Large aerospace component hot working quality and energy consumption cooperative control method and system

Abstract

The invention belongs to the field of emitters, and discloses a large aerospace component hot working quality and energy consumption cooperative control method and system, and the method comprises the steps: collecting data to form an equipment energy consumption feature data set; setting a plurality of groups of multi-pass process parameters, performing simulation calculation to obtain corresponding quality data, and forming a quality feature data set; respectively inputting the energy consumption characteristic data set and the quality characteristic data set into a to-be-trained adaptive fuzzy neural network for training until the trained model converges, and obtaining an energy consumption prediction model and a quality prediction model; respectively inputting the data into the two models, and predicting a standard value of energy consumption required by a processing component to obtain a quality prediction value of a hot processing component product; and the particle swarm optimization is used for adjusting and optimizing the process of the subsequent process, so that the cooperative control of the quality and energy consumption of the hot-working component is realized. By means of the method, flexible control over the whole forging process of the large aerospace component can be achieved, the product quality and energy consumption are stabilized, and the production benefits of factories are improved.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of launcher technology, and in particular relates to a method and system for coordinated control of thermal processing quality and energy consumption of large aerospace components. Background Art

[0002] Large aerospace components such as aircraft landing gear and rocket engine heads need to have extremely high strength, toughness and other properties. They are generally forged using large-tonnage forging presses. The entire manufacturing process includes: billet making, die forging and heat treatment, of which billet making and die forging include heating, forging and insulation processes. Forgings undergo repeated heating and cooling and large-scale deformation during the manufacturing process, and the process is complex. At the same time, the corresponding processing equipment is large in size, which makes it difficult to stabilize the actual process during the production process, and is prone to fluctuations, which in turn affects the final quality of the forgings.

[0003] The manufacturing of large forgings is also a high energy consumption process. Currently, forging plants lack management of production energy consumption and it is difficult to actively and effectively control energy consumption.

[0004] Quality and cost are the main criteria for factories to judge the quality of product production, and energy costs account for a large proportion of forging costs. Due to the large fluctuations in quality and energy consumption during the forging process of large aerospace components at this stage, when the quality is not up to standard, it will also lead to additional work such as rework and repair, which will further increase costs.

[0005] In view of the above analysis, the technical problems that need to be solved urgently in the prior art are:

[0006] The factory urgently needs to dynamically control the entire process of hot processing of large components to ensure the stability of product quality and energy consumption.

[0007] Chinese patent CN115358889A discloses an industrial device energy consumption control method based on a back propagation (BP) neural network, including: collecting the historical energy consumption information and influencing factor information of the industrial device and their corresponding historical data to form an energy consumption characteristic data set; inputting the energy consumption characteristic data set into the BP neural network model to be trained for training until the trained BP neural network model converges to obtain a trained BP neural network model; inputting the collected real-time energy consumption data of the industrial device into the trained BP neural network model for operation to obtain the predicted value of the energy consumption of the industrial device; when the absolute value of the deviation between the predicted energy consumption value and the actual energy consumption value is greater than the set deviation fluctuation value, the industrial device performs an abnormal alarm. This method can use the industrial device energy consumption prediction model to alarm the abnormal energy consumption performance in production, but it cannot automatically adjust the equipment to return the energy consumption to normal, and the degree of intelligence is insufficient.

[0008] Chinese patent CN118034212A discloses a forging process optimization system and optimization method based on digital twin, including: a physical entity acquisition module and a digital twin system, wherein the physical entity acquisition module includes a data acquisition module, and the data acquisition module is used for information data during the operation of the physical entity acquisition module. This patent integrates digital twin technology into the forging process, and guides the process optimization process by comparing the performance of the metal after actual forging with the performance after digital twin simulation forging. The above optimization method updates the process parameters based on the quality after metal processing, and it is impossible to control the process in production to control the final quality. Due to the large fluctuations in the production process of large aerospace forgings, the quality fluctuations of each product are also large. At the same time, if the quality does not meet the standards, it will face high rework or scrap costs. The above method cannot dynamically adjust the production of large forgings in a highly volatile production environment. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method and system for coordinated control of thermal processing quality and energy consumption of large aerospace components.

[0010] The present invention is implemented as follows: a method for collaboratively controlling the thermal processing quality and energy consumption of large aerospace components, characterized in that the method for collaboratively controlling the thermal processing quality and energy consumption of large aerospace components specifically comprises:

[0011] S1: Collect energy consumption data and process data of each device during the thermal processing of aerospace components, and combine them with existing historical energy consumption data to form an equipment energy consumption characteristic data set;

[0012] S2: Import the aerospace component model and mold model into the finite element simulation software. Based on the actual hot working process, set multiple sets of multi-pass process parameters to simulate and calculate the corresponding quality data. Combine the quality inspection data of each batch of components and the corresponding process data to form a quality feature data set.

