weights to each objective function to eliminate the influence of different dimensions. The multi-objective optimization problem is converted into a single-objective optimization problem through the weight coefficient α. The unified objective function after conversion is as follows:

[0015] F(x)=min[f1(x),f2(x),f3(x)]=α1f1(x)+α2f2(x)+α3f3(x);

[0016] Where α i is the weight coefficient of the single target output value, i=1,2,...,5, and its value is determined by the following relationship:

[0017] in

[0018] Where c and d are the single target output values ​​f i (x) the range of variation;

[0019] Based on the selected parameters and optimization goals, and according to the relevant data collected by the data perception system, an intelligent algorithm is used to find the optimal solution.

[0020] Based on the selected parameters and optimization targets, and according to the relevant data collected by the data perception system, an intelligent algorithm is used to perform optimization. In one embodiment, the intelligent algorithm is used to perform parameter optimization using an improved particle swarm optimization BP neural network algorithm.

[0021] Compared with the prior art, this application has at least the following beneficial effects:

[0022] In the above scheme, by integrating digital twin technology into the forging process, it is conducive to the formation of intelligent and networked production, facilitating the allocation of manufacturing resources and the sharing of production technology, and using digital twin technology for virtual-real interaction in the production process, which can dynamically display the entire process of part forming, facilitating offline operation training and online production guidance; through the dynamic simulation of the digital twin model, the process parameters are optimized and adjusted, potential problems in the forming process can be predicted and improvement measures can be proposed, thereby improving the yield rate of parts and reducing resource waste, with higher economic benefits; reducing the blindness and subjectivity of conventional optimization methods, shortening the time for searching for the optimal process parameter combination, effectively improving process design efficiency and reducing sample development costs.

[0023] Figures in the specification

[0024] The accompanying drawings, which are incorporated herein and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure and to enable one skilled in the relevant art to make and use the present disclosure.

[0025] FIG1 is a system structure diagram of a forging process optimization system and optimization method based on digital twin;

[0026] Figure 2 is a flow chart of the forging process optimization system and optimization method based on digital twin.

[0027] As shown in the figure, in order to clearly implement the structure of the embodiment of the present application, specific structures and devices are marked in the figure, but this is only for illustrative purposes and is not intended to limit the present application to the specific structure, device and environment. According to specific needs, ordinary technicians in this field can adjust or modify these devices and environments, and the adjustments or modifications made are still included in the scope of the appended claims. DETAILED DESCRIPTION

[0028] The following describes in detail a digital twin-based forging process optimization system and method provided by this application, in conjunction with the accompanying drawings and specific embodiments. It is also noted that, to make the embodiments more detailed, the following embodiments are optimal and preferred embodiments, and those skilled in the art may also adopt other alternatives for implementing certain known technologies. Furthermore, the accompanying drawings are only for the purpose of describing the embodiments in more detail and are not intended to limit this application to any specific extent.

[0029] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0030] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0031] It will be understood that the meanings of “on,” “over,” and “above” in this disclosure should be interpreted in the broadest manner, such that “on” means not only “directly on” something, but also includes being “on” something with intervening features or layers, and “on” or “over” means not only “on” or “above” something, but also includes being “on” or “above” something with no intervening features or layers.

[0032] Additionally, spatially relative terms such as "below," "beneath," "lower," "above," and "upper" may be used herein for descriptive convenience to describe the relationship of one element or feature to another element or features, as illustrated in the accompanying drawings. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. The device may be oriented in other ways, and the spatially relative descriptors used herein should be similarly interpreted accordingly.

