CN116776561A - Digital twin model construction method in product processing process - Google Patents
Digital twin model construction method in product processing process Download PDFInfo
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- CN116776561A CN116776561A CN202310597205.1A CN202310597205A CN116776561A CN 116776561 A CN116776561 A CN 116776561A CN 202310597205 A CN202310597205 A CN 202310597205A CN 116776561 A CN116776561 A CN 116776561A
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- 238000012545 processing Methods 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 75
- 230000008569 process Effects 0.000 title claims abstract description 49
- 238000010276 construction Methods 0.000 title claims description 9
- 238000004519 manufacturing process Methods 0.000 claims abstract description 29
- 238000004088 simulation Methods 0.000 claims abstract description 15
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 238000012795 verification Methods 0.000 claims abstract description 8
- 238000012423 maintenance Methods 0.000 claims abstract description 6
- 238000013179 statistical model Methods 0.000 claims abstract description 6
- 239000002994 raw material Substances 0.000 claims description 14
- 238000013461 design Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 238000003754 machining Methods 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000011958 production data acquisition Methods 0.000 claims 1
- 238000012360 testing method Methods 0.000 abstract description 2
- 239000000047 product Substances 0.000 description 24
- 239000012467 final product Substances 0.000 description 5
- 238000013135 deep learning Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000003801 milling Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000007514 turning Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
- G06F16/2365—Ensuring data consistency and integrity
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The application discloses a method for constructing a digital twin model in a product processing process, which comprises the following steps of: step 1: collecting production data; step 2: preparing data; step 3: establishing a digital twin model; step 4: model verification; step 5: model application; step 6: model updating and maintenance. The application can construct a model based on a physical model, a simulation model, a statistical model and experimental data, can rapidly test the influence of the processing process under different conditions, provides a decision support and an optimization method, improves the processing efficiency of products, optimizes the processing parameters and the process, reduces the production cost, improves the product quality, improves the controllability and the safety of the processing process, and reduces the risks of production accidents and human misoperation.
Description
Technical Field
The application relates to the technical field of digital twin model construction, in particular to a digital twin model construction method in a product processing process.
Background
Conventional manufacturing often consumes significant amounts of time and resources during product design and processing, and it is difficult to predict the performance and quality of the final product. The development of digital technology brings new opportunities to the manufacturing industry so that factories can optimize product design and processing procedures through digital simulation, improving efficiency and quality. Thus, digital simulation has become one of the important technologies in modern manufacturing.
Digital simulation is now widely used in a variety of fields, including product manufacturing processes. Product processing refers to the process of converting a raw material into a final product, typically comprising multiple steps, such as milling, turning, drilling, etc. In conventional processes, multiple trials and adjustments are often required to obtain the final product. And by adopting a digital simulation method, different processing schemes can be rapidly simulated in a virtual environment, and the performance and quality of each scheme can be evaluated through simulation analysis. Therefore, the experimental period can be greatly shortened, the cost is reduced, and the production efficiency is improved.
However, the application of digital simulation in the processing of products also has the problem of accurately describing the deformations and deformations of the raw materials in the processing, since in the actual production process, the raw materials are often subjected to various factors, such as temperature, pressure, mechanical load, etc., which lead to deformations and deformations, which have an important influence on the properties and quality of the final product. Therefore, a new solution is needed.
Disclosure of Invention
The application aims to provide a digital twin model construction method in the product processing process, which solves the problem that raw materials are often influenced by various factors, such as temperature, pressure, mechanical load and the like, in the actual production process, so that the raw materials deform and deform, and the deformation has important influence on the performance and quality of a final product.
In order to achieve the above purpose, the present application provides the following technical solutions: the digital twin model construction method for the product processing process comprises the following steps:
step 1: collecting production data;
step 2: preparing data;
step 3: establishing a digital twin model;
step 4: model verification;
step 5: model application;
step 6: model updating and maintenance.
As a preferred embodiment of the present application, the production data collection in the step 1 includes production equipment, raw materials, and sensor signals, wherein the production equipment data includes processing time, processing temperature, and processing speed, the raw material data includes density, thermal expansion coefficient, thermal conductivity, heat capacity, elastic modulus, and yield strength, and the sensor signals include a temperature and humidity sensor, a pressure sensor, a displacement sensor, a force sensor, a current sensor, an optical sensor, a vibration sensor, and a sound sensor.
As a preferred embodiment of the present application, the data preparation in step 2 needs to be cleaned and processed after the data is acquired, so as to remove abnormal data, empty data, etc. And meanwhile, the data are standardized and normalized so as to ensure the consistency and accuracy of the data.
As a preferred embodiment of the application, the establishing of the digital twin model in the step 3 is implemented by inputting the processed data into a digital twin system, and establishing the digital twin model according to the actual processing process condition, wherein the digital twin model comprises a physical model, a simulation model and a statistical model.
As a preferred embodiment of the present application, the model verification in the step 4 verifies the accuracy and reliability of the digital twin model by comparing with the actual machining process. If there is an error in the model, model optimization and reconstruction are required.