[0013] S3: inputting the energy consumption characteristic data set and the quality characteristic data set into the adaptive fuzzy neural network to be trained respectively, until the trained model converges, and obtaining the energy consumption prediction model of each hot processing equipment and the multi-pass quality prediction model of the hot processing component;

[0014] S4: input the preset hot working process data into the energy consumption prediction model to obtain the standard energy consumption prediction value of each device in the hot working process of aerospace components, and predict the standard value of energy consumption required for processing components according to the standard hot working process route data, which is used as a reference value for energy consumption in actual processing;

[0015] S5: input the real-time process data in the actual hot processing production into the quality prediction model to obtain the quality prediction value of the hot processing component product;

[0016] S6: Based on whether the quality prediction value meets the quality requirements and the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value, the particle swarm algorithm is used to adjust and optimize the process of subsequent processes to achieve coordinated control of the quality and energy consumption of hot-processed components.

[0017] Furthermore, in S1, the hot working process includes billet making, die forging and heat treatment; the energy consumption data and process data of each equipment include the input power, pressing speed, pressing force, die temperature, component temperature of the forging press, the input power or gas consumption rate, heating rate, and temperature change curve in the furnace of the heating furnace or heat treatment furnace.

[0018] Furthermore, the S2, quality data includes the grain size, yield strength, tensile strength, etc. of the final product of the hot-processed component; the process data includes the forging rate of multiple forging passes, the component temperature before forging, the mold temperature, the heat treatment heating rate, the holding temperature, the holding time, etc.

[0019] Further, the energy consumption prediction model in S3 is established for different hot processing equipment, including: free forging press, die forging press, pre-forging heating furnace and heat treatment furnace. The input of the model is the equipment process data of the time series, and the output is the equipment power / gas consumption rate of the time series, which can be converted into the total power consumption / gas consumption of the equipment in a period of time after processing. The prediction model can simulate and calculate the input power or gas consumption rate corresponding to the operation of the equipment according to a series of set process parameters in actual production;

[0020] Quality prediction models are established for different output targets, including multiple prediction models with quality indicators such as grain size, yield strength, and tensile strength as output values. The model input is the full-process process parameter matrix. The model establishes the relationship between the final quality of the forging and the complete hot working process technology.

[0021] Furthermore, in S6, when it is predicted that the final quality of the component is unqualified, the particle swarm algorithm is used to perform multi-objective optimization on the subsequent process, the process parameters are updated through the algorithm, and the process parameters are input into the quality and energy consumption prediction model, and the process parameters are updated with the final quality prediction of the component meeting the standard and the minimum energy consumption as the goal feedback, and finally the optimization is achieved. The objective function can be expressed as:

[0022]

[0023] Among them, X represents the multidimensional process matrix composed of process parameters, Q i (X) indicates the predicted values ​​of different qualities, a i ~b i It represents the required range of different qualities, and E(X) represents the total energy consumption of the whole processing process, which is the sum of the energy consumption forecasts of each equipment.

[0024] Another object of the present invention is to provide a large-scale aerospace component thermal processing quality and energy consumption coordinated control system, the system specifically comprising:

[0025] Data acquisition module, which collects and processes data to build equipment energy consumption characteristic data sets and quality characteristic data sets;

[0026] Energy consumption prediction module, based on the energy consumption prediction model, obtains the standard energy consumption prediction value of each equipment in the hot processing of aerospace components;

[0027] The quality prediction module obtains the quality prediction value of the hot-processed component product based on the quality prediction model;

[0028] The optimization module uses particle swarm algorithm to perform multi-objective optimization on subsequent processes.

[0029] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0030] First, the present invention establishes an energy consumption prediction model for the equipment through the operation data and energy consumption data of the equipment in actual production. The model can predict the energy consumption of the equipment under different processes. Through multiple equipment energy consumption models, the energy consumed in the entire product processing process can be predicted.

[0031] The present invention integrates the process data and quality inspection results in actual production with the simulation process data and performance results based on the material performance evolution mechanism, and establishes a quality prediction model for finished forgings. The model can predict the final forging quality based on the process parameters of each processing stage.

[0032] The present invention can achieve quality and energy consumption management of the whole process of hot processing with the help of quality and energy consumption prediction models. By comparing the energy consumption prediction results with the energy consumption of the equipment monitored during actual processing, the energy consumption fluctuation situation can be judged. By comparing the quality prediction results with the required quality, the quality fluctuation situation can be judged. Based on the energy consumption and quality fluctuation situation, the subsequent processes are combined and optimized with the goals of achieving quality standards and minimizing energy consumption.

[0033] With the help of the present invention, the entire forging process of large-scale aerospace components can be flexibly controlled, product quality and energy consumption can be stabilized, and factory production efficiency can be improved.

[0034] Second, the present invention provides a system for the coordinated control of the quality and energy consumption of thermal processing of large aerospace components, which realizes the dual optimization of quality control and energy consumption management in the thermal processing process by integrating modules such as data acquisition, finite element simulation, adaptive fuzzy neural network training, energy consumption and quality prediction, and optimization control.