[0033] Example

[0034] As shown in Figure 1, an embodiment of the present application provides a forging process optimization system based on digital twins, including: a physical entity acquisition module and a digital twin system. The physical entity acquisition module includes a data acquisition module, which is used to collect information data during the operation of the physical entity acquisition module. The physical entity acquisition module also includes a material proportioning module, which is used for various material proportions in the forging process. The physical entity acquisition module also includes a network information acquisition and transmission module, which is used to collect and transmit forging process information on the network. The various information in the data acquisition module and the material proportioning module is transmitted to the digital twin system through the network transmission system. A virtual simulation of the physical entity is established in the digital twin system, and a dynamic simulation of the forging process is performed to realize virtual-real interactive mapping. In some examples, the digital twin system includes a forging process simulation module, a forging information storage module, and a forging information comparison module. The forging process simulation module is used to intelligently optimize and adjust key process parameters for forging, and feed the optimization solution back to the production process to guide actual production operations and improve the closed-loop optimization management of the production process. The forging information storage module is used to save the data after forging process optimization simulation. The forging information comparison module is used to compare the various data of the forgings after actual forging with the various data of the simulated forgings, and feed it back to the digital twin system model. In some examples, the network transmission system includes the production workshop transmission network and the corresponding network routing, data communication interface, human-computer interaction interface, and cloud database; the network transmission supports the transmission protocols of local area network, LAN, WiFi, Zigbee, Bluetooth, 5G, RFID, and GPS; through standardized network and interface access, each physical resource is virtualized as a resource node in the network to achieve ubiquitous interconnection of physical entity information in the production workshop and interactive mapping with the virtual space.

[0035] In some examples, the data acquisition module collects information data from the physical entity acquisition module during operation and transmits this information to the digital twin system via the network information acquisition and transmission module. The material proportioning module transmits the various material proportions during the forging process to the digital twin system via the network information acquisition and transmission module. The physical entity acquisition module transmits data collected during actual production about the forging process to the digital twin system.

[0036] The digital twin system is used to simulate the forging process, obtain data from the simulation, and compare the data collected from the actual forging process with the simulated data to optimize the forging process parameters. To achieve these functions of the digital twin system, the forging process simulation module in the digital twin system can establish a virtual simulation of the physical entity and perform dynamic simulation of the forging process. The forging information storage module saves the data after the forging process optimization simulation, and the forging information comparison module compares the various data of the forgings after actual forging with the various data of the simulated forgings. The forging process simulation module also further performs intelligent optimization and adjustment of key forging process parameters to obtain the optimal process parameter combination.

[0037] The digital twin system model mentioned above refers to the forged twin model created by the digital twin system.

[0038] In some examples, the digital twin system realizes the creation and dynamic display of forging twin models, data fusion analysis and intelligent optimization of process parameters; through the network and interface of the network transmission system, a mutual mapping relationship is formed between all work-in-progress and manufacturing resources of the physical entity system, realizing virtual simulation modeling and display of the forging forming process and its manufacturing resources, as well as data interaction with other modules of the digital twin system, realizing information exchange and closed-loop optimization; the virtual simulation of the forging process is processed using Procast and Deform software to predict possible quality defects in the forming process and provide corresponding improvement measures, realizing interactive mapping between virtual space and physical entities, and facilitating offline operation training and online production guidance.

[0039] In some examples, the information in the forging information storage module and the forging information comparison module needs to be cleaned and integrated to remove data noise and interference, retain useful signals, and then the data is standardized and discretized through data normalization processing to form a unified data system for subsequent data mining processing.

[0040] Example

[0041] As shown in FIG2 , an embodiment of the present application provides a forging process optimization method based on digital twinning, comprising the following steps:

[0042] Step 1: Load the metal to be forged and prepare to heat it. Heat the metal to be forged to the temperature required by the metal and then keep it warm.

[0043] Step 2: Preheat the mold required for forging by using one or more of the following methods: heat transfer from liquid metal, electric heating, and far-infrared heating. A laser thermometer is used to collect the temperature of the metal to be forged and the temperature of the mold in real time, and the data is transmitted to a data acquisition module.

[0044] Step 3: Place the heated forging into a forging die, and then forge the forging inside the die using a press. The forging pressure uses a hydraulic system, a pneumatic system, or a combination of the two.

[0045] Step 4: heat treating the forging;

[0046] Step 5: Surface treatment is performed on the forged metal. The forged metal parts are then inspected and various performance data of the metal are tested. The tested data are then entered into the forging information storage module for storage.

[0047] In step six, the data detected in step five is compared with the data simulated by the digital twin system model. The information is then transmitted to the digital twin system for analysis and processing. The forging process parameters are quickly optimized based on the parameter information to obtain the optimal process parameter combination. This parameter combination is then fed back to the production equipment and process control system to achieve dynamic adjustment and precise control of the production process, thereby improving the performance of forgings and increasing production efficiency.

[0048] In some examples, a hydraulic system or a pneumatic system or a combination of the two is used to generate the forging pressure in step three.

