Multi-head milling machine feeding control system based on workpiece characteristics

Abstract

The invention provides a multi-head milling machine feeding control system based on workpiece characteristics, and relates to the technical field of milling machining. A workpiece transfer module; the feature recognition module is used for performing workpiece feature recognition to obtain a workpiece structure feature set; the scheme matching module is used for carrying out feeding scheme matching to obtain an initial feeding scheme set; the fitting relation establishing module is used for establishing a function fitting relation between the machining evaluation parameters and the feeding control parameters; the feed parameter optimization module is used for performing feed parameter optimization on the initial feed scheme set to obtain a target feed scheme; and the feeding control module is used for carrying out feeding control on the milling assembly set according to the target feeding scheme. The technical problems that a traditional multi-head milling machine feeding control system usually conducts control according to fixed parameters, accurate analysis on machining parameters is lacked, adaptability is lacked, and the optimal machining effect cannot be achieved are solved.

Description

Technical Field

[0001] The invention relates to the technical field of milling processing, and in particular to a multi-head milling machine feed control system based on workpiece features. Background Art

[0002] Milling machine feed control systems are mainly used in milling processing in the manufacturing industry, which usually has strict requirements on the processing accuracy, surface quality and processing efficiency of the workpiece. However, the existing technology still has certain problems with the feed control of milling machines. On the one hand, the traditional system is mainly based on empirical rules or simple parameter presets, and cannot perform precise parameter control according to the specific workpiece characteristics, resulting in instability during the processing process, affecting the surface quality and processing accuracy of the workpiece; on the other hand, the traditional system usually relies on simple image processing technology or manual detection to identify the features of the workpiece. This method is not only inefficient, but also prone to omissions and recognition errors. Summary of the invention

[0003] The present application provides a multi-head milling machine feed control system based on workpiece features, aiming to solve the technical problem that traditional multi-head milling machine feed control systems are usually controlled according to fixed parameters, lack of precise analysis of processing parameters, resulting in lack of adaptability and inability to achieve optimal processing effects.

[0004] The present application discloses a multi-head milling machine feed control system based on workpiece features, the system comprising: an information interaction module, the information interaction module is used to interactively obtain material information and processing requirement information of the target workpiece when the target workpiece is transferred to a first working area, wherein the processing requirement information includes a target milling index; a workpiece transfer module, the workpiece transfer module is used to transfer the target workpiece to a second working area, wherein the second working area includes a workpiece image acquisition component and a workpiece feature recognition component; a feature recognition module, the feature recognition module is used to perform image acquisition on the target workpiece through the workpiece image acquisition component, perform workpiece feature recognition on the image acquisition result through the workpiece feature recognition component, and obtain a workpiece structural feature set; a solution matching module, the solution matching module is used to, based on the material information and the workpiece structural feature set, Feed scheme matching is performed in a preset feed scheme library to obtain an initial feed scheme set; a fitting relationship establishment module is used to establish a function fitting relationship between the processing evaluation parameters of the target workpiece and the feed control parameters of the target multi-head milling machine, wherein the processing evaluation parameters correspond to the target milling index; a feed parameter optimization module is used to optimize the feed parameters of the initial feed scheme set according to the function fitting relationship and take the target milling index as the optimization target to obtain a target feed scheme; a feed control module is used to transfer the target workpiece to a third working area, the third working area includes a milling component set and a workpiece fixing component, the target workpiece is fixed according to the workpiece fixing component, and the feed of the milling component set is controlled according to the target feed scheme.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the information interaction module, the system can accurately and interactively obtain material information and processing requirement information when the workpiece enters the first working area, ensuring the rationality and accuracy of the processing parameters; the feature recognition module performs comprehensive image acquisition and feature recognition on the workpiece through the image acquisition component and the feature recognition component, and accurately obtains the structural feature set of the workpiece; the solution matching module performs precise matching from the preset feed solution library according to the material information and the workpiece structural feature set, and generates an initial feed solution set to ensure the adaptability of the feed parameters; the fitting relationship establishment module and the feed parameter optimization module optimize the initial feed solution for the target milling index by establishing a function fitting relationship and parameter optimization, obtain the optimal target feed solution, and improve the processing quality and efficiency; the feed control module performs feed control of the milling component according to the target feed solution to ensure the stability and efficiency of the processing process.

[0006] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic diagram of the structure of a multi-head milling machine feed control system based on workpiece features is provided for the embodiment of the present application; Figure 2 A schematic diagram of the execution flow of an image acquisition result acquisition module in a multi-head milling machine feed control system based on workpiece features is provided for an embodiment of the present application.

[0008] Explanation of the reference numerals: information interaction module 10 , workpiece transmission module 20 , feature recognition module 30 , solution matching module 40 , fitting relationship establishment module 50 , feed parameter optimization module 60 , feed control module 70 . DETAILED DESCRIPTION

[0009] The embodiment of the present application solves the technical problem that the traditional multi-head milling machine feed control system is usually controlled according to fixed parameters, lacks precise analysis of processing parameters, resulting in lack of adaptability and inability to achieve optimal processing effects by providing a multi-head milling machine feed control system based on workpiece features.