[0035] The data acquisition unit collects the energy consumption data and process parameters of each thermal processing equipment in real time through sensors and data interfaces, and combines historical data to form a comprehensive equipment energy consumption characteristic data set.

[0036] The finite element simulation module uses finite element analysis software to simulate and calculate multiple sets of process parameters for aerospace components and mold models, generate corresponding quality data, and combine it with actual quality inspection data to construct a quality feature data set.

[0037] The adaptive fuzzy neural network training module generates energy consumption prediction model and quality prediction model respectively by training the adaptive fuzzy neural network, ensuring the high accuracy and reliability of the prediction results.

[0038] The energy consumption prediction module generates standard energy consumption prediction values ​​for each device based on preset process parameters using the energy consumption prediction model, providing a reference benchmark for energy consumption management in actual production.

[0039] The quality prediction module inputs the quality prediction model through real-time process data to generate the quality prediction value of the component to ensure quality control during the hot working process.

[0040] The optimization control unit comprehensively considers the deviation of quality and energy consumption, and dynamically optimizes and adjusts the process parameters through the particle swarm algorithm to achieve coordinated control of quality and energy consumption in the hot working process, thereby improving production efficiency and economic benefits.

[0041] The system for coordinated control of thermal processing quality and energy consumption of large aerospace components of the present invention has the following significant technical effects in industrial applications:

[0042] 1. Achieve dual optimization of quality and energy consumption: By integrating quality prediction and energy consumption prediction models, the system can optimize process parameters, reduce energy consumption and improve production efficiency while ensuring component quality.

[0043] 2. Improve the accuracy and reliability of predictions: Adaptive fuzzy neural network is used for model training, combined with finite element simulation and historical data, which significantly improves the accuracy of energy consumption and quality predictions and reduces prediction errors.

[0044] 3. Realize real-time monitoring and dynamic adjustment: The system can monitor the process parameters and equipment energy consumption during the hot processing process in real time, and optimize the dynamic adjustment of the control unit to achieve real-time optimization of the production process to ensure the efficiency and stability of the production process.

[0045] 4. Reduce production costs and energy consumption: By optimizing process parameters, the system effectively reduces energy consumption during thermal processing, reduces production costs, improves resource utilization, and meets the requirements of green production and sustainable development.

[0046] 5. Enhance the intelligence and automation level of the system: Integrate multiple intelligent modules to realize the automation of the entire process from data collection, prediction to optimization control, reduce manual intervention, and improve the intelligence level and operational efficiency of the system.

[0047] 6. Adapt to the complex processing requirements of large aerospace components: The system is designed according to the thermal processing characteristics of large aerospace components, and can handle complex process requirements with multiple passes and multiple parameters, ensuring the quality and energy consumption management of components during high-strength and high-precision processing.

[0048] Through the above technical scheme, the present invention effectively solves the problem of separation of quality control and energy consumption management in the existing hot processing process of large aerospace components, realizes the coordinated optimization of the two, significantly improves the overall efficiency and economic benefits of the hot processing process, and has broad industrial application prospects and significant technological progress.

[0049] The expected benefits and commercial value of the technical solution of the present invention after transformation are as follows: the present invention will ensure the stability of the production quality and energy consumption of large aerospace forgings, help reduce the production costs of enterprises, and have significant economic benefits. The aerospace hot processing component quality and energy consumption coordinated control system provided by the present invention will promote the transformation and upgrading of the traditional aerospace hot processing industry to high quality, high efficiency and low consumption.

[0050] Third, the method for coordinated control of quality and energy consumption of thermal processing of large aerospace components provided by the present invention effectively solves several major technical problems in the prior art by dual prediction and control of energy consumption and quality, and achieves significant technical progress:

[0051] 1. Accurately control energy consumption and reduce waste

[0052] In the prior art, the control of equipment energy consumption during hot processing lacks accurate prediction and optimization means, which often leads to energy waste. The present invention collects equipment energy consumption characteristic data, combines adaptive fuzzy neural network model and particle swarm optimization algorithm, realizes real-time prediction and dynamic adjustment of energy consumption, thereby greatly reducing energy consumption fluctuations, reducing unnecessary energy consumption, and significantly improving energy saving effects.

[0053] 2. Improve the stability and consistency of component quality

[0054] In traditional hot processing, the problem of unstable quality often occurs due to inaccurate control of process parameters. The present invention realizes real-time monitoring of product quality through the quality prediction model, and can adjust subsequent process parameters in time when deviations are found. This real-time quality optimization control ensures the stability and consistency of component quality, reduces the product failure rate caused by quality fluctuations, and improves processing quality.

