Aluminum alloy profile production and processing optimization control method and system

Abstract

The invention discloses an aluminum alloy profile production and processing optimization control method and system, and relates to the technical field of aluminum alloy processing, the system monitors and acquires processing data in real time through a processing data acquisition module, and processes the data through an optimization algorithm in a dynamic control adjustment analysis module; and optimizing and outputting a grain size change rate, a stress relaxation time constant and an oxide layer thickness change rate. The optimization parameters are calculated and analyzed through a comprehensive optimization objective function, and the quality stability and consistency in the machining process are ensured. Especially in the production process, by adjusting the grain size change rate, the stress relaxation time constant and the oxide layer thickness change rate, the system can respond to changes in the machining process in real time, and therefore the machining quality of the aluminum alloy profile is remarkably improved, product quality fluctuation is reduced, and production reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aluminum alloy processing, and in particular to an aluminum alloy profile production and processing optimization control method and system. Background Art

[0002] The production and processing of aluminum alloy profiles is an important application direction in the field of materials science and engineering technology. This field involves the manufacturing and processing of aluminum alloy materials, and the key links include extrusion, cooling and surface treatment processes. Aluminum alloy profiles are widely used in industries such as construction, transportation and aerospace due to their excellent mechanical properties and corrosion resistance. Especially in the extrusion production process of aluminum alloy profiles, how to effectively control and optimize processing parameters to ensure product quality and performance is a core issue in this field. With the advancement of manufacturing technology, the complexity of production processes has increased, and the demand for real-time data collection and processing and dynamic adjustment of process parameters has also increased.

[0003] Although automated control systems have been widely used in the current production process of aluminum alloy profiles, these systems often face the following problems: First, data collection and processing often lag behind, making it difficult to reflect the actual status of the processing process in real time, resulting in an inability to quickly respond to abnormal situations in production. Second, most of the existing process optimization methods are based on historical data and empirical rules, lacking dynamic analysis and adjustment of real-time data, and often unable to effectively respond to new problems that arise during the processing process. Third, the optimization algorithms in existing systems are relatively simple, and fail to comprehensively consider factors such as grain size, stress relaxation, and oxide layer thickness, resulting in limited optimization effects and an inability to comprehensively improve product quality;

[0004] The above problems are mainly caused by the lack of real-time data processing capabilities of traditional systems and the limitations of process optimization algorithms. In the production process, due to the lack of timely data collection, adjustments can only be made based on preset fixed parameters. This makes it difficult to ensure the stability of the processing process and the high quality of the product when facing the ever-changing conditions in actual production. Insufficient process optimization will lead to problems such as uneven grain size, insufficient stress relaxation, and uneven oxide layer of the product, which not only affects the mechanical properties and appearance quality of the product, but may also increase material waste and production costs. Ultimately, these deficiencies may cause the performance of aluminum alloy profiles to fail to meet application requirements, affecting the market competitiveness of the product. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an aluminum alloy profile production and processing optimization control method and system, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: including a processing data acquisition module, a data processing module, a processing technology optimization algorithm module, a dynamic control adjustment analysis module and a processing quality optimization analysis module;

[0007] The processing data acquisition module collects the processing data of aluminum alloy profile production in real time by installing data acquisition equipment and setting the collection time interval, and encapsulates the processing data to obtain a processing data package, and then sends the processing data package to the data processing module through a transmission protocol;

[0008] The data processing module is used to receive the processed data packets in real time, unpack the processed data packets, extract the processed data, pre-process the processed data, obtain the standard data group, and then build a time series database, mark the time series of each data in the standard data group, and store them in the time series database;

[0009] The processing technology optimization algorithm module is used to construct a related algorithm model, extract a standard data group from a time series database in real time, input it into the related algorithm model, and output the optimized grain size change rate, stress relaxation time constant and oxide layer thickness change rate;

[0010] The dynamic control adjustment analysis module is used to construct a comprehensive optimization algorithm model, input the output grain size change rate, stress relaxation time constant and oxide layer thickness change rate into the constructed comprehensive optimization algorithm model, calculate and output the comprehensive optimization objective function, and pre-set the optimization threshold and the comprehensive optimization objective function for preliminary comparison and evaluation, and analyze the optimization effect;

[0011] The processing quality optimization analysis module is used to calculate and output the quality of the optimized product after optimizing and controlling the production and processing of aluminum alloy profiles, and to comprehensively calculate and output the comprehensive processing score based on the optimized product quality, and to perform a secondary comparative evaluation between the preset processing quality threshold and the comprehensive processing score, and to generate an iterative optimization strategy based on the evaluation results.

[0012] Preferably, the processing data acquisition module includes an acquisition unit and a transmission unit;

[0013] The acquisition unit is used to install data acquisition equipment on the aluminum alloy profile production and processing equipment, and collect data once every hour to collect the processing data of the aluminum alloy profile production and processing equipment in real time;

[0014] The data acquisition equipment includes a processing data acquisition equipment group and a defect data acquisition equipment group;

[0015] The processing data acquisition equipment group includes an infrared thermometer, a laser velocimeter, a stress gauge, an ambient temperature sensor, a flow meter and an energy consumption meter;

[0016] The defect data collection equipment group includes a scanning electron microscope, a visual inspection system, an impact tester, a laser rangefinder and an automatic defect detection system;

[0017] The processed data includes a processed data set and a defect data set;

[0018] The processing data set includes equipment temperature, extrusion speed, production stress, material surface temperature, ambient temperature, cooling speed and actual energy consumption;

[0019] The defect data set includes aluminum alloy grain size uniformity, aluminum alloy profile impact resistance, yield strength, hardness, surface quality, actual size, optimized product defect rate and initial product defect rate;

[0020] The transmission unit is used to convert the collected processing data into JSON format, where the JSON format specifically includes the timestamp, data type and source identifier of each data in the processing data, and then use the encoding algorithm to encapsulate the processing data in JSON format into a processing data encapsulation package, and then select the TCP / IP transmission protocol to transmit the processing data encapsulation package to the data processing module.

