A data-monitoring-based method, system, and storage medium for controlling deformation in aluminum frame processing.

Abstract

This application relates to the field of intelligent manufacturing technology, and in particular to a method, system, and storage medium for controlling the deformation of aluminum frame processing based on data monitoring. The method includes: acquiring surface curvature and deformation data of the aluminum frame through sensors, generating a curvature distribution map and deformation vector field after image processing; analyzing the data based on a neural network model to identify stress distribution and potential defects, and determining speed adjustment requirements in conjunction with equipment delay information; if the requirements exceed a threshold, generating a dynamic speed curve and extracting key parameters to update equipment control; the system monitors the execution effect through feedback loops, and if the indicators do not meet the standards, it backtracks the data, optimizes model parameters, drives the equipment to complete processing through improved instructions, and collects subsequent data to evaluate the overall quality; this application improves the processing quality of aluminum frames.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to a method, system and storage medium for controlling deformation during aluminum frame processing based on data monitoring. Background Technology

[0002] Aluminum frame processing is a crucial technology in modern manufacturing, widely used in aerospace, automotive, and construction industries. In these fields, the processing quality of aluminum frames directly impacts product performance and safety. Aluminum frames are widely used due to their lightweight, high strength, and corrosion resistance; however, the special properties of aluminum and the complex processing environment necessitate extremely high precision and stability in their processing. Especially when dealing with complex curved surfaces and irregularly shaped frames, the dynamic matching between the material properties of the aluminum frame and the processing technology becomes a focal point of industry attention. In actual processing, the control of curvature changes and deformation behavior is extremely important due to the complex shape of the aluminum frame; precise management of these factors is key to improving product quality and processing efficiency. Traditional aluminum frame processing methods typically rely on static process parameters, failing to fully consider the dynamic changes in the aluminum material during processing, such as variations in temperature, stress, and cutting forces. With changes in cutting forces and temperature during processing, uneven stress distribution occurs in the aluminum material, leading to processing defects such as cracks and warping. For example, in the machining of automotive aluminum alloy frames, the concentration of local stress during high-speed cutting may cause micro-cracks or deformation on the surface of the aluminum frame, which will affect the accuracy of subsequent assembly and may even cause the product to be scrapped.

[0003] In existing technologies, these traditional processing methods fail to respond promptly to changes in material properties and processing techniques. The lack of real-time acquisition and analysis of dynamic data during processing makes it difficult to quickly adjust to real-time changes in material properties, thus affecting processing accuracy and consistency. Furthermore, existing methods struggle to accurately identify high-risk areas and potential defect locations during processing, and cannot achieve dynamic optimization of the processing path, which limits the quality and efficiency of aluminum frame processing.

[0004] Therefore, the key to solving the current technical challenges in aluminum frame processing lies in how to capture and process dynamic data on curvature changes and deformation behavior in real time, and accurately identify high-risk areas and dynamically adjust processing parameters to avoid defects and improve processing accuracy. Summary of the Invention

[0005] This application provides a data monitoring-based method, system, and storage medium for controlling deformation during aluminum frame processing, which can improve the processing quality of aluminum frames.

[0006] In a first aspect, this application provides a method for controlling deformation during aluminum frame processing based on data monitoring, the method comprising: Step S1: Obtain curvature change data and deformation behavior data of the aluminum frame surface through sensors, and process the curvature change data and deformation behavior data using image processing algorithms to obtain curvature distribution map and deformation vector field; Step S2: Based on the curvature distribution map and the deformation vector field, material properties are simulated using a preset neural network model to determine the stress distribution pattern and the location of potential processing defects; based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements; Step S3: If the speed adjustment requirement exceeds the preset speed threshold, the speed command sequence is generated by the control system to obtain a dynamic speed curve; Step S4: Extract key switching points from the dynamic speed curve to determine precise speed parameters; update the equipment control module according to the precise speed parameters, and use a feedback loop mechanism to monitor the execution effect and obtain real-time matching indicators; Step S5: If the real-time matching index is lower than the preset standard, then backtrack the curvature change data and the deformation behavior data, input the optimization algorithm to adjust the parameters of the neural network model, and determine the improved speed instruction sequence; Step S6: Drive the device to execute the processing path through the improved speed command sequence, collect subsequent deformation behavior data, and judge the overall processing quality. Secondly, this application provides a data monitoring-based aluminum frame processing deformation control system, the system comprising: The data acquisition and processing module is used to acquire curvature change data and deformation behavior data of the aluminum frame surface through sensors, and to process the curvature change data and deformation behavior data using image processing algorithms to obtain curvature distribution map and deformation vector field; The characteristic simulation and judgment module is used to simulate material characteristics based on the curvature distribution map and the deformation vector field through a preset neural network model to determine the stress distribution pattern and the location of potential processing defects; based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements; The instruction generation and adjustment module is used to generate a speed instruction sequence through the control system to obtain a dynamic speed curve if the speed adjustment requirement exceeds a preset speed threshold. The parameter extraction and update module is used to extract key switching points from the dynamic speed curve and determine precise speed parameters; the device control module is updated according to the precise speed parameters, and a feedback loop mechanism is used to monitor the execution effect and obtain real-time matching indicators. The model optimization and backtracking module is used to backtrack the curvature change data and the deformation behavior data if the real-time matching index is lower than the preset standard, input the optimization algorithm to adjust the parameters of the neural network model, and determine the improved speed instruction sequence; The execution verification and evaluation module is used to drive the device to execute the processing path through the improved speed command sequence, collect subsequent deformation behavior data, and judge the overall processing quality.