[0055] 3. Reduce production costs and improve economic benefits

[0056] The present invention optimizes the hot processing parameters through the standard energy consumption prediction model and the quality prediction model, reduces unnecessary energy consumption and material waste, and directly reduces the production cost. In addition, the reduced energy consumption and optimized process parameters also shorten the processing time, improve the equipment utilization rate, and bring significant economic benefits to the enterprise.

[0057] 4.Automation and intelligent control of process parameters

[0058] The solution of the present invention relies on adaptive fuzzy neural networks and particle swarm algorithms to achieve intelligent control of process parameters, so that the heat treatment process is transformed from relying on manual experience to relying on intelligent system control. It is easy to operate and reduces human errors, making the production process more efficient and controllable.

[0059] 5. Improve the adsorption performance and other key performance of hot-processed components

[0060] By optimizing processes such as oxidation baking, the present invention further improves key physical properties such as the adsorption performance of the components, meets the strict requirements of high-precision aerospace equipment, thereby expanding the application range of the product and enhancing the competitiveness of the product in the aerospace field.

[0061] 6. Promote efficient use of resources and environmentally friendly production

[0062] The invention realizes the resource utilization of waste gas and the optimized management of energy consumption, conforms to the development direction of energy conservation and emission reduction, promotes the realization of environmentally friendly production, and demonstrates the advantages of sustainable development. In the manufacturing of aerospace components, this environmentally friendly and efficient solution significantly improves the sustainability of the production process.

[0063] Through the above technical scheme, the present invention effectively solves the problems of energy waste, unstable quality, high production cost and so on in the prior art, and achieves significant technological progress. It not only promotes the upgrading of aerospace component production technology, but also enhances the market competitiveness of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of a method for coordinated control of thermal processing quality and energy consumption of large aerospace components provided by an embodiment of the present invention;

[0065] Figure 2 is a schematic diagram of the structure of an adaptive fuzzy neural network provided by an embodiment of the present invention;

[0066] Figure 3 It is a module diagram for coordinated control of thermal processing quality and energy consumption of large aerospace components provided by an embodiment of the present invention.

[0067] Figure 4 This is the hot working flow chart of the die forging.

[0068] Figure 5 Plot of the iterative optimization prediction results for the average grain size.

[0069] Figure 6 Graph showing iterative optimization prediction results for total energy consumption. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0071] like Figure 1 As shown, an embodiment of the present invention provides a method for coordinated control of thermal processing quality and energy consumption of large aerospace components, the method specifically comprising:

[0072] S1: Collect energy consumption data and process data of each device during the thermal processing of aerospace components, and combine them with existing historical energy consumption data to form an equipment energy consumption characteristic data set;

[0073] S2: Import the aerospace component model and mold model into the finite element simulation software. Based on the actual hot working process, set multiple sets of multi-pass process parameters to simulate and calculate the corresponding quality data. Combine the quality inspection data of each batch of components and the corresponding process data to form a quality feature data set.

[0074] S3: inputting the energy consumption characteristic data set and the quality characteristic data set into the adaptive fuzzy neural network to be trained respectively, until the trained model converges, and obtaining the energy consumption prediction model of each hot processing equipment and the multi-pass quality prediction model of the hot processing component;

[0075] S4: input the preset hot working process data into the energy consumption prediction model to obtain the standard energy consumption prediction value of each device in the hot working process of aerospace components, and predict the standard value of energy consumption required for processing components according to the standard hot working process route data, which is used as a reference value for energy consumption in actual processing;

[0076] S5: input the real-time process data in the actual hot processing production into the quality prediction model to obtain the quality prediction value of the hot processing component product;

[0077] S6: Based on whether the quality prediction value meets the quality requirements and the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value, the particle swarm algorithm is used to adjust and optimize the process of subsequent processes to achieve coordinated control of the quality and energy consumption of hot-processed components.

[0078] An embodiment of the present invention provides a method for coordinated control of thermal processing quality and energy consumption of large aerospace components, the method specifically comprising:

[0079] S1: Collect energy consumption data and process data of each device during the thermal processing of aerospace components, and combine them with existing historical energy consumption data to form an equipment energy consumption characteristic data set;

[0080] S2: Import the aerospace component model and mold model into the finite element simulation software. Based on the actual hot working process, set multiple sets of multi-pass process parameters to simulate and calculate the corresponding quality data. Combine the quality inspection data of each batch of components and the corresponding process data to form a quality feature data set.

[0081] S3: inputting the energy consumption characteristic data set and the quality characteristic data set into the adaptive fuzzy neural network to be trained respectively, until the trained model converges, and obtaining the energy consumption prediction model of each hot processing equipment and the multi-pass quality prediction model of the hot processing component;

[0082] S4: input the preset hot working process data into the energy consumption prediction model to obtain the standard energy consumption prediction value of each device in the hot working process of aerospace components, and predict the standard value of energy consumption required for processing components according to the standard hot working process route data, which is used as a reference value for energy consumption in actual processing;

[0083] S5: input the real-time process data in the actual hot processing production into the quality prediction model to obtain the quality prediction value of the hot processing component product;

[0084] S6: Based on whether the quality prediction value meets the quality requirements and the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value, the particle swarm algorithm is used to adjust and optimize the process of subsequent processes to achieve coordinated control of the quality and energy consumption of hot-processed components.