[0021] Preferably, the data processing module includes a processing unit and a storage unit;

[0022] The processing unit receives a processed data package through a network interface, unpacks the processed data package using a JSON parser, extracts the processed data from the JSON object, and then performs a preprocessing technique on the extracted processed data to obtain a standard data set, wherein the preprocessing technique includes using a linear interpolation technique to fill missing data, using a z-score method to detect outliers, and dimensionless processing;

[0023] The storage unit is used to construct a time series database setting writing API, write the preprocessed standard data group into the time series database, and store the standard data group in time series according to the timestamp in JSON format.

[0024] Preferably, the processing technology optimization algorithm module includes a grain size optimization unit, a stress relaxation optimization unit and an oxide layer optimization unit;

[0025] The grain size optimization unit is used to construct a material grain size change algorithm model, extract the equipment temperature and extrusion speed in the standard data group, input them into the material grain size change algorithm model, and calculate and output the grain size change rate.

[0026] Preferably, the stress relaxation optimization unit is used to construct a stress relaxation time constant algorithm model, extract the production stress in the standard data set, input it into the stress relaxation time constant algorithm model, and calculate and output the stress relaxation time constant.

[0027] Preferably, the oxide layer optimization unit is used to construct a surface oxide layer thickness change rate algorithm model, extract the material surface temperature, ambient temperature and cooling rate in the standard data group and input them into the surface oxide layer thickness change rate algorithm model, and calculate and output the oxide layer thickness change rate.

[0028] Preferably, the dynamic control adjustment analysis module includes a comprehensive optimization unit and an optimization evaluation unit;

[0029] The comprehensive optimization unit is used to perform comprehensive calculation and output a comprehensive optimization objective function based on the obtained grain size change rate, stress relaxation time constant and oxide layer thickness change rate;

[0030] The optimization evaluation unit performs a preliminary comparative evaluation based on the preset optimization threshold by the user and the obtained comprehensive optimization objective function to analyze the optimization effect of aluminum alloy profile production and processing. The specific evaluation contents are as follows;

[0031] When the comprehensive optimization objective function is ≥ the optimization threshold, it means that the optimization effect is abnormal, the comprehensive optimization of aluminum alloy profile production and processing has not reached the standard, and the energy consumption of profile production and processing equipment is abnormal. At this time, the grain size adjustment coefficient, stress relaxation adjustment coefficient and oxide layer thickness adjustment coefficient are automatically reduced by 10%, and the iteration mechanism is executed to re-execute the system for optimization and analysis;

[0032] When the comprehensive optimization objective function is less than the optimization threshold, it means that the optimization effect is normal, and the processing quality optimization analysis module is executed at this time.

[0033] Preferably, the processing quality optimization analysis module includes a product quality analysis unit, a comprehensive processing quality analysis unit and a processing quality evaluation unit;

[0034] The product quality analysis unit constructs a product quality algorithm formula, extracts the uniformity of aluminum alloy grain size, impact resistance, yield strength, hardness, surface quality and actual size of aluminum alloy profiles, inputs them into the product quality algorithm formula, calculates and outputs the optimized product quality;

[0035] The comprehensive processing quality analysis unit performs comprehensive calculation on the obtained optimized product quality and the actual energy consumption, optimized product defect rate and initial product defect rate in the standard data group to obtain a comprehensive processing score.

[0036] Preferably, the processing quality evaluation unit presets a processing quality threshold based on the target processing quality of the aluminum alloy profile, and then performs a secondary comparative evaluation with the obtained comprehensive processing score to analyze the product quality status after the processing equipment is optimized;

[0037] When the comprehensive processing score is greater than the processing quality threshold, it means that when the initial processing equipment is initially optimized normally, the production and processing quality of aluminum alloy profiles is abnormal. At this time, the iteration mechanism is executed, the system is re-executed, and the processing equipment is optimized and adjusted again;

[0038] When the comprehensive processing score is ≤ the processing quality threshold, it means that when the initial processing equipment is initially optimized normally, the production and processing quality of the aluminum alloy profile is also normal, and there is no need to generate an iterative mechanism.