[0007] Thirdly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the aforementioned data monitoring-based aluminum frame processing deformation control method.

[0008] This application presents a data-monitoring-based deformation control method, system, and storage medium for aluminum frame processing, aiming to improve the accuracy and stability of aluminum frame processing through real-time monitoring and intelligent control. The solution utilizes a multi-sensor system to collect real-time data on the curvature changes and deformation behavior of the aluminum frame surface. Combined with image processing algorithms, it generates accurate curvature distribution maps and deformation vector fields, providing reliable data support for material property simulation. A preset neural network model extracts features from the data, and finite element analysis is used to accurately calculate the stress distribution pattern and potential processing defect locations of the aluminum frame. A real-time data fusion algorithm integrates equipment response delay information to promptly determine the need for processing speed adjustments, generating dynamic speed curves and optimizing the processing path. Furthermore, a feedback loop mechanism is employed to monitor the execution effect in real time, dynamically optimizing the neural network model and processing commands to ensure that the processing quality meets preset standards. This intelligent control method effectively addresses complex deformations in aluminum frame processing, improving processing accuracy, reducing defect rates, and significantly enhancing production efficiency and quality. It possesses broad application prospects in intelligent manufacturing and represents a technological advancement in the automation and intelligence of aluminum frame processing. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of the data monitoring-based aluminum frame processing deformation control method of this application; Figure 2 This is a flowchart of the data monitoring and delay indicator judgment process for this application; Figure 3 This application provides a dynamic optimization process for processing quality based on real-time matching indicators and model feedback. Figure 4 This is a schematic diagram of the aluminum frame processing deformation control system based on data monitoring according to this application. Detailed Implementation

[0011] This application provides a method, system, and storage medium for controlling deformation in aluminum frame processing based on data monitoring. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0012] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the aluminum frame processing deformation control method based on data monitoring in this application includes: Step S1: Acquire curvature change data and deformation behavior data of the aluminum frame surface using sensors, and process the curvature change data and deformation behavior data using image processing algorithms to obtain a curvature distribution map and a deformation vector field; specifically including: The original curvature change data and original deformation behavior data of the aluminum frame surface are collected by at least one sensor; noise filtering is performed on the original curvature change data and original deformation behavior data to obtain first curvature change data and first deformation behavior data; for the first curvature change data, an edge detection algorithm is used to extract curvature features and generate a curvature distribution map; for the first deformation behavior data, an optical flow algorithm is used to calculate the displacement vector and generate a deformation vector field; based on the curvature distribution map and deformation vector field, the geometric characteristics of the aluminum frame surface are determined; the accuracy of the curvature distribution map and deformation vector field is verified by the geometric characteristics; if the accuracy is lower than the preset standard, the parameters of the image processing algorithm are adjusted, and the curvature distribution map and deformation vector field are regenerated.

[0013] Specifically, real-time data on curvature changes and deformation behavior of the aluminum frame surface is collected using sensors. Data acquisition can be performed via laser sensors or contact curvature meters to ensure real-time data transmission. Gaussian filtering is used to remove noise from the raw data, resulting in clear curvature change and deformation behavior data. For the curvature change data, an edge detection algorithm is used to extract curvature features and generate a curvature distribution map. Edge detection typically identifies the edges of curvature by calculating the image gradient, using the following formula: in, and These represent the gradients of the image in the x and y directions, respectively. These are the pixel values ​​of the image; using these gradient values, key features of curvature changes can be extracted to generate a curvature distribution map; the optical flow algorithm is used to calculate the displacement vector of the deformation behavior data, generating a deformation vector field; the optical flow algorithm calculates the displacement within each time step by estimating the displacement vector of each pixel in the image, as shown in the following formula: in, and These represent the gradients of the image in the x and y directions, respectively. It is a change in the image along the time direction. and These are the displacement components in the x and y directions; this formula is used to estimate the displacement of each pixel in an image between consecutive frames, thereby generating a deformation vector field that reveals the direction and magnitude of deformation of the aluminum frame.

[0014] By combining curvature distribution maps and deformation vector fields, the geometric characteristics of the aluminum frame surface, such as curvature and torsion, are further analyzed. Based on these characteristics, the overall deformation of the aluminum frame can be checked and error correction can be performed. If the accuracy of the data is lower than the preset standard, the processing parameters are adjusted through optimization algorithms to regenerate a more accurate curvature map and deformation vector field. Through comprehensive analysis of curvature change trends and deformation behavior data, the change trend of the processing area is determined, and subsequent processing paths and speed adjustments are guided to ensure that the aluminum frame can be precisely controlled during processing, thereby optimizing processing quality and reducing the occurrence of defects.