[0049] In some examples, after the forging in step three is completed, cooling water is injected into the upper water tank of the upper mold and the lower water tank of the lower mold to quickly cool the forging; during the forging process, the real-time slider speed, impact force and holding time are collected by relevant sensing equipment and transmitted to the digital twin system for analysis and processing.

[0050] In some examples, five parameters, namely metal forging temperature, die temperature, slide speed, impact force and production cycle, are selected, and three parameters, namely hardness, tensile yield strength and elongation, are used as optimization targets. The optimization model of process parameters is constructed as follows:

[0051] Where x1 is the metal forging temperature, x2 is the die temperature, x3 is the slider speed, x4 is the impact force, and x5 is the production cycle; b i and a i They correspond to the input variables x i (i=1,2,...,5) the upper and lower limits of the value; f1(x) is the target function of forging hardness, f2(x) is the target function of forging tensile yield, and f3(x) is the target function of forging elongation;

[0052] The weighted combination method is used to assign different weight values ​​to each objective function to eliminate the influence of different dimensions. The multi-objective optimization problem is converted into a single-objective optimization problem through the weight coefficient α. The unified objective function after conversion is as follows:

[0053] F(x)=min[f1(x),f2(x),f3(x)]=α1f1(x)+α2f2(x)+α3f3(x);

[0054] Where α i is the weight coefficient of the single target output value, i=1,2,...,5, and its value is determined by the following relationship:

[0055] in

[0056] Where c and d are the single target output values ​​f i (x) the range of variation;

[0057] In some examples, A in the above formula i That is α i .

[0058] Based on the selected parameters and optimization goals, and according to the relevant data collected by the data perception system, an intelligent algorithm is used to find the optimal solution.

[0059] In some examples, the intelligent algorithm uses an improved particle swarm optimization BP neural network algorithm to perform parameter optimization.

[0060] The technical solution provided in this application integrates digital twin technology into the forging process, which is conducive to the formation of intelligent and networked production, facilitates the allocation of manufacturing resources and the sharing of production technology, and uses digital twin technology for virtual-real interaction in the production process, which can dynamically display the entire process of part forming, facilitating offline operation training and online production guidance; through the dynamic simulation of the digital twin model, the process parameters are optimized and adjusted, potential problems in the forming process can be predicted and improvement measures can be proposed, thereby improving the yield rate of parts and reducing resource waste, with high economic benefits; reducing the blindness and subjectivity of conventional optimization methods, shortening the time for searching for the optimal process parameter combination, effectively improving process design efficiency and reducing sample development costs.

[0061] This application encompasses any alternatives, modifications, equivalents, and solutions that are not within the spirit and scope of this application. To provide a thorough understanding of this application, specific details are described in detail in the following preferred embodiments of this application, but those skilled in the art will be able to fully understand this application without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of this application, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0062] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0063] The above is only a preferred embodiment of the present application. 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 application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0064] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

Claims

1. A forging process optimization system based on digital twin, comprising: The physical entity acquisition module and the digital twin system are characterized in that the physical entity acquisition module includes a data acquisition module, which is used to collect information data during the operation of the physical entity acquisition module. The physical entity acquisition module also includes a material ratio module, which is used for various material ratios in the forging process. The physical entity acquisition module also includes a network information acquisition and transmission module, which is used to collect and transmit forging process information on the network. The various information in the data acquisition module and the material ratio module are transmitted to the digital twin system through the network transmission system, a virtual simulation of the physical entity is established in the digital twin system, and a dynamic simulation of the forging process is performed to realize virtual-real interactive mapping.

2. The forging process optimization system based on digital twin according to claim 1 is characterized in that: The digital twin system includes a forging process simulation module, a forging information storage module and a forging information comparison module. The forging process simulation module is used to intelligently optimize and adjust the key process parameters of forging, and feed back the optimization plan to the production process to guide actual production operation and improve the closed-loop optimization management of the production process. The forging information storage module is used to save the data after the forging process optimization simulation. The forging information comparison module is used to compare the various data of the forging after actual forging with the various data of the simulated forging, and feed back the data to the digital twin system model.

3. The forging process optimization system based on digital twin according to claim 1 is characterized in that: The network transmission system includes a production workshop transmission network and corresponding network routing, data communication interface, human-computer interaction interface and cloud database; network transmission supports transmission protocols of local area network, LAN, WiFi, Zigbee, Bluetooth, 5G, RFID and GPS; through standardized access to various physical resources through networks and interfaces, they are virtualized into resource nodes in the network to achieve ubiquitous interconnection of physical entity information in the production workshop and interactive mapping with virtual space.