[0039] An aluminum alloy profile production and processing optimization control method comprises the following steps:

[0040] S1. Install data acquisition equipment, set the acquisition time interval, collect the processing data of aluminum alloy profile production in real time, encapsulate the processing data to obtain a processing data package, and then send the processing data package to the data processing module through the transmission protocol;

[0041] S2, receiving the processed data packets in real time, unpacking the processed data packets, extracting the processed data, preprocessing the processed data, obtaining the standard data group, and then constructing the time series database, marking the time series of each data in the standard data group, and storing them in the time series database;

[0042] S3, constructing a relevant algorithm model, extracting a standard data group from a time series database in real time, inputting the data into the relevant algorithm model, and outputting the optimized grain size change rate, stress relaxation time constant, and oxide layer thickness change rate;

[0043] S4, constructing a comprehensive optimization algorithm model, inputting the outputted grain size change rate, stress relaxation time constant and oxide layer thickness change rate into the constructed comprehensive optimization algorithm model, calculating and outputting a comprehensive optimization objective function, and presetting an optimization threshold Y and the comprehensive optimization objective function for preliminary comparative evaluation, and analyzing the optimization effect;

[0044] S5. After optimizing and controlling the production and processing of aluminum alloy profiles, calculate and output the quality of the optimized product, and comprehensively calculate and output the comprehensive processing score based on the optimized product quality. Perform a secondary comparative evaluation between the preset processing quality threshold and the comprehensive processing score, and generate an iterative optimization strategy based on the evaluation results.

[0045] The present invention provides an aluminum alloy profile production and processing optimization control method and system. It has the following beneficial effects:

[0046] (1) The system monitors and collects processing data in real time through the processing data acquisition module, and processes these data through the optimization algorithm in the dynamic control adjustment analysis module to optimize the output of grain size change rate, stress relaxation time constant and oxide layer thickness change rate. These optimization parameters are calculated and analyzed through the comprehensive optimization objective function to ensure the quality stability and consistency of the processing process. In particular, during the production process, by adjusting the grain size change rate, stress relaxation time constant and oxide layer thickness change rate, the system can respond to changes in the processing process in real time, thereby significantly improving the processing quality of aluminum alloy profiles, reducing product quality fluctuations, and improving production reliability and efficiency.

[0047] (2) The system constructs a comprehensive optimization objective function by combining the optimization parameters such as grain size change rate, stress relaxation time constant and oxide layer thickness change rate through the comprehensive optimization algorithm in the dynamic control adjustment analysis module. The comprehensive optimization objective function is used to evaluate the energy efficiency level of the processing process and optimize the energy consumption during the processing. The system significantly reduces energy consumption and material waste during the processing by adjusting the processing parameters in real time. Specifically, by optimizing the energy efficiency level and the adjustment strategy during the processing, the system can reduce the actual energy consumption and reduce the defect rate of the optimized product, thereby achieving higher energy utilization efficiency and material use efficiency. This not only improves the economic benefits of production, but also promotes more environmentally friendly production practices.

[0048] (3) The processing quality optimization and analysis module of the system calculates the quality of the optimized product by constructing a product quality algorithm formula. The system further uses the comprehensive processing quality analysis unit to comprehensively calculate the optimized product quality with the actual energy consumption in the standard data group, the optimized product defect rate and the initial product defect rate to obtain a comprehensive processing score. This score is used to evaluate the optimization effect of the processing equipment to ensure that the final product quality meets the preset standards. The processing quality evaluation unit provides an intelligent optimization strategy by comparing and analyzing the processing quality threshold. If the product quality meets expectations, the system does not need iterative optimization; otherwise, the system will automatically generate a second iteration mechanism for further optimization and adjustment. This intelligent quality evaluation and optimization mechanism improves the accuracy of the production process and ensures the competitiveness of the product in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of a process flow of an aluminum alloy profile production and processing optimization control system of the present invention;

[0050] Figure 2 The present invention is a schematic diagram of the steps of an aluminum alloy profile production and processing optimization control method. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] See also Figure 1 , the present invention provides an aluminum alloy profile production and processing optimization control system. To achieve the above purpose, the present invention is implemented through the following technical solutions: including a processing data acquisition module, a data processing module, a processing technology optimization algorithm module, a dynamic control adjustment analysis module and a processing quality optimization analysis module;

[0054] The processing data acquisition module collects the processing data of aluminum alloy profile production in real time by installing data acquisition equipment and setting the collection time interval, and then encapsulates the processing data to obtain a processing data package, and then sends the processing data package to the data processing module through the transmission protocol;

[0055] The data processing module is used to receive the processed data packets in real time, unpack the processed data packets, extract the processed data, pre-process the processed data, obtain the standard data group, and then build a time series database. After marking the time series of each data in the standard data group, it is stored in the time series database;

[0056] The processing technology optimization algorithm module is used to build relevant algorithm models, extract standard data groups from the time series database in real time, input them into the relevant algorithm models, and output the optimized grain size change rate GCRopt, stress relaxation time constant Topt and oxide layer thickness change rate ORRopt;

[0057] The dynamic control adjustment analysis module is used to construct a comprehensive optimization algorithm model, input the output grain size change rate GCRopt, stress relaxation time constant Topt and oxide layer thickness change rate ORRopt into the constructed comprehensive optimization algorithm model, calculate and output the comprehensive optimization objective function OPTtotal, and preset the optimization threshold Y and the comprehensive optimization objective function OPTtotal for preliminary comparative evaluation and analysis of the optimization effect;

[0058] The processing quality optimization analysis module is used to calculate and output the optimized product quality Qprod after optimizing and controlling the production and processing of aluminum alloy profiles, and to perform comprehensive calculation and output of the comprehensive processing score Score based on the optimized product quality Qprod. It also performs a secondary comparative evaluation between the preset processing quality threshold D and the comprehensive processing score Score, and generates an iterative optimization strategy based on the evaluation results.