[0015] Step S2: Based on the curvature distribution map and deformation vector field, simulate material properties using a preset neural network model to determine the stress distribution pattern and potential processing defect locations; based on the stress distribution pattern and potential processing defect locations, use a real-time data fusion algorithm to integrate equipment response delay information and determine speed adjustment requirements; In step S2, based on the curvature distribution diagram and deformation vector field, a preset neural network model is used to simulate material properties, determine the stress distribution pattern and the location of potential processing defects, including: The curvature distribution map and deformation vector field are used as input data. A pre-defined neural network model is used to extract features from the input data to obtain material property parameters. Based on these parameters, the stress distribution pattern on the aluminum frame surface is calculated. For the stress distribution pattern, finite element analysis is used to determine stress concentration regions. Based on these stress concentration regions, potential processing defects are identified. A defect distribution map is generated using these potential defect locations. Based on the defect distribution map and the stress distribution pattern, stress anomalies during processing are determined. If the stress at a stress anomaly location exceeds a pre-defined stress threshold, the potential processing defect location is marked as a high-risk area. The training data for the neural network model is updated using these high-risk areas.

[0016] Specifically, the curvature distribution map and deformation vector field are used as input data. A pre-defined neural network model is used for feature extraction to obtain material property parameters. For example, the neural network extracts local curvature features from the curvature distribution map through convolutional layers to identify the curved regions of the aluminum frame, and reduces the dimensionality of the deformation vector field through pooling layers. The neural network outputs material properties such as the elastic modulus and Poisson's ratio through fully connected layers. These parameters are helpful for subsequent stress calculations. Based on these material property parameters, the stress distribution pattern on the aluminum frame surface is calculated using a stress-strain relationship: in, For stress, For elastic modulus, For strain; taking the bending process of an aluminum frame as an example, the Young's modulus of the material reflects the stiffness of the aluminum material, helping to simulate the deformation response; strain is usually calculated by the ratio of the deformation to the original size, thus obtaining the stress distribution; the stress distribution is further processed using the finite element analysis method; the surface of the aluminum frame is divided into multiple small elements, and the stress value of each element is calculated; the finite element analysis is based on the following equilibrium equations: in, It is the volume of the region being analyzed. and It is the subscript for the coordinate direction. For stress tensor, The displacement component represents the position of each point in the aluminum frame. Displacement in the direction, The subscript indicating the direction of the coordinate axis. For the boundary surface, This is the boundary force; this method enables the precise identification of areas of stress concentration on the aluminum frame surface, especially in areas of high curvature.

[0017] Based on the stress concentration areas, a defect distribution map is generated and stress anomalies in the processing are identified. For example, if the stress value exceeds a set stress threshold, the area will be marked as a high-risk area, which is usually the source of cracks or other defects. The training data of the neural network model will be updated using the data from these high-risk areas, further improving the accuracy of defect prediction.

[0018] Furthermore, in step S2, based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements, including: The process involves: acquiring stress distribution patterns and potential machining defect locations; integrating equipment response delay information and machining equipment status data using a real-time data fusion algorithm to generate comprehensive machining status data; calculating the response delay index of the machining equipment based on the comprehensive machining status data; determining whether the machining speed meets real-time machining requirements based on the response delay index; identifying speed adjustment needs if the response delay index exceeds a preset delay range; generating speed adjustment priorities based on the speed adjustment needs; determining the critical adjustment areas of the machining equipment based on the speed adjustment priorities; generating speed adjustment requirement data based on the critical adjustment areas; and verifying the real-time response capability of the machining equipment using the speed adjustment requirement data.

[0019] Specifically, the stress distribution pattern and potential processing defect locations of the aluminum frame are obtained. These defect locations are simulated using a neural network model based on curvature distribution maps and deformation vector fields. The curvature distribution map reflects the curvature changes in different regions of the aluminum frame surface, while the deformation vector field shows the deformation behavior of the material during processing. A real-time data fusion algorithm is used to integrate equipment response delay information with the processing equipment's status data to generate comprehensive processing status data. Equipment response delay information refers to the time delay from receiving the instruction to actual execution, typically recorded using a built-in timer. Status data includes real-time monitoring data such as the equipment's rotational speed, load, and temperature. For example, in the aluminum frame bending processing scenario, the equipment's response delay and processing status data are fused, and a comprehensive processing status data is calculated using a Kalman filter algorithm. This algorithm, based on a weighted average of delay information and equipment status, can reflect the equipment's response bottlenecks under different loads or temperatures, providing an accurate basis for subsequent judgments. This fusion process helps reduce data noise and improve the reliability of judgments. Figure 2 The diagram illustrates the data monitoring and delay indicator assessment process.