As a preferred embodiment of the application, the model application in the step 5 uses a digital twin model for simulation and optimization, so that different processing conditions and parameters can be simulated, an optimal processing scheme can be found, corresponding process parameters can be output, and meanwhile, important parameters such as stress, strain, temperature and the like generated in the processing process can be calculated.
As a preferred embodiment of the application, in the step 6, the model updating and maintenance are performed, the deformation and deformation conditions of the raw materials in the processing process are predicted through the digital twin model, the product design and the processing process can be optimized, the efficiency and the quality are improved, and the digital twin model needs to be updated and optimized periodically along with the change of various conditions in the processing process of the product, so that the accuracy and the adaptability of the model are ensured.
Compared with the prior art, the application has the following beneficial effects:
the application can construct a model based on a physical model, a simulation model, a statistical model and experimental data, describes the processing process of a product by using a physical equation and knowledge, obtains the output result of the model by solving through a numerical method, can accurately simulate the processing process, and has important significance for the design and optimization of a controller; the digital twin model is built by using machine learning or deep learning and other methods, so that the model can be automatically learned from data, more accurate model representation can be obtained, a model result can be quickly obtained, and the method is suitable for large-scale data processing and complex processing modeling; a series of experiments are carried out in a laboratory environment to obtain data related to a product processing process, a digital twin model is constructed based on the data, real processing process data can be obtained, errors caused by the fact that the model is inconsistent with actual conditions are reduced, accuracy of the digital twin model can be improved, a computer simulation software is used for modeling and simulating the product processing process to generate the digital twin model, influences of the processing process can be rapidly tested under different conditions, decision support and optimization methods are provided, product processing efficiency is improved, processing parameters and processes are optimized, production cost is reduced, product quality is improved, controllability and safety of the processing process are improved, and risks of production accidents and artificial misoperation are reduced.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a technical scheme that: the digital twin model construction method for the product processing process comprises the following steps:
step 1: collecting production data; the method comprises the steps of producing equipment, raw materials and sensor signals, wherein the data of the production equipment comprise processing time, processing temperature and processing speed, the data of the raw materials comprise density, thermal expansion coefficient, thermal conductivity, heat capacity, elastic modulus and yield strength, the sensor signals comprise a temperature and humidity sensor, a pressure sensor, a displacement sensor, a force sensor, a current sensor, an optical sensor, a vibration sensor and a sound sensor, the acquisition method and details are generally determined for the purpose and the range of data needing to be acquired, the acquisition method and the details are generally required to be acquired definitely, the sensors can be installed on the equipment for acquiring real-time operation data of the processing equipment, the data are transmitted to a background database through a computer network, and the aspects of quality, integrity, safety and the like of the data are also required to be paid attention.
Step 2: preparing data; after the data preparation is carried out, cleaning and processing are required to be carried out, abnormal data, vacant data and the like are removed, and meanwhile, standardization and normalization processing are carried out on the data so as to ensure the consistency and the accuracy of the data, wherein the data processing is the basis of a digital twin model and comprises a plurality of steps of data mining, data processing, data quality analysis and the like; proper tools and techniques, such as Python, R, SQL, need to be selected to process and analyze the data, and when cleaning the data, the characteristics of various data contents, such as data type, precision, error range, etc., need to be paid attention to ensure high data quality
Step 3: establishing a digital twin model; inputting the processed data into a digital twin system, and establishing a digital twin model according to the actual processing process condition, wherein the digital twin model comprises a physical model, a simulation model and a statistical model, and the establishment of the model is a core link for establishing the digital twin model. Modeling may be performed using various methods, such as techniques based on physical equations, machine learning, deep learning, data mining, and the like. And a proper method is required to be selected according to actual conditions to carry out model design.
Step 4: model verification; and comparing with the actual machining process to verify the accuracy and reliability of the digital twin model. If the model has errors, model optimization and reconstruction are needed, the verification of the digital twin model needs to take the sources, the testing method, the index setting and the like of verification data into consideration, a data comparison method is suggested to be adopted for comparing the digital twin model with the operation data of an actual processing system so as to evaluate the accuracy and the reliability of the model, and the model is found to have errors and needs to be optimized and adjusted so as to be more in line with the actual processing process.
Step 5: model application; the digital twin model is used for simulation and optimization, different processing conditions and parameters can be simulated, an optimal processing scheme can be found, corresponding technological parameters are output, and important parameters such as stress, strain, temperature and the like generated in the processing process can be calculated.
Step 6: model updating and maintenance, namely predicting deformation and deformation conditions of raw materials in the processing process through a digital twin model, optimizing the product design and the processing process, improving the efficiency and the quality, and along with the change of various conditions in the processing process of the product, periodically updating and optimizing the digital twin model to ensure the accuracy and the adaptability of the model, wherein the updating of the digital twin model is usually continuous, can be periodically updated according to actual conditions, and needs to pay attention to the safety and confidentiality problems of data.