[0059] In this embodiment, the system collects and encapsulates the processing data of aluminum alloy profile production in real time through the processing data acquisition module, and uses the data processing module to unseal, preprocess and store these data in time, thereby ensuring the accuracy and integrity of the data. This process not only improves the efficiency of data processing, but also provides a solid data foundation for subsequent process optimization. The processing technology optimization algorithm module analyzes the standard data group in real time by constructing and applying relevant algorithm models, and calculates the grain size change rate GCRopt, stress relaxation time constant Topt and oxide layer thickness change rate ORRopt. The precise output of these optimization parameters enables the dynamic control adjustment analysis module to conduct a preliminary comparative evaluation and analysis of the optimization effect by comprehensively optimizing the objective function OPTtotal and presetting the optimization threshold Y with the comprehensive optimization objective function OPTtotal. The comprehensive optimization algorithm of this system can not only accurately adjust the production parameters, but also automatically adjust the strategy when the optimization effect does not meet the standard, thereby significantly improving the adaptability and optimization effect of the production process. The processing quality optimization analysis module conducts a secondary evaluation by comprehensively calculating the optimized product quality Qprod and the processing score Score to ensure that the product quality meets the preset standards.

[0060] Example 2

[0061] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the processing data acquisition module includes an acquisition unit and a transmission unit;

[0062] The collection unit is used to install data collection equipment on the aluminum alloy profile production and processing equipment, and collect data once every hour to collect the processing data of the aluminum alloy profile production and processing equipment in real time;

[0063] The data collection equipment includes a processing data collection equipment group and a defect data collection equipment group;

[0064] The processing data acquisition equipment group includes infrared thermometers, laser velocimeters, stress gauges, ambient temperature sensors, flow meters and energy consumption meters;

[0065] The defect data collection equipment group includes scanning electron microscope, visual inspection system, impact tester, laser rangefinder and automatic defect detection system;

[0066] The processed data includes a processed data set and a defect data set;

[0067] The processing data set includes equipment temperature T, extrusion speed V, production stress Yl, material surface temperature Ts, ambient temperature Ta, cooling speed Vc and actual energy consumption Ea;

[0068] The equipment temperature T and the material surface temperature Ts are collected and obtained by infrared thermometer;

[0069] The extrusion speed V is acquired by laser velocimeter;

[0070] The production stress Yl is calculated by sticking it on the key structural parts of the material surface and measuring the tiny deformation of the material under stress.

[0071] The ambient temperature Ta is acquired through the ambient temperature sensor;

[0072] The cooling rate Vc is calculated by measuring the cooling water flow rate of the processing equipment with a flow meter;

[0073] The actual energy consumption Ea is obtained by installing it at the power input end of the production equipment to measure the power consumption of the equipment in real time;

[0074] The defect data set includes aluminum alloy grain size uniformity GL, aluminum alloy profile impact resistance ML, yield strength Tyi, hardness H, surface quality SL, actual size Dop, optimized product defect rate Dde and initial product defect rate Din;

[0075] Aluminum alloy grain size uniformity GL uses a scanning electron microscope to observe the microstructure of the sample, directly observe and record the grain distribution and size of the aluminum alloy profile through a scanning electron microscope, and analyze and obtain the aluminum alloy grain size uniformity;

[0076] The impact resistance ML of aluminum alloy profiles is obtained by applying impact force to aluminum alloy samples using an impact testing machine and measuring it;

[0077] Surface quality SL uses a high-resolution camera and image processing algorithm through a visual inspection system to detect defects on the surface of aluminum alloy profiles in real time and perform calculations and analysis to obtain them;

[0078] The actual size Dop is obtained by measuring the size of the aluminum alloy profile sample with a laser rangefinder;

[0079] The optimized product defect rate Dde and the initial product defect rate Din are automatically detected by an automatic detection system that integrates computer vision, ultrasonic wave and X-ray technology to monitor the surface defects of aluminum alloy profiles, such as cracks and dents, in real time and automatically generate defect rate data;

[0080] The transmission unit is used to convert the collected processing data into JSON format. The JSON format specifically includes the timestamp, data type and source identifier of each data in the processing data. Then, the encoding algorithm is used to encapsulate the processing data in JSON format into a processing data encapsulation package, and then the TCP / IP transmission protocol is selected to transmit the processing data encapsulation package to the data processing module.

[0081] In this embodiment, the acquisition module of the system includes an acquisition unit and a transmission unit. The acquisition unit collects processing data in real time through data acquisition equipment. The transmission unit ensures efficient transmission and processing of data by converting the collected data into JSON format and transmitting it using the TCP / IP protocol. The improvement of this system is reflected in the comprehensiveness and real-time nature of data acquisition. Through detailed equipment and defect data collection, it provides more accurate feedback on production status, optimizes the data transmission process, and improves data processing efficiency. These improvements effectively enhance the monitoring capabilities of the production process, making the quality control of aluminum alloy profile production more accurate, and providing a solid foundation for subsequent processing optimization and quality assessment.

[0082] Example 3

[0083] This embodiment is explained in Example 5. Please refer to Figure 1 ,Specifically: the data processing module includes a processing unit and a storage unit;

[0084] The processing unit receives the processed data package through the network interface, unpacks the processed data package using the JSON parser, extracts the processed data from the JSON object, and then performs preprocessing technology on the extracted processed data to obtain a standard data group. The preprocessing technology includes using linear interpolation technology to fill missing data, using the z-score method to detect outliers and dimensionless processing;

[0085] The storage unit is used to build a time series database setting writing API, write the preprocessed standard data group into the time series database, and store the standard data group in time series according to the timestamp in JSON format.