[0020] Furthermore, based on comprehensive processing status data, the equipment's response delay index is calculated. This index, obtained by multiplying delay information with status data, quantifies whether the equipment can respond to processing demands in a timely manner. If the response delay index exceeds a preset range, it indicates that speed adjustment is required. Speed ​​adjustment demands are prioritized based on the severity of the defect location. In the case of multiple potential defect areas, the analytic hierarchy process (AHP) is used to calculate the priority of each area, typically with higher stress areas having higher priority. For example, in aluminum frame bending, when the stress distribution shows a defect located in a high curvature area, this area will be given higher priority to ensure speed adjustment before other areas, reducing the risk of cracks and deformation. Based on speed adjustment demands and priorities, key adjustment areas of the processing equipment are identified, and these areas are mapped to specific speed adjustment demand data. Verification is performed through an equipment simulation module to ensure that the equipment can respond promptly to new speed demands, thereby guaranteeing processing accuracy and stability during the aluminum frame processing.

[0021] Step S3: If the speed adjustment requirement exceeds the preset speed threshold, the control system generates a speed command sequence to obtain a dynamic speed curve; specifically including: The system acquires speed adjustment requests; if the speed adjustment requests exceed a preset speed threshold, it generates an initial speed command sequence through the control system; based on the initial speed command sequence, it calculates the speed change trend of the processing equipment; based on the speed change trend, it generates a dynamic speed curve; for the dynamic speed curve, it uses a smoothing algorithm to optimize the continuity of speed changes; based on the dynamic speed curve, it determines the speed distribution of the processing equipment in different processing areas; based on the speed distribution, it verifies the stability of the dynamic speed curve; if the stability is lower than a preset standard, it adjusts the parameters of the initial speed command sequence and regenerates the dynamic speed curve; based on the dynamic speed curve, it determines the speed control strategy for the processing equipment.

[0022] Specifically, the system acquires speed adjustment requests and determines whether these requests exceed a preset speed threshold. If they do, the system generates an initial speed command sequence. For example, in the precision machining of aluminum frames, when significant curvature changes or high-stress areas occur during machining, the system may need to adjust the speed to avoid machining defects. Based on the initial speed command sequence, the speed change trend of the machining equipment is calculated. This step helps to understand whether the equipment's speed changes meet the machining requirements and whether appropriate adjustments can be made in different machining areas. By analyzing the speed change trend, a dynamic speed curve is generated. This curve reflects how smoothly the equipment changes speed during machining to adapt to different machining needs. For example, in areas with high curvature or large deformation, the dynamic speed curve may show lower speed changes to reduce stress concentration and prevent cracks or deformation. To ensure machining stability, a smoothing algorithm is used to optimize the speed changes on the dynamic speed curve. For example, if some abrupt changes in the curve cause unstable response of the machining equipment, the smoothing algorithm will adjust the slope of the curve to make the speed changes smoother, thereby reducing vibration and unnecessary load. Based on the optimized dynamic speed curve...

[0023] Furthermore, the speed distribution of the processing equipment in different processing areas is analyzed. For example, the speed distribution may differ significantly between straight and curved sections in aluminum frame processing. In straight sections, the equipment can operate faster, while in curved sections, the speed needs to be reduced to minimize material deformation. By verifying the speed distribution, the stability of the dynamic speed curve is checked. If the stability of the curve is found to be lower than the preset standard, such as slow equipment response or inconsistent speed changes in certain areas, the parameters of the initial speed command sequence will be adjusted to regenerate a smoother and more stable dynamic speed curve. Based on the optimized dynamic speed curve, the speed control strategy of the processing equipment is determined. For example, during the bending process of the aluminum frame, the system may reduce the equipment speed in high-stress areas to avoid excessive deformation or crack formation, while in straight sections, the speed may be increased to improve efficiency. Through this refined speed control strategy, it is ensured that the equipment can accurately respond to processing requirements throughout the entire processing process, maintaining the required processing accuracy and stability, thereby improving processing quality and reducing the defect rate.

[0024] Step S4: Extract key switching points from the dynamic speed curve and determine precise speed parameters; update the equipment control module based on the precise speed parameters, and use a feedback loop mechanism to monitor the execution effect and obtain real-time matching indicators; In step S4, key switching points are extracted from the dynamic speed curve to determine precise speed parameters, including: The process involves: acquiring dynamic speed curves; extracting key switching points from the dynamic speed curves using curve analysis algorithms; determining speed requirements for high and low curvature regions based on these key switching points; calculating precise speed parameters using linear interpolation based on the speed requirements; generating a speed parameter table based on the precise speed parameters; verifying the applicability of the precise speed parameters based on the speed parameter table; adjusting the parameters of the linear interpolation method and recalculating the precise speed parameters if the applicability is lower than the preset standard; determining the speed switching strategy for the processing equipment based on the precise speed parameters; and optimizing the speed allocation of the processing path based on the speed switching strategy.