[0085] The S1, hot working process includes billet making, die forging and heat treatment; the energy consumption data and process data of each equipment include the input power, pressing speed, pressing force, die temperature, component temperature of the forging press, the input power or gas consumption rate, heating rate, and temperature change curve in the furnace of the heating furnace or heat treatment furnace.

[0086] The S2, quality data includes the grain size, yield strength, tensile strength, etc. of the final product of the hot-processed component; the process data includes the forging rate of multiple forging passes, the component temperature before forging, the die temperature, the heat treatment heating rate, the holding temperature, the holding time, etc.

[0087] The S3, energy consumption prediction model is established for different hot processing equipment, including: free forging press, die forging press, pre-forging heating furnace and heat treatment furnace. The input of the model is the equipment process data of the time series, and the output is the equipment power / gas consumption rate of the time series, which can be converted into the total power consumption / gas consumption of the equipment over a period of time after processing. The prediction model can simulate and calculate the input power or gas consumption rate corresponding to the operation of the equipment according to a series of set process parameters in actual production.

[0088] The quality prediction model is established for different output targets, including multiple prediction models with quality indicators such as grain size, yield strength, and tensile strength as output values. The model input is the matrix of process parameters of the whole process. The model establishes the relationship between the final quality of the forging and the complete hot working process.

[0089] The method for coordinated control of quality and energy consumption of hot working of large aerospace components provided in the embodiment of the present invention aims to achieve dual optimization of quality control and energy consumption management in the hot working process through data-driven prediction and optimization technology. The method ensures that aerospace components meet high quality standards during hot working while minimizing energy consumption and improving production efficiency and economic benefits through four main links: collection, analysis, prediction and optimization.

[0090] First, in step S1: the energy consumption data and process data of each device in the hot working process of aerospace components are collected. The system monitors and records the energy consumption parameters (such as input power, pressing speed, pressing force, mold temperature, component temperature, gas consumption rate, heating rate, etc.) and process parameters of the equipment in real time through sensors installed on various hot working equipment (such as blank making machines, die forging presses, heating furnaces and heat treatment furnaces). These data include not only real-time data, but also historical energy consumption data to form a comprehensive equipment energy consumption characteristic data set. In this way, the system can fully understand the energy consumption performance of each device under different process conditions, providing a reliable data basis for subsequent energy consumption prediction and optimization.

[0091] Next, in step S2: the aerospace component model and the mold model are imported into the finite element simulation software, and the system uses the finite element analysis (FEA) technology to simulate and calculate the actual hot working process. By setting multiple sets of multi-pass process parameters (such as forging rate, pre-forging component temperature, mold temperature, heat treatment heating rate, insulation temperature and insulation time, etc.), the system can simulate the influence of different process parameter combinations on component quality (such as grain size, yield strength, tensile strength, etc.). At the same time, combined with the actual quality inspection data of each batch of components, a quality feature data set is formed. This method of combining simulation with actual data ensures the accuracy and reliability of the quality prediction model, laying a solid foundation for subsequent quality and energy consumption prediction.

[0092] In step S3: the energy consumption characteristic data set and the quality characteristic data set are respectively input into the adaptive fuzzy neural network to be trained. The system uses the advanced adaptive fuzzy neural network (AFNN) for data modeling. Through the training of the energy consumption characteristic data set, the system can establish energy consumption prediction models for each hot processing equipment. These models can accurately predict the power consumption or gas consumption rate of the equipment under different process parameters based on the equipment process data of the time series. At the same time, the training of the quality characteristic data set enables the system to establish multi-pass quality prediction models for hot processing components. These models can predict the key quality indicators of the components based on the complete hot processing process parameters. The training process continues until the model converges to ensure the high accuracy and stability of the prediction results.

[0093] Enter step S4: input the preset heat treatment process data into the energy consumption prediction model. The system uses the trained energy consumption prediction model to predict the standard energy consumption value required for each device during the processing based on the standard heat treatment process route data. These standard energy consumption values ​​serve as a reference for energy consumption in actual production, helping enterprises make scientific decisions when formulating process parameters to ensure that energy consumption is within a reasonable range. At the same time, the system compares these predicted values ​​with the real-time energy consumption data in actual processing to detect energy consumption anomalies in a timely manner and ensure energy-saving and efficient production processes.

[0094] The fuzzy neural network in S4, Figure 2 This is the structural diagram of the fuzzy neural network. The prediction model establishment process includes:

[0095] (1) Fuzzify the input parameters and establish fuzzy rules. According to the complexity and distribution characteristics of the input parameters, select the appropriate membership function and the number of membership functions. The membership function can be Gaussian membership function, triangle membership function, trapezoidal membership function, etc. The number of functions ranges from 2 to 8. The fuzzification operation can be expressed as:

[0096]

[0097] x i represents the i-th input parameter, μ ij (x i ) is the jth membership function of the ith input parameter, is the membership value.