[0086] In this embodiment, the data processing module of the system receives the processed data encapsulation package through the processing unit using the network interface, and uses the JSON parser to unpack the data, extract and perform preprocessing. These preprocessing technologies significantly improve the integrity and accuracy of the data, effectively solve the problems of data missing and abnormalities, and provide a reliable standard data set for subsequent analysis. The storage unit stores the preprocessed data in time series by building a time series database and setting a write API to ensure the timeliness and continuity of the data. The improvement of this module is to realize automated data preprocessing and systematic time series storage, improve the efficiency and accuracy of data processing, and enhance the real-time and traceability of data.

[0087] Example 4

[0088] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the processing process optimization algorithm module includes grain size optimization unit, stress relaxation ,optimization unit and oxide layer optimization unit;

[0089] The grain size optimization unit is used to construct a material grain size change algorithm model, extract the equipment temperature T and extrusion speed V in the standard data group, input them into the material grain size change algorithm model, and calculate and output the grain size change rate GCRopt;

[0090] The grain size change rate GCRopt is calculated and output by the following grain size change algorithm model;

[0091]

[0092] In the formula, GCRset represents the current grain size setting value, and the specific value is set by the user, α represents the grain size adjustment coefficient, d represents the differential operator, t represents time, and x represents the spatial position. It represents the rate of change of device temperature T with time t, represents the rate of change of the extrusion velocity V with the spatial position x, and the calculated and The data is input into the formula to optimize the grain size change rate by adjusting the temperature and extrusion speed control device.

[0093] The stress relaxation optimization unit is used to construct a stress relaxation time constant algorithm model, extract the production stress Yl in the standard data group, input it into the stress relaxation time constant algorithm model, and calculate the output stress relaxation time constant Topt;

[0094] The stress relaxation time constant Topt is output by the following stress relaxation time constant algorithm model;

[0095]

[0096] Wherein, Tset represents the current stress relaxation time constant setting value, and the specific value is set by the user. β represents the stress relaxation adjustment coefficient, and Yy represents the material yield stress, which is input by the user based on the aluminum alloy profile extrusion parameters. The stress relaxation time constant is optimized by adjusting the heating or cooling rate during the processing process, or by controlling the stress application method.

[0097] The oxide layer optimization unit is used to construct an algorithm model for the change rate of the surface oxide layer thickness, extract the material surface temperature Ts, the ambient temperature Ta and the cooling rate Vc in the standard data group and input them into the algorithm model for the change rate of the surface oxide layer thickness, and calculate the output oxide layer thickness change rate ORRopt;

[0098] The oxide layer thickness change rate ORRopt is output by the following surface oxide layer thickness change rate algorithm model;

[0099]

[0100] In the formula, ORRset represents the current oxide layer thickness change rate setting value, which is set by the user according to the process requirements, and γ represents the oxide layer thickness adjustment coefficient. The ratio of the difference between the material surface temperature Ts and the ambient temperature Ta to the cooling rate Vc reflects the rate of oxide layer formation.

[0101] In this embodiment, the processing technology optimization algorithm module of the system realizes the comprehensive optimization of the aluminum alloy profile processing process through the grain size optimization unit, the stress relaxation optimization unit and the oxide layer optimization unit. The grain size optimization unit calculates the grain size change rate GCRopt according to the changes in the equipment temperature and the extrusion speed through the material grain size change algorithm model. This process effectively improves the grain uniformity of the aluminum alloy and improves the product quality by adjusting the equipment temperature T and the extrusion speed V. The stress relaxation optimization unit calculates the stress relaxation time constant Topt based on the standard data of production stress. By adjusting the heating or stress application method during the processing, the stress relaxation characteristics of the material are optimized, and the mechanical properties of the material are further improved. The oxide layer optimization unit calculates the oxide layer thickness change rate ORRopt using the material surface temperature Ts, the ambient temperature Ta and the cooling rate Vc, and optimizes the thickness of the surface oxide layer by adjusting the relevant process parameters, thereby improving the surface quality of the product. The intelligent algorithm model provides precise control, improves the stability of the processing process and the consistency of the product, and significantly improves the overall processing quality and efficiency of aluminum alloy profiles.

[0102] Example 5

[0103] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the dynamic control regulation analysis module includes a ,comprehensive optimization unit and an optimization evaluation unit;

[0104] The comprehensive optimization unit is used to perform comprehensive calculation and output the comprehensive optimization objective function OPTtotal based on the obtained grain size change rate GCRopt, stress relaxation time constant Topt and oxide layer thickness change rate ORRopt;

[0105] The comprehensive optimization objective function OPTtotal is calculated and obtained by the following algorithm formula;

[0106]

[0107] Where Eref represents the reference energy consumption, represents the energy efficiency level, w1, w2, w3 and w4 represent the preset weight values ​​of the grain size change rate GCRopt, the stress relaxation time constant Topt, the oxide layer thickness change rate ORRopt and the energy efficiency level, respectively, and w1+w2+w3+w4=1, min represents the minimization function;

[0108] The optimization evaluation unit performs a preliminary comparative evaluation through the user's preset optimization threshold value Y and the obtained comprehensive optimization objective function OPTtotal to analyze the optimization effect of aluminum alloy profile production and processing. The specific evaluation contents are as follows;

[0109] When the comprehensive optimization objective function OPTtotal ≥ optimization threshold Y, it indicates that the optimization effect is abnormal, the comprehensive optimization of aluminum alloy profile production and processing does not meet the standard, and the energy consumption of profile production and processing equipment is abnormal. At this time, the grain size adjustment coefficient α, stress relaxation adjustment coefficient β and oxide layer thickness adjustment coefficient γ are automatically reduced by 10%, and the iteration mechanism is executed to re-execute the system for optimization and analysis;

[0110] When the comprehensive optimization objective function OPTtotal is less than the optimization threshold Y, it indicates that the optimization effect is normal, and the processing quality optimization analysis module is executed at this time.