[0025] Specifically, dynamic speed curves are acquired, and key switching points are extracted using curve analysis algorithms. These points typically occur in areas of significant speed variation, i.e., speed changes exceeding a set threshold, such as transitions from low to high speed. For these switching points, speed requirements for high-curvature and low-curvature regions are determined. For example, high-curvature areas of the aluminum frame require lower speeds to reduce deformation, while flat areas can utilize higher speeds to improve efficiency. Next, precise speed parameters are calculated using linear interpolation to ensure smooth transitions between different regions. Then, a speed parameter table is generated based on the precise speed parameters, and its applicability is verified. Different processing scenarios are simulated to check if the parameters meet accuracy requirements. If the parameters do not meet the standards, the interpolation method is adjusted, and the speed parameters are recalculated to ensure continuity and accuracy during processing. Based on the precise speed parameters, a speed switching strategy for the processing equipment is determined, and the speed allocation of the processing path is optimized. This speed switching strategy ensures smoother transitions between different processing areas, avoiding abrupt changes or instability. For example, during aluminum frame bending, lower speeds are used in high-curvature areas, while higher speeds are used in low-curvature areas, thereby optimizing processing quality and production efficiency.

[0026] In step S4, the equipment control module is updated based on the precise speed parameters, and a feedback loop mechanism is used to monitor the execution effect to obtain real-time matching indicators, including: The system acquires precise speed parameters; updates the control parameters of the equipment control module using these precise speed parameters; employs a feedback loop mechanism to collect real-time execution data from the processing equipment; calculates the real-time matching index of the processing equipment based on the execution data; determines whether the execution effect of the processing equipment meets the preset standard based on the real-time matching index; if the real-time matching index is lower than the preset standard, records the deviation value of the execution data; adjusts the control parameters of the equipment control module based on the deviation value; re-executes the processing path using the adjusted control parameters; and updates the real-time matching index based on the re-executed processing path.

[0027] Specifically, by acquiring precise speed parameters, the control parameters in the equipment control module are updated. These parameters affect the equipment's speed adjustment during processing to ensure accuracy and stability. A feedback loop mechanism is used to collect real-time execution data from the processing equipment, such as its current speed, load, and temperature. This data is used to calculate the real-time matching index of the processing equipment to evaluate its performance. By analyzing the real-time matching index, it is determined whether the processing equipment has met the preset processing standards. For example, if the equipment's speed control fails to meet the set accuracy requirements, the real-time matching index may be lower than the expected standard. In this case, the system records the deviation value of the execution data, analyzes the differences, and adjusts the parameters of the equipment control module based on these deviations. The adjusted control parameters affect the equipment's speed adjustment, ensuring greater accuracy in subsequent processing. After updating the control parameters, the equipment re-executes the processing path, collecting new execution data during execution and updating the real-time matching index based on the re-executed processing path. This method, through continuous adjustment and optimization, ensures that real-time feedback during processing can respond promptly to the equipment's operating status.

[0028] Step S5: If the real-time matching index is lower than the preset standard, then backtrack the curvature change data and deformation behavior data, input the data into the optimization algorithm to adjust the neural network model parameters, and determine the improved speed instruction sequence; specifically including: The process involves: acquiring real-time matching metrics; if the real-time matching metrics are lower than a preset standard, then backtracking on curvature change data and deformation behavior data; analyzing the curvature change data and deformation behavior data using an optimization algorithm to generate an adjustment parameter set; updating the parameters of the neural network model based on the adjustment parameter set; resimulating material properties using the updated neural network model to obtain a new stress distribution pattern; generating an improved speed command sequence based on the new stress distribution pattern; verifying the response effect of the processing equipment using the improved speed command sequence; if the response effect is lower than a preset standard, readjusting the parameters of the optimization algorithm; and optimizing the control strategy of the processing equipment by improving the speed command sequence.

[0029] Specifically, real-time matching indicators are acquired to assess whether the processing equipment has met the predetermined processing quality standards. If the real-time matching indicators are lower than the preset standards, it indicates that the equipment's performance has not met expectations. In this case, it is necessary to backtrack on curvature change data and deformation behavior data. The optimization algorithm analyzes this data to identify the key factors causing the execution deviation and generates a set of adjustment parameters. These parameters are used to update the parameters of the neural network model to better reflect changes in material properties in subsequent simulations. The updated neural network model will then re-simulate material properties based on the new parameters to obtain an updated stress distribution pattern. This new stress distribution pattern will provide a basis for improving the speed command sequence to ensure more precise speed adjustment during processing. Subsequently, the system will generate an improved speed command sequence and verify the processing equipment's response effect using these commands. If the processing equipment's response effect is still lower than the preset standards, the optimization algorithm will be readjusted to optimize the model's accuracy. Through this feedback loop, the improved speed command sequence will continuously optimize the processing equipment's control strategy, ensuring precise execution of the processing path. This dynamic adjustment can significantly improve the processing equipment's response accuracy and processing quality. Figure 3 The figure illustrates the dynamic optimization process of processing quality based on real-time matching indicators and model feedback.