[0098] (2) Fuzzy rule incentive strength calculation: multiply any two memberships of any two input parameters to get the output:

[0099]

[0100] (3) The normalized calculation of the usage of each fuzzy rule ensures that the relative importance of all rules is balanced. The function is:

[0101]

[0102] (4) Calculation of the normalized regular excitation intensity linear output:

[0103]

[0104] (5) Weighted summation and defuzzification:

[0105]

[0106] (6) Fuzzy neural network parameter optimization: The parameters of the membership function in step (1) and the linear function in step (4) are adjusted through the back propagation algorithm to make the prediction model converge.

[0107] In step S5: the real-time process data in the actual hot processing production is input into the quality prediction model. The system monitors the real-time process parameters (such as forging rate, die temperature, heating rate, etc.) and uses the trained quality prediction model to predict the quality indicators of hot processing component products in real time. These predicted values ​​can reflect the impact of current process parameters on component quality, provide a basis for timely adjustment of process parameters during production, and ensure that each batch of products can meet the predetermined quality standards.

[0108] Finally, in step S6: based on whether the quality prediction value meets the quality requirements and the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value, the particle swarm algorithm is used to adjust and optimize the process of the subsequent process. The system uses the particle swarm optimization algorithm (PSO) to dynamically optimize the process parameters. By analyzing the deviation between the quality prediction value and the target quality value, as well as the difference between the actual energy consumption and the standard energy consumption prediction value, the particle swarm algorithm can adjust the process parameters under the framework of multi-objective optimization to achieve the coordinated optimization of quality and energy consumption. This optimization process not only improves the processing quality of components, but also effectively reduces energy consumption, improves overall production efficiency and economic benefits, and ultimately achieves the high quality and energy saving goals in the hot processing of large aerospace components.

[0109] Furthermore, in S6, when it is predicted that the final quality of the component is unqualified, the particle swarm algorithm is used to perform multi-objective optimization on the subsequent process, the process parameters are updated through the algorithm, and the process parameters are input into the quality and energy consumption prediction model, and the process parameters are updated with the final quality prediction of the component meeting the standard and the minimum energy consumption as the goal feedback, and finally the optimization is achieved. The objective function can be expressed as:

[0110]

[0111] Among them, X represents the multidimensional process matrix composed of the whole process parameters, Q i (X) indicates the predicted values ​​of different qualities, a i ~b i It represents the required range of different qualities, and E(X) represents the total energy consumption of the whole processing process, which is the sum of the energy consumption forecasts of each equipment.

[0112] In summary, the method for coordinated control of thermal processing quality and energy consumption of large aerospace components of the present invention solves the key technical problems in the field of large forging processing through innovative data acquisition, analysis, prediction and optimization technologies, significantly improves the efficiency and effect of the system, and has broad application prospects and significant technological progress.

[0113] Adaptive fuzzy neural network is a hybrid intelligent algorithm that combines fuzzy logic and artificial neural network. It can use the self-learning ability of neural network and the knowledge expression ability of fuzzy logic to deal with uncertainty and ambiguity in complex systems. The basic structure is as follows Figure 2 shown.

[0114] The square nodes in the network need to learn parameters. Its structure is a five-layer feedforward network. The node function of each layer of the network structure is described as follows:

[0115] like Figure 3 As shown, an embodiment of the present invention provides a large-scale aerospace component thermal processing quality and energy consumption coordinated control system, which specifically includes:

[0116] Data acquisition module, which collects and processes data to build equipment energy consumption characteristic data sets and quality characteristic data sets;

[0117] Energy consumption prediction module, based on the energy consumption prediction model, obtains the standard energy consumption prediction value of each equipment in the hot processing of aerospace components;

[0118] The quality prediction module obtains the quality prediction value of the hot-processed component product based on the quality prediction model;

[0119] The optimization module uses particle swarm algorithm to perform multi-objective optimization on subsequent processes.

[0120] 1. Specific application fields or related products of the present invention.

[0121] Example based on a certain type of aluminum alloy aviation die forging

[0122] Figure 4 Hot working process of the die forging

[0123] 1. Collect the energy consumption data and process data of each equipment in the processing process, including the input power, pressing speed, pressing force, die temperature, component temperature of the die forging hydraulic press, the input power and furnace temperature change curve of the pre-forging heating furnace and heat treatment rate, to form the energy consumption characteristic data set of each equipment.

[0124] 2. Import the forging and corresponding die models into the simulation software Deform, and set multiple sets of process parameters for each process, including the forging rate of pre-forging and final forging, the initial temperature of the forging and die, the heat treatment heating rate, the holding temperature, and the holding time. Through the combined simulation calculation of multiple processes and multiple groups of processes, the final average grain size data of the die forging under each group of processes are obtained. Combined with the average grain size in the quality inspection after the actual production of the die forging and the corresponding process data, the average grain size feature data set of the die forging is formed.