[0111] In this embodiment, the dynamic control adjustment and analysis module of the system realizes the dynamic adjustment and optimization of the aluminum alloy profile processing process through the comprehensive optimization unit and the optimization evaluation unit. The comprehensive optimization unit inputs the grain size change rate GCRopt, the stress relaxation time constant Topt and the oxide layer thickness change rate ORRopt into the calculation formula of the comprehensive optimization objective function OPTtotal, comprehensively considers multiple optimization objectives, and generates a comprehensive optimization index. This objective function ensures the comprehensive optimization of the processing process by minimizing energy efficiency loss and optimization parameters, and improves the production efficiency and quality of aluminum alloy profiles. The optimization evaluation unit conducts a preliminary comparative evaluation of the comprehensive optimization objective function according to the preset optimization threshold Y to ensure that the optimization effect meets the standard. When the comprehensive optimization objective function OPTtotal exceeds the optimization threshold Y, the system will automatically adjust the optimization parameters and re-optimize to ensure the stability and quality of the processing process; when the comprehensive optimization objective function OPTtotal is lower than the optimization threshold Y, it means that the optimization effect is good, and the system continues to perform subsequent processing quality optimization analysis. Through dynamic adjustment and real-time evaluation, this module significantly improves the flexibility and accuracy of the optimization process, and effectively improves the production quality and energy efficiency of aluminum alloy profiles.

[0112] Example 6

[0113] This embodiment is explained in Example 5. Please refer to Figure 1 ,Specifically: the processing quality optimization and analysis module includes the ,product quality analysis unit, the comprehensive processing quality analysis unit and the processing ,quality evaluation unit;

[0114] The product quality analysis unit constructs a product quality algorithm formula, extracts the aluminum alloy grain size uniformity GL, aluminum alloy profile impact resistance ML, yield strength Tyi, hardness H, surface quality SL and actual size Dop, inputs them into the product quality algorithm formula, calculates and outputs the optimized product quality Qprod;

[0115] The optimized product quality Qprod is output through the following product quality algorithm formula;

[0116]

[0117] Where Mop represents the target size;

[0118] The comprehensive processing quality analysis unit performs comprehensive calculation on the obtained optimized product quality Qprod, the actual energy consumption Ea, the optimized product defect rate Dde and the initial product defect rate Din in the standard data group to obtain the comprehensive processing score Score;

[0119] The comprehensive processing score Score is calculated by the following algorithm formula;

[0120]

[0121] Where Tres represents the system response time, which is extracted from the processing equipment work log, Qtarget represents the target product quality, represents the defect rate, a1, a2, a3 and a4 represent the preset weight values ​​of product quality, energy efficiency level, system response time and defect rate respectively, and a1+a2+a3+a4=1.

[0122] The processing quality assessment unit presets the processing quality threshold D based on the target processing quality of the aluminum alloy profile, and then conducts a secondary comparative assessment with the obtained comprehensive processing score Score to analyze the product quality status after the processing equipment is optimized;

[0123] When the comprehensive processing score Score>processing quality threshold D, it means that when the initial processing equipment is initially optimized normally, the production and processing quality of aluminum alloy profiles is abnormal. At this time, the iteration mechanism is executed, the system is re-executed, and the processing equipment is optimized and adjusted again;

[0124] When the comprehensive processing score Score ≤ the processing quality threshold D, it means that when the preliminary processing equipment is initially optimized normally, the production and processing quality of the aluminum alloy profile is also normal, and there is no need to generate an iterative mechanism at this time.

[0125] In this embodiment, the processing quality optimization analysis module of the system comprehensively improves the production quality and efficiency of aluminum alloy profiles through the product quality analysis unit, the comprehensive processing quality analysis unit and the processing quality evaluation unit. The product quality analysis unit uses the product quality algorithm formula to integrate the key parameters such as alloy grain size uniformity GL, aluminum alloy profile impact resistance ML, yield strength Tyi, hardness H, surface quality SL and actual size Dop, and accurately calculates the optimized product quality Qprod. This process not only improves the evaluation accuracy of product quality, but also provides a clear quantitative basis for the optimization target. The comprehensive processing quality analysis unit calculates the comprehensive processing score Score by combining the optimized product quality Qprod with the actual energy consumption Ea, the optimized product defect rate Dde and the initial product defect rate Din. This comprehensive scoring system takes into account the weights of product quality, energy efficiency, response time and defect rate, and provides a quantitative reference standard for comprehensively evaluating the processing quality. The processing quality evaluation unit further performs a secondary evaluation based on the comparison between the comprehensive processing score Score and the preset processing quality threshold D to ensure the practical application of the optimization effect. When the comprehensive processing score Score is lower than the processing quality threshold D, the system will trigger the second iterative optimization to continuously improve product quality.