[0030] Step S6: Drive the equipment to execute the processing path by improving the speed command sequence, collect subsequent deformation behavior data, and judge the overall processing quality; specifically including: The process involves: acquiring an improved speed command sequence; driving the equipment to execute the machining path using the improved speed command sequence; collecting subsequent deformation behavior data in real time during the machining process; generating machining quality assessment data based on the subsequent deformation behavior data; determining whether the overall machining quality meets the preset requirements based on the machining quality assessment data; recording anomalies in the subsequent deformation behavior data if the overall machining quality is lower than the preset requirements; adjusting the parameters of the improved speed command sequence based on the anomalies; re-executing the machining path using the adjusted improved speed command sequence; and updating the machining quality assessment data based on the re-executed machining path.

[0031] Specifically, the system acquires an improved sequence of speed commands, which drive the processing equipment to execute processing tasks along an optimized path. Taking the processing of aluminum alloy frames as an example, when the processing equipment starts working according to the adjusted speed commands, it will execute the path according to a precise speed curve and adjusted control parameters to ensure the efficiency and accuracy of the processing. During execution, the system will collect deformation behavior data generated during processing in real time. This data reflects the dynamic response of the material during processing, especially the deformation in high-stress areas. For example, if an area of ​​the aluminum frame develops irregular bending or cracks due to uneven cutting forces, the system will capture this deformation information immediately.

[0032] Based on this subsequent deformation behavior data, the system generates processing quality assessment data. This data is used to measure the deviation between the actual performance of the material and the expected target during processing. For example, if a microcrack is found on the material surface during the processing of a curved area, the system will conduct a detailed analysis of that area to assess whether it meets the preset quality standards. By analyzing the processing quality assessment data, the system can determine whether the overall processing quality meets the preset requirements. If the overall processing quality is lower than the expected standard, the system will record anomalies in the deformation behavior data. These anomalies may be microcracks or uneven deformation in high-stress areas. By analyzing these anomalies, the system can promptly identify potential problems and respond accordingly.

[0033] Based on these anomalies, the system adjusts and improves the parameters of the speed command sequence to optimize the machining process and eliminate potential defects. For example, at the high curvature of the aluminum alloy frame, if deformation exceeding the standard is detected, the system will adjust the speed curve in a timely manner to reduce stress concentration during machining. The adjusted speed command sequence will be reapplied to the machining path execution. By re-executing the machining path, the system will update the machining quality assessment data. Through this closed-loop feedback mechanism of continuous adjustment and optimization, the system can continuously improve machining accuracy and quality, ensuring that the final product meets quality standards. Through this feedback mechanism, the machining process of the aluminum alloy frame can not only adjust the speed in real time, but also significantly reduce cracks or other quality problems caused by unstable stress distribution, thereby greatly improving the product yield.

[0034] It is understood that the executing entity of this application can be a data monitoring-based aluminum frame processing deformation control system, or it can be a terminal or a server; no specific limitation is made here. This application's embodiment uses a server as the executing entity for illustration.

[0035] The above describes the aluminum frame processing deformation control method based on data monitoring in the embodiments of this application. The following describes the aluminum frame processing deformation control system based on data monitoring in the embodiments of this application. Please refer to [link / reference]. Figure 4 One embodiment of the aluminum frame processing deformation control system based on data monitoring in this application includes: The data acquisition and processing module 201 is used to acquire curvature change data and deformation behavior data of the aluminum frame surface through sensors, and to process the curvature change data and deformation behavior data using image processing algorithms to obtain curvature distribution map and deformation vector field. The characteristic simulation and judgment module 202 is used to simulate material characteristics based on the curvature distribution map and deformation vector field through a preset neural network model to determine the stress distribution pattern and the location of potential processing defects; based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements; The instruction generation and adjustment module 203 is used to generate a speed instruction sequence through the control system to obtain a dynamic speed curve if the speed adjustment requirement exceeds the preset speed threshold. The parameter extraction and update module 204 is used to extract key switching points from the dynamic speed curve and determine precise speed parameters; update the equipment control module according to the precise speed parameters, and use a feedback loop mechanism to monitor the execution effect and obtain real-time matching indicators. The model optimization and backtracking module 205 is used to backtrack the curvature change data and deformation behavior data if the real-time matching index is lower than the preset standard, input the optimization algorithm to adjust the neural network model parameters, and determine the improved speed instruction sequence; The execution verification and evaluation module 206 is used to drive the device to execute the processing path by improving the speed command sequence, collect subsequent deformation behavior data, and judge the overall processing quality.