4. The forging process optimization system based on digital twinning according to claim 1 is characterized in that: The digital twin system enables the creation and dynamic display of forging twin models, data fusion analysis, and intelligent optimization of process parameters. Through the network and interfaces of the network transmission system, a mutual mapping relationship is established between all work-in-progress and manufacturing resources of the physical entity system, enabling virtual simulation modeling and display of the forging process and its manufacturing resources, as well as data interaction with other modules of the digital twin system, thus achieving information exchange and closed-loop optimization. The virtual simulation of the forging process is processed using Procast and Deform software to predict possible quality defects in the forming process and provide corresponding improvement measures. This realizes the interactive mapping between virtual space and physical entities, facilitating offline operation training and online production guidance.

5. The forging process optimization system based on digital twin according to claim 2 is characterized in that: The information in the forging information storage module and the forging information comparison module needs to be cleaned and integrated to remove data noise and interference and retain useful signals. Then, the data is standardized and discretized through data normalization processing to form a unified data system for subsequent data mining processing.

6. A forging process optimization method based on digital twin, characterized in that: The forging process optimization system based on digital twinning applied to any one of claims 1 to 5 above comprises the following steps: Step 1: Load the metal to be forged and prepare to heat it. Heat the metal to be forged to the temperature required by the metal and then keep it warm. Step 2: Preheat the mold required for forging by using one or more of the following methods: heat transfer from liquid metal, electric heating, and far-infrared heating. A laser thermometer is used to collect the temperature of the metal to be forged and the temperature of the mold in real time, and the data is transmitted to a data acquisition module. Step 3: Place the heated forging into a forging die, and then forge the forging inside the die using a press. The forging pressure uses a hydraulic system, a pneumatic system, or a combination of the two. Step 4: heat treating the forging; Step 5: Surface treatment is performed on the forged metal. The forged metal parts are then inspected and various performance data of the metal are tested. The tested data are then entered into the forging information storage module for storage. In step six, the data detected in step five is compared with the data simulated by the digital twin system model, and then the information is transmitted to the digital twin system for analysis and processing. The forging process parameters are quickly optimized based on the parameter information to obtain the optimal process parameter combination, and the parameter combination is fed back to the production equipment and process control system.

7. The forging process optimization method based on digital twinning according to claim 6 is characterized in that: After the forging in step three is completed, cooling water is injected into the upper water tank of the upper mold and the lower water tank of the lower mold to quickly cool the forging; during the forging process, the real-time slider speed, impact force and holding time are collected through relevant sensing equipment and transmitted to the digital twin system for analysis and processing.

8. The forging process optimization method based on digital twinning according to claim 6 is characterized in that: Five parameters, namely metal forging temperature, die temperature, slide speed, impact force and production cycle, were selected, and three parameters, namely hardness, tensile yield strength and elongation, were taken as optimization targets. The optimization model of process parameters was constructed as follows: Where x1 is the metal forging temperature, x2 is the die temperature, x3 is the slider speed, x4 is the impact force, and x5 is the production cycle; b i and a i They correspond to the input variables x i The upper and lower limits of the value are i=1,2,...,5; f1(x) is the objective function of forging hardness, f2(x) is the objective function of forging tensile yield, and f3(x) is the objective function of forging elongation. A weighted combination method is used to assign different weights to each objective function to eliminate the influence of different dimensions. The multi-objective optimization problem is converted into a single-objective optimization problem through the weight coefficient α. The unified objective function after conversion is as follows: F(x)=min[f1(x),f2(x),f3(x)]=α1f1(x)+α2f2(x)+α3f3(x); Where α i is the weight coefficient of the single target output value, i=1,2,...,5, and its value is determined by the following relationship: A i =0.5×1 / Δ f i 2 (x), where Where c and d are the single target output values ​​f i (x) the range of variation; Based on the selected parameters and optimization goals, and according to the relevant data collected by the data perception system, an intelligent algorithm is used to find the optimal solution.

9. The forging process optimization method based on digital twinning according to claim 8 is characterized in that: The optimization using intelligent algorithm is to use improved particle swarm optimization BP neural network algorithm to perform parameter optimization.

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