[0126] Example 7

[0127] See also Figure 1 and Figure 2 , an aluminum alloy profile production and processing optimization control method, comprising the following steps:

[0128] S1. Install data acquisition equipment, set the acquisition time interval, collect the processing data of aluminum alloy profile production in real time, encapsulate the processing data to obtain a processing data package, and then send the processing data package to the data processing module through the transmission protocol;

[0129] S2, receiving the processed data packets in real time, unpacking the processed data packets, extracting the processed data, preprocessing the processed data, obtaining the standard data group, and then constructing the time series database, marking the time series of each data in the standard data group, and storing them in the time series database;

[0130] S3, constructing a relevant algorithm model, extracting the standard data group in the time series database in real time, inputting it into the relevant algorithm model, and outputting the optimized grain size change rate GCRopt, stress relaxation time constant Topt and oxide layer thickness change rate ORRopt;

[0131] S4, construct a comprehensive optimization algorithm model, input the output grain size change rate GCRopt, stress relaxation time constant Topt and oxide layer thickness change rate ORRopt into the constructed comprehensive optimization algorithm model, calculate and output the comprehensive optimization objective function OPTtotal, and pre-set the optimization threshold Y and the comprehensive optimization objective function OPTtotal for preliminary comparative evaluation and analysis of the optimization effect;

[0132] S5. After optimizing and controlling the production and processing of aluminum alloy profiles, the optimized product quality Qprod is calculated and output, and the comprehensive processing score Score is comprehensively calculated and output based on the optimized product quality Qprod. A secondary comparative evaluation is performed between the preset processing quality threshold D and the comprehensive processing score Score, and an iterative optimization strategy is generated based on the evaluation results.

[0133] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. An aluminum alloy profile production and processing optimization control system, characterized by: It includes processing data acquisition module, data processing module, processing technology optimization algorithm module, dynamic control adjustment analysis module and processing quality optimization analysis module; The processing data acquisition module collects the processing data of aluminum alloy profile production in real time by installing data acquisition equipment and setting the collection time interval, and encapsulates the processing data to obtain a processing data package, and then sends the processing data package to the data processing module through a transmission protocol; The data processing module is used to receive the processed data packets in real time, unpack the processed data packets, extract the processed data, pre-process the processed data, obtain the standard data group, and then build a time series database, mark the time series of each data in the standard data group, and store them in the time series database; The processing technology optimization algorithm module is used to construct a related algorithm model, extract a standard data group from a time series database in real time, input it into the related algorithm model, and output the optimized grain size change rate, stress relaxation time constant and oxide layer thickness change rate; The dynamic control adjustment analysis module is used to construct a comprehensive optimization algorithm model, input the output grain size change rate, stress relaxation time constant and oxide layer thickness change rate into the constructed comprehensive optimization algorithm model, calculate and output the comprehensive optimization objective function, and pre-set the optimization threshold and the comprehensive optimization objective function for preliminary comparison and evaluation, and analyze the optimization effect; The processing quality optimization analysis module is used to calculate and output the quality of the optimized product after optimizing and controlling the production and processing of aluminum alloy profiles, and to comprehensively calculate and output the comprehensive processing score based on the optimized product quality, and to perform a secondary comparative evaluation between the preset processing quality threshold and the comprehensive processing score, and to generate an iterative optimization strategy based on the evaluation results.

2. The aluminum alloy profile production and processing optimization control system according to claim 1 is characterized in that: The processing data acquisition module includes an acquisition unit and a transmission unit; The acquisition unit is used to install data acquisition equipment on the aluminum alloy profile production and processing equipment, and collect data once every hour to collect the processing data of the aluminum alloy profile production and processing equipment in real time; The data acquisition equipment includes a processing data acquisition equipment group and a defect data acquisition equipment group; The processing data acquisition equipment group includes an infrared thermometer, a laser velocimeter, a stress gauge, an ambient temperature sensor, a flow meter and an energy consumption meter; The defect data collection equipment group includes a scanning electron microscope, a visual inspection system, an impact tester, a laser rangefinder and an automatic defect detection system; The processed data includes a processed data set and a defect data set; The processing data set includes equipment temperature, extrusion speed, production stress, material surface temperature, ambient temperature, cooling speed and actual energy consumption; The defect data set includes aluminum alloy grain size uniformity, aluminum alloy profile impact resistance, yield strength, hardness, surface quality, actual size, optimized product defect rate and initial product defect rate; The transmission unit is used to convert the collected processing data into JSON format, where the JSON format specifically includes the timestamp, data type and source identifier of each data in the processing data, and then use the encoding algorithm to encapsulate the processing data in JSON format into a processing data encapsulation package, and then select the TCP / IP transmission protocol to transmit the processing data encapsulation package to the data processing module.

3. The aluminum alloy profile production and processing optimization control system according to claim 2 is characterized by: The data processing module includes a processing unit and a storage unit; The processing unit receives a processed data package through a network interface, unpacks the processed data package using a JSON parser, extracts the processed data from the JSON object, and then performs a preprocessing technique on the extracted processed data to obtain a standard data set, wherein the preprocessing technique includes using a linear interpolation technique to fill missing data, using a z-score method to detect outliers, and dimensionless processing; The storage unit is used to construct a time series database setting writing API, write the preprocessed standard data group into the time series database, and store the standard data group in time series according to the timestamp in JSON format.