[0125] 3. Establish energy consumption prediction models for forging presses, pre-forging heating furnaces and heat treatment furnaces, and establish average grain size prediction models for die forgings. This example details the process of establishing an energy consumption prediction model for pre-forging heating furnaces:

[0126] Since the heating furnace only works during heating and insulation, only the data from the heating and insulation stages are used for model training. Fuzzy neural network is used for modeling, and the membership function is Gaussian function, which is expressed as

[0127]

[0128] The input variables of the model are set temperature, average temperature in the furnace, and heating rate, and the output variable is power. The number of membership functions for each input variable is set to 3. The input data is passed through the fuzzy layer, rule layer, normalization layer, and linear layer to obtain the output result. The model is trained through the hybrid optimization algorithm, and finally the energy consumption prediction model E of the pre-forging heating furnace is obtained. 加热 (X 加热 ).

[0129] According to the above method, the energy consumption prediction model E of the die forging press is obtained. 压机 (X 压机 ), Heat treatment furnace energy consumption prediction model E 热处理 (X 热处理 ) and the average grain size prediction model D(X) for die forgings.

[0130] 4. The standard process data of the aluminum alloy die forgings are respectively input into the energy consumption prediction models of the die forging press, pre-forging heating furnace, and heat treatment furnace, and the standard energy consumption values ​​of each equipment in each process are calculated as reference values ​​for actual processing.

[0131] 5. In actual processing, the actual process data of the completed process and the standard process data of the subsequent process are combined and input into the average grain size prediction model of the die forging to obtain the final average grain size prediction value of the current die forging.

[0132] 6. Based on the deviation between the predicted value and the required value of the average grain size, and the deviation between the actual total energy consumption value of the equipment and the predicted value of the standard total energy consumption, the particle swarm algorithm is used to adjust and optimize the process of subsequent processes to achieve coordinated control of the quality and energy consumption of hot-processed components.

[0133] The following two situations need to be optimized:

[0134] (1) The predicted average grain size does not meet the requirement. The grain size requirement of the aviation aluminum alloy die forging in this example is 7-8, that is, the average grain size requirement range is 0.016-0.032mm. When the predicted average grain size exceeds the required range, subsequent process optimization is required.

[0135] (2) The predicted average grain size meets the requirement, but the actual energy consumption of the previous process is higher than the standard energy consumption value by a certain range. In this example, it is set that the actual energy consumption of each process is allowed to exceed the standard energy consumption by 10%. If the actual energy consumption is too high, subsequent process optimization is required.

[0136] The particle swarm algorithm is used for optimization, the purpose of which is to find a process combination solution that meets the average grain size requirements and has the lowest energy consumption. The optimization objective function is:

[0137]

[0138] The input X of D(X) is the process data of the completed process and the process data of the subsequent process, and the optimization object is the process combination of the subsequent process. E(X) represents the total energy consumption prediction value, which is the sum of the actual energy consumption of the completed process and the predicted energy consumption of the subsequent process.

[0139] After a pre-forging, the quality prediction module predicts that the final average grain size of the aluminum alloy forging will be greater than 0.032 mm. The control system starts the optimization module to optimize the subsequent final forging and heat treatment processes. Figure 5 Iterative optimization prediction results for the average grain size, Figure 6 Iterative optimization prediction results for total energy consumption.

[0140] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0141] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for coordinated control of thermal processing quality and energy consumption of large aerospace components, characterized in that: The method specifically includes: S1: Collect energy consumption data and process data of each device during the thermal processing of aerospace components, and combine them with existing historical energy consumption data to form an equipment energy consumption characteristic data set; S2: Import the aerospace component model and mold model into the finite element simulation software. Based on the actual hot working process, set multiple sets of multi-pass process parameters to simulate and calculate the corresponding quality data. Combine the quality inspection data of each batch of components and the corresponding process data to form a quality feature data set. S3: inputting the energy consumption characteristic data set and the quality characteristic data set into the adaptive fuzzy neural network to be trained respectively, until the trained model converges, and obtaining the energy consumption prediction model of each hot processing equipment and the multi-pass quality prediction model of the hot processing component; S4: input the preset hot working process data into the energy consumption prediction model to obtain the standard energy consumption prediction value of each device in the hot working process of aerospace components, and predict the standard value of energy consumption required for processing components according to the standard hot working process route data, which is used as a reference value for energy consumption in actual processing; S5: input the real-time process data in the actual hot processing production into the quality prediction model to obtain the quality prediction value of the hot processing component product; S6: Based on whether the quality prediction value meets the quality requirements and the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value, the particle swarm algorithm is used to adjust and optimize the process of subsequent processes to achieve coordinated control of the quality and energy consumption of hot-processed components.