[0036] The aluminum frame processing deformation control system based on data monitoring in this embodiment achieves precise control of the aluminum frame processing process through the collaborative work of multiple modules. The data acquisition and processing module acquires real-time data on the curvature changes and deformation behavior of the aluminum frame surface through sensors, and generates curvature distribution maps and deformation vector fields using image processing algorithms, providing a basis for subsequent analysis. Based on this data, the characteristic simulation and judgment module uses a neural network model to simulate material properties, determine the stress distribution pattern and potential processing defect locations, and integrates equipment response delay information through a real-time data fusion algorithm to determine whether the processing speed needs adjustment. The instruction generation and adjustment module... Based on the judgment results, a speed command sequence is generated to obtain a dynamic speed curve. The parameter extraction and update module extracts key switching points and optimizes speed parameters. If the real-time matching index does not meet the standard, the model optimization and backtracking module will backtrack the curvature change and deformation data, adjust the neural network model parameters through optimization algorithms, and improve the speed command sequence. The execution verification and evaluation module drives the equipment to execute the processing path through the improved speed command sequence, collects subsequent deformation behavior data, and judges the overall processing quality. Through the collaborative work of the above modules, this system can achieve dynamic optimization and precise control of the processing process, thereby effectively avoiding defects and improving the quality and efficiency of aluminum frame processing.

[0037] above Figure 4 The data monitoring-based aluminum frame processing deformation control system in this embodiment of the invention is described in detail from the perspective of modular functional entities.

[0038] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the data monitoring-based aluminum frame processing deformation control method.

[0039] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the system described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0040] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0041] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling deformation during aluminum frame processing based on data monitoring, characterized in that, The method includes: Step S1: Obtain curvature change data and deformation behavior data of the aluminum frame surface through sensors, and process the curvature change data and deformation behavior data using image processing algorithms to obtain curvature distribution map and deformation vector field; Step S2: Based on the curvature distribution map and the deformation vector field, material properties are simulated using a preset neural network model to determine the stress distribution pattern and the location of potential processing defects; based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements; Step S3: If the speed adjustment requirement exceeds the preset speed threshold, the speed command sequence is generated by the control system to obtain a dynamic speed curve; Step S4: Extract key switching points from the dynamic speed curve to determine precise speed parameters; update the equipment control module according to the precise speed parameters, and use a feedback loop mechanism to monitor the execution effect and obtain real-time matching indicators; Step S5: If the real-time matching index is lower than the preset standard, then backtrack the curvature change data and the deformation behavior data, input the optimization algorithm to adjust the parameters of the neural network model, and determine the improved speed instruction sequence; Step S6: Drive the device to execute the processing path through the improved speed command sequence, collect subsequent deformation behavior data, and judge the overall processing quality.

2. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 1, characterized in that, Step S1 includes: At least one sensor is used to collect raw curvature change data and raw deformation behavior data of the aluminum frame surface; noise filtering is applied to the raw curvature change data and raw deformation behavior data to obtain first curvature change data and first deformation behavior data; for the first curvature change data, an edge detection algorithm is used to extract curvature features to generate the curvature distribution map; for the first deformation behavior data, an optical flow algorithm is used to calculate the displacement vector to generate the deformation vector field; the geometric characteristics of the aluminum frame surface are determined based on the curvature distribution map and the deformation vector field; the accuracy of the curvature distribution map and the deformation vector field is verified by the geometric characteristics; if the accuracy is lower than a preset standard, the parameters of the image processing algorithm are adjusted, and the curvature distribution map and the deformation vector field are regenerated.

3. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 1, characterized in that, In step S2, based on the curvature distribution diagram and the deformation vector field, a preset neural network model is used to simulate material properties and determine the stress distribution pattern and potential processing defect locations, including: The curvature distribution map and the deformation vector field are used as input data; features are extracted from the input data using a preset neural network model to obtain material property parameters; the stress distribution pattern on the aluminum frame surface is calculated based on the material property parameters; stress concentration regions are determined using finite element analysis for the stress distribution pattern; potential processing defect locations are identified based on the stress concentration regions; a defect distribution map is generated based on the potential processing defect locations; stress anomaly points during processing are determined based on the defect distribution map and the stress distribution pattern; if the stress at the stress anomaly point exceeds a preset stress threshold, the potential processing defect location is marked as a high-risk area; the training data of the neural network model is updated based on the high-risk areas.

4. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 3, characterized in that, In step S2, based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements, including: The process involves: acquiring the stress distribution pattern and the location of potential processing defects; integrating equipment response delay information and processing equipment status data using a real-time data fusion algorithm to generate comprehensive processing status data; calculating the response delay index of the processing equipment based on the comprehensive processing status data; determining whether the processing speed meets real-time processing requirements based on the response delay index; identifying speed adjustment requirements if the response delay index exceeds a preset delay range; generating speed adjustment priorities based on the speed adjustment priorities; determining the key adjustment areas of the processing equipment based on the key adjustment areas; and verifying the real-time response capability of the processing equipment using the speed adjustment requirement data.

5. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 1, characterized in that, Step S3 includes: The system acquires the speed adjustment requirement; if the speed adjustment requirement exceeds a preset speed threshold, it generates an initial speed command sequence through the control system; based on the initial speed command sequence, it calculates the speed change trend of the processing equipment; based on the speed change trend, it generates a dynamic speed curve; for the dynamic speed curve, it uses a smoothing algorithm to optimize the continuity of speed changes; based on the dynamic speed curve, it determines the speed distribution of the processing equipment in different processing areas; based on the speed distribution, it verifies the stability of the dynamic speed curve; if the stability is lower than a preset standard, it adjusts the parameters of the initial speed command sequence and regenerates the dynamic speed curve; based on the dynamic speed curve, it determines the speed control strategy of the processing equipment.

6. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 1, characterized in that, In step S4, key switching points are extracted from the dynamic speed curve to determine precise speed parameters, including: The process involves: acquiring the dynamic speed curve; extracting key switching points from the dynamic speed curve using a curve analysis algorithm; determining the speed requirements for high-curvature and low-curvature regions based on the key switching points; calculating precise speed parameters using a linear interpolation method based on the speed requirements; generating a speed parameter table using the precise speed parameters; verifying the applicability of the precise speed parameters based on the speed parameter table; adjusting the parameters of the linear interpolation method and recalculating the precise speed parameters if the applicability is lower than a preset standard; determining the speed switching strategy for the processing equipment based on the precise speed parameters; and optimizing the speed allocation of the processing path based on the speed switching strategy.

7. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 6, characterized in that, In step S4, the equipment control module is updated according to the precise speed parameters, and a feedback loop mechanism is used to monitor the execution effect to obtain real-time matching indicators, including: The precise speed parameters are obtained; the control parameters of the equipment control module are updated using the precise speed parameters; a feedback loop mechanism is used to collect the execution data of the processing equipment in real time; the real-time matching index of the processing equipment is calculated based on the execution data; the execution effect of the processing equipment is judged to meet the preset standard based on the real-time matching index; if the real-time matching index is lower than the preset standard, the deviation value of the execution data is recorded; the control parameters of the equipment control module are adjusted based on the deviation value; the processing path is re-executed using the adjusted control parameters; and the real-time matching index is updated based on the re-executed processing path.

8. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 1, characterized in that, Step S5 includes: The process involves: acquiring the real-time matching index; if the real-time matching index is lower than a preset standard, then backtracking the curvature change data and the deformation behavior data; analyzing the curvature change data and the deformation behavior data using an optimization algorithm to generate an adjustment parameter set; updating the parameters of the neural network model based on the adjustment parameter set; resimulating material properties using the updated neural network model to obtain a new stress distribution pattern; generating an improved speed command sequence based on the new stress distribution pattern; verifying the response effect of the processing equipment using the improved speed command sequence; if the response effect is lower than a preset standard, readjusting the parameters of the optimization algorithm; and optimizing the control strategy of the processing equipment using the improved speed command sequence.

9. The method for controlling deformation during aluminum frame processing based on data monitoring according to claim 1, characterized in that, Step S6 includes: The process involves: acquiring the improved speed command sequence; driving the device to execute the processing path using the improved speed command sequence; collecting subsequent deformation behavior data during the processing in real time; generating processing quality evaluation data based on the subsequent deformation behavior data; determining whether the overall processing quality meets preset requirements based on the processing quality evaluation data; recording anomalies in the subsequent deformation behavior data if the overall processing quality is lower than the preset requirements; adjusting the parameters of the improved speed command sequence based on the anomalies; re-executing the processing path using the adjusted improved speed command sequence; and updating the processing quality evaluation data based on the re-executed processing path.

10. A data-monitoring-based aluminum frame processing deformation control system, used to implement the data-monitoring-based aluminum frame processing deformation control method as described in any one of claims 1 to 9, characterized in that, The data monitoring-based aluminum frame processing deformation control system includes: The data acquisition and processing module is used to acquire curvature change data and deformation behavior data of the aluminum frame surface through sensors, and to process the curvature change data and deformation behavior data using image processing algorithms to obtain curvature distribution map and deformation vector field; The characteristic simulation and judgment module is used to simulate material characteristics based on the curvature distribution map and the deformation vector field through a preset neural network model to determine the stress distribution pattern and the location of potential processing defects; based on the stress distribution pattern and the location of potential processing defects, a real-time data fusion algorithm is used to integrate equipment response delay information to determine speed adjustment requirements; The instruction generation and adjustment module is used to generate a speed instruction sequence through the control system to obtain a dynamic speed curve if the speed adjustment requirement exceeds a preset speed threshold. The parameter extraction and update module is used to extract key switching points from the dynamic speed curve and determine precise speed parameters; the device control module is updated according to the precise speed parameters, and a feedback loop mechanism is used to monitor the execution effect and obtain real-time matching indicators. The model optimization and backtracking module is used to backtrack the curvature change data and the deformation behavior data if the real-time matching index is lower than the preset standard, input the optimization algorithm to adjust the parameters of the neural network model, and determine the improved speed instruction sequence; The execution verification and evaluation module is used to drive the device to execute the processing path through the improved speed command sequence, collect subsequent deformation behavior data, and judge the overall processing quality.

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