4. The aluminum alloy profile production and processing optimization control system according to claim 3 is characterized in that: The processing technology optimization algorithm module includes a grain size optimization unit, a stress relaxation optimization unit and an oxide layer optimization unit; The grain size optimization unit is used to construct a material grain size change algorithm model, extract the equipment temperature and extrusion speed in the standard data group, input them into the material grain size change algorithm model, and calculate and output the grain size change rate.

5. The aluminum alloy profile production and processing optimization control system according to claim 4, characterized in that: The stress relaxation optimization unit is used to construct a stress relaxation time constant algorithm model, extract production stress in a standard data set, input it into the stress relaxation time constant algorithm model, and calculate and output the stress relaxation time constant.

6. The aluminum alloy profile production and processing optimization control system according to claim 4, characterized in that: The oxide layer optimization unit is used to construct a surface oxide layer thickness change rate algorithm model, extract the material surface temperature, ambient temperature and cooling rate in the standard data group and input them into the surface oxide layer thickness change rate algorithm model, and calculate and output the oxide layer thickness change rate.

7. The aluminum alloy profile production and processing optimization control system according to claim 6, characterized in that: The dynamic control adjustment analysis module includes a comprehensive optimization unit and an optimization evaluation unit; The comprehensive optimization unit is used to perform comprehensive calculation and output a comprehensive optimization objective function based on the obtained grain size change rate, stress relaxation time constant and oxide layer thickness change rate; The optimization evaluation unit performs a preliminary comparative evaluation based on the preset optimization threshold by the user and the obtained comprehensive optimization objective function to analyze the optimization effect of aluminum alloy profile production and processing. The specific evaluation contents are as follows; When the comprehensive optimization objective function is ≥ the optimization threshold, it means that the optimization effect is abnormal, the comprehensive optimization of aluminum alloy profile production and processing has not reached the standard, and the energy consumption of profile production and processing equipment is abnormal. At this time, the grain size adjustment coefficient, stress relaxation adjustment coefficient and oxide layer thickness adjustment coefficient are automatically reduced by 10%, and the iteration mechanism is executed to re-execute the system for optimization and analysis; When the comprehensive optimization objective function is less than the optimization threshold, it means that the optimization effect is normal, and the processing quality optimization analysis module is executed at this time.

8. The aluminum alloy profile production and processing optimization control system according to claim 7, characterized in that: The processing quality optimization and analysis module includes a product quality analysis unit, a comprehensive processing quality analysis unit and a processing quality evaluation unit; The product quality analysis unit constructs a product quality algorithm formula, extracts the uniformity of aluminum alloy grain size, impact resistance, yield strength, hardness, surface quality and actual size of aluminum alloy profiles, inputs them into the product quality algorithm formula, calculates and outputs the optimized product quality; The comprehensive processing quality analysis unit performs comprehensive calculation on the obtained optimized product quality and the actual energy consumption, optimized product defect rate and initial product defect rate in the standard data group to obtain a comprehensive processing score.

9. The aluminum alloy profile production and processing optimization control system according to claim 8, characterized in that: The processing quality evaluation unit presets a processing quality threshold based on the target processing quality of the aluminum alloy profile, and then performs a secondary comparative evaluation with the obtained comprehensive processing score to analyze the product quality status after the processing equipment is optimized; When the comprehensive processing score is greater than the processing quality threshold, it means that when the initial processing equipment is initially optimized normally, the production and processing quality of aluminum alloy profiles is abnormal. At this time, the iteration mechanism is executed, the system is re-executed, and the processing equipment is optimized and adjusted again; When the comprehensive processing score is ≤ the processing quality threshold, it means that when the initial processing equipment is initially optimized normally, the production and processing quality of the aluminum alloy profile is also normal, and there is no need to generate an iterative mechanism.

10. An aluminum alloy profile production and processing optimization control method, applied to an aluminum alloy profile production and processing optimization control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Install data acquisition equipment, set the acquisition time interval, collect the processing data of aluminum alloy profile production in real time, encapsulate the processing data to obtain a processing data package, and then send the processing data package to the data processing module through the transmission protocol; S2, receiving the processed data packets in real time, unpacking the processed data packets, extracting the processed data, preprocessing the processed data, obtaining the standard data group, and then constructing the time series database, marking the time series of each data in the standard data group, and storing them in the time series database; S3, constructing a relevant algorithm model, extracting a standard data group from a time series database in real time, inputting the data into the relevant algorithm model, and outputting the optimized grain size change rate, stress relaxation time constant, and oxide layer thickness change rate; S4, constructing a comprehensive optimization algorithm model, inputting the outputted grain size change rate, stress relaxation time constant and oxide layer thickness change rate into the constructed comprehensive optimization algorithm model, calculating and outputting a comprehensive optimization objective function, and presetting an optimization threshold Y and the comprehensive optimization objective function for preliminary comparative evaluation, and analyzing the optimization effect; S5. After optimizing and controlling the production and processing of aluminum alloy profiles, calculate and output the quality of the optimized product, and comprehensively calculate and output the comprehensive processing score based on the optimized product quality. Perform a secondary comparative evaluation between the preset processing quality threshold and the comprehensive processing score, and generate an iterative optimization strategy based on the evaluation results.

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