2. The method for coordinated control of thermal processing quality and energy consumption of large aerospace components according to claim 1, characterized in that: The S1, hot working process includes billet making, die forging and heat treatment; the energy consumption data and process data of each equipment include the input power, pressing speed, pressing force, die temperature, component temperature of the forging press, the input power or gas consumption rate, heating rate, and temperature change curve in the furnace of the heating furnace or heat treatment furnace.

3. The method for coordinated control of thermal processing quality and energy consumption of large aerospace components according to claim 1, characterized in that: The S2, quality data includes the grain size, yield strength, tensile strength, etc. of the final product of the hot-processed component; the process data includes the forging rate of multiple forging passes, the component temperature before forging, the die temperature, the heat treatment heating rate, the holding temperature, the holding time, etc.

4. The method for coordinated control of thermal processing quality and energy consumption of large aerospace components according to claim 1, characterized in that: The energy consumption prediction model in S3 is established for different hot processing equipment, including: free forging press, die forging press, pre-forging heating furnace and heat treatment furnace. The input of the model is the equipment process data of the time series, and the output is the equipment power / gas consumption rate of the time series, which can be converted into the total power consumption / gas consumption of the equipment in a period of time after processing. The prediction model can simulate and calculate the input power or gas consumption rate corresponding to the operation of the equipment according to a series of set process parameters in actual production; Quality prediction models are established for different output targets, including multiple prediction models with quality indicators such as grain size, yield strength, and tensile strength as output values. The model input is the full-process process parameter matrix. The model establishes the relationship between the final quality of the forging and the complete hot working process technology.

5. The method for coordinated control of thermal processing quality and energy consumption of large aerospace components according to claim 1, characterized in that: In S6, when it is predicted that the final quality of the component is unqualified, the particle swarm algorithm is used to perform multi-objective optimization on the subsequent process, the process parameters are updated through the algorithm, and the process parameters are input into the quality and energy consumption prediction model. The process parameters are updated with the final quality prediction of the component as a constraint and the minimum energy consumption as the goal feedback, and finally the optimization is achieved. The objective function can be expressed as: Among them, X represents the multidimensional process matrix composed of the whole process parameters, Q i (X) indicates the predicted values ​​of different qualities, a i ~b i It represents the required range of different qualities, and E(X) represents the total energy consumption of the whole processing process, which is the sum of the energy consumption forecasts of each equipment.

6. A large-scale aerospace component thermal processing quality and energy consumption coordinated control system based on claims 1-4, characterized in that: The system specifically includes: A data acquisition unit is configured to collect energy consumption data and process data of each device during the thermal processing of the aerospace component, and to form an equipment energy consumption characteristic data set in combination with the existing historical energy consumption data; A finite element simulation module is configured to receive an aerospace component model and a mold model, import them into a finite element simulation software, set multiple sets of multi-pass process parameters based on an actual hot working process to perform simulation calculations, generate corresponding quality data, and combine the quality inspection data and process data of each batch of components to form a quality feature data set; An adaptive fuzzy neural network training module is configured to input the equipment energy consumption characteristic data set and the quality characteristic data set into the adaptive fuzzy neural network to be trained, respectively, until the model converges, and generate an energy consumption prediction model for each hot processing equipment and a multi-pass quality prediction model for hot processing components; An energy consumption prediction module, configured to input preset thermal processing process data into the energy consumption prediction model to generate a standard energy consumption prediction value of each device in the thermal processing process of an aerospace component; A quality prediction module, configured to input real-time process data in actual hot working production into the quality prediction model to generate a quality prediction value of the hot working component product; The optimization control unit is configured to use a particle swarm algorithm to adjust and optimize the process parameters of subsequent processes based on whether the quality prediction value meets the quality requirements and the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value, so as to achieve coordinated control of the quality and energy consumption of the hot-processed components.

7. The system for coordinated control of thermal processing quality and energy consumption of large aerospace components according to claim 6, characterized in that: The data acquisition unit further comprises: Energy consumption data collection module, used to collect energy consumption data and process data of various equipment such as input power, pressing speed, pressing force, die temperature, component temperature of forging press, input power or gas consumption rate, heating rate, temperature change curve in furnace of heating furnace or heat treatment furnace; The process data acquisition module is used to collect process parameter data such as forging rate, pre-forging component temperature, die temperature, heat treatment heating rate, holding temperature, holding time, etc. of multiple forging passes.

8. The system for coordinated control of thermal processing quality and energy consumption of large aerospace components according to claim 6, characterized in that: The optimization control unit further comprises: Deviation analysis module, used to calculate the deviation between the quality prediction value and the quality requirement value, as well as the deviation between the actual equipment energy consumption value and the standard energy consumption prediction value; The process parameter adjustment module dynamically adjusts the process parameters such as forging rate, pressing speed, heating rate, etc. of the subsequent process based on the deviation analysis results to optimize the quality and energy consumption in the hot working process.

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