Compared with the prior art, the application has the following beneficial effects:
the application can construct a model based on a physical model, a simulation model, a statistical model and experimental data, describes the processing process of a product by using a physical equation and knowledge, obtains the output result of the model by solving through a numerical method, can accurately simulate the processing process, and has important significance for the design and optimization of a controller; the digital twin model is built by using machine learning or deep learning and other methods, so that the model can be automatically learned from data, more accurate model representation can be obtained, a model result can be quickly obtained, and the method is suitable for large-scale data processing and complex processing modeling; a series of experiments are carried out in a laboratory environment to obtain data related to a product processing process, a digital twin model is constructed based on the data, real processing process data can be obtained, errors caused by the fact that the model is inconsistent with actual conditions are reduced, accuracy of the digital twin model can be improved, a computer simulation software is used for modeling and simulating the product processing process to generate the digital twin model, influences of the processing process can be rapidly tested under different conditions, decision support and optimization methods are provided, product processing efficiency is improved, processing parameters and processes are optimized, production cost is reduced, product quality is improved, controllability and safety of the processing process are improved, and risks of production accidents and artificial misoperation are reduced.
While the fundamental and principal features of the application and advantages of the application have been shown and described, it will be apparent to those skilled in the art that the application is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The last points to be described are: first, in the description of the present application, it should be noted that, unless otherwise specified and defined, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be mechanical or electrical, or may be a direct connection between two elements, and "upper," "lower," "left," "right," etc. are merely used to indicate relative positional relationships, which may be changed when the absolute position of the object being described is changed.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present application has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A digital twin model construction method in the product processing process is characterized in that: the digital twin model construction of the product processing process comprises the following steps:
step 1: collecting production data;
step 2: preparing data;
step 3: establishing a digital twin model;
step 4: model verification;
step 5: model application;
step 6: model updating and maintenance.
2. The method for constructing a digital twin model of a product manufacturing process according to claim 1, wherein: the production data acquisition in the step 1 comprises production equipment, raw materials and sensor signals, wherein the production equipment data comprises processing time, processing temperature and processing speed, the raw material data comprises density, thermal expansion coefficient, thermal conductivity, heat capacity, elastic modulus and yield strength, and the sensor signals comprise a temperature and humidity sensor, a pressure sensor, a displacement sensor, a force sensor, a current sensor, an optical sensor, a vibration sensor and a sound sensor.
3. The method for constructing a digital twin model of a product manufacturing process according to claim 1, wherein: and 2, after the data preparation in the step is carried out to obtain the data, cleaning and processing are needed to remove abnormal data, vacant data and the like. And meanwhile, the data are standardized and normalized so as to ensure the consistency and accuracy of the data.
4. The method for constructing a digital twin model of a product manufacturing process according to claim 1, wherein: and 3, establishing a digital twin model, namely inputting processed data into a digital twin system, and establishing the digital twin model according to the actual processing process condition, wherein the digital twin model comprises a physical model, a simulation model and a statistical model.
5. The method for constructing a digital twin model of a product manufacturing process according to claim 1, wherein: and (3) comparing the model verification in the step (4) with the actual machining process to verify the accuracy and reliability of the digital twin model. If there is an error in the model, model optimization and reconstruction are required.
6. The method for constructing a digital twin model of a product manufacturing process according to claim 1, wherein: the model application in the step 5 uses a digital twin model for simulation and optimization, so that different processing conditions and parameters can be simulated, an optimal processing scheme can be found, corresponding process parameters can be output, and important parameters such as stress, strain, temperature and the like generated in the processing process can be calculated.
7. The method for constructing a digital twin model of a product manufacturing process according to claim 1, wherein: and in the step 6, model updating and maintenance are performed, deformation and deformation conditions of raw materials in the processing process are predicted through the digital twin model, the product design and the processing process can be optimized, the efficiency and the quality are improved, and the digital twin model needs to be updated and optimized periodically along with the change of various conditions in the product processing process so as to ensure the accuracy and the adaptability of the model.
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| CN202310597205.1A CN116776561A (en) | 2023-05-25 | 2023-05-25 | Digital twin model construction method in product processing process |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110704974A (en) * | 2019-09-30 | 2020-01-17 | 江苏科技大学 | Modeling and using method of process model based on digital twin drive |
| CN110900307A (en) * | 2019-11-22 | 2020-03-24 | 北京航空航天大学 | Numerical control machine tool cutter monitoring system driven by digital twin |
| CN111161410A (en) * | 2019-12-30 | 2020-05-15 | 中国矿业大学(北京) | Mine digital twinning model and construction method thereof |
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- 2023-05-25 CN CN202310597205.1A patent/CN116776561A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110704974A (en) * | 2019-09-30 | 2020-01-17 | 江苏科技大学 | Modeling and using method of process model based on digital twin drive |
| CN110900307A (en) * | 2019-11-22 | 2020-03-24 | 北京航空航天大学 | Numerical control machine tool cutter monitoring system driven by digital twin |
| CN111161410A (en) * | 2019-12-30 | 2020-05-15 | 中国矿业大学(北京) | Mine digital twinning model and construction method thereof |
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