[0010] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0011] like Figure 1 As shown, the embodiment of the present application provides a multi-head milling machine feed control system based on workpiece features, the system comprising: The information interaction module 10 is used to interactively acquire material information and processing requirement information of the target workpiece when the target workpiece is transferred to the first working area, wherein the processing requirement information includes a target milling index.

[0012] When the target workpiece is transferred to the first working area, the sensor in the area detects the arrival of the workpiece, triggering the start of the information interaction module 10, and reading the material identification label or barcode on the target workpiece through sensors, such as RFID readers, barcode scanners, etc., which pre-store the material information of the workpiece, or querying the background database through the unique identification code of the workpiece, such as the serial number, to obtain the material information.

[0013] The processing requirement information is pre-stored in the system's database. The corresponding processing requirement information is extracted from the database through the identification code of the target workpiece. This information includes target milling indicators, such as milling accuracy, surface roughness, etc.

[0014] The workpiece conveying module 20 is used to convey the target workpiece to a second working area, wherein the second working area includes a workpiece image acquisition component and a workpiece feature recognition component.

[0015] After completing information acquisition in the first working area, the target workpiece is moved from the first working area to the second working area. The second working area includes a workpiece image acquisition component and a workpiece feature recognition component. The workpiece image acquisition component includes one or more fixed image acquisition devices, such as a camera, a laser scanner, etc. The workpiece feature recognition component includes a structural feature recognition model for performing image processing on the acquired workpiece image and identifying the structural features of the workpiece.

[0016] The feature recognition module 30 is used to perform image acquisition on the target workpiece through the workpiece image acquisition component, perform workpiece feature recognition on the image acquisition result through the workpiece feature recognition component, and obtain a workpiece structural feature set.

[0017] After the feature recognition module 30 receives the signal that the workpiece transfer is completed, it starts the workpiece image acquisition component, automatically focuses and calibrates the workpiece, and performs image acquisition when ready. Since the workpiece requires multi-angle image acquisition, the system controls the rotating table to rotate the workpiece in a predetermined angle sequence. An image is acquired each time the workpiece rotates one angle, and the surface image of the workpiece is captured at each predetermined angle to ensure that all key surface features of the workpiece are fully covered and the image acquisition results are obtained.

[0018] The collected images are denoised, and image enhancement technology is used to improve the clarity and contrast of the images. For images collected at multiple angles, image alignment is performed so that the images at each angle can accurately correspond to different parts of the workpiece. The structural feature recognition model is used to identify the feature areas in the image to represent the structural features of the workpiece.

[0019] The scheme matching module 40 is used to match the feeding schemes in a preset feeding scheme library according to the material information and the workpiece structure feature set, and obtain an initial feeding scheme set.

[0020] Load the preset feed plan library, which stores feed plans corresponding to various materials and structural features. These plans include parameters such as cutting speed, feed rate, tool selection, cutting path, cutting depth, etc.

[0021] According to the received material information, the preset feeding scheme library is traversed to screen out the feeding scheme that matches the material characteristics of the target workpiece to form a preliminary feeding scheme set; according to the workpiece structural feature set, the preset feeding scheme library is screened to find the feeding scheme that matches the specific structural characteristics of the workpiece.

[0022] The results of material information matching and structural feature matching are cross-validated so that the selected scheme meets the requirements of both material and structural features. The cross-validated schemes are combined to generate an initial feed scheme set. Each scheme includes a combination of multiple parameters, such as cutting speed, feed speed, tool selection, cutting path, cutting depth, etc.

[0023] The fitting relationship establishing module 50 is used to establish a functional fitting relationship between the machining evaluation parameters of the target workpiece and the feed control parameters of the target multi-head milling machine, wherein the machining evaluation parameters correspond to the target milling index.

[0024] Determine the machining evaluation parameters of the target workpiece, which include surface roughness, machining accuracy, machining time, tool wear, etc., corresponding to the target milling indicators, extract key feed control parameters from the initial feed scheme set, including cutting speed, feed rate, cutting depth, cutting path, tool selection, etc., retrieve historical machining data with these key feed control parameters as constraints, extract the machining evaluation parameters of the corresponding workpiece and the corresponding feed control parameters as part of the training data set.

[0025] Select a function fitting model, such as linear regression, with the input variable being the feed control parameter and the output variable being the processing evaluation parameter. Divide the data set into a training set and a validation set for model training and validation. Use the training set to train the model, minimize the error function by adjusting the model parameters, use the validation set to test the trained model, evaluate the model's prediction accuracy, adjust the model parameters based on the evaluation results, and further improve the model's accuracy. Iterate the optimization process to determine the final fitting function form, which can accurately express the relationship between the feed control parameters and the processing evaluation parameters.

[0026] The feed parameter optimization module 60 is used to optimize the feed parameters of the initial feed scheme set according to the function fitting relationship and take the target milling index as the optimization target to obtain the target feed scheme.

[0027] Determine the optimization target, that is, the target milling index, including surface roughness, machining accuracy, machining time, tool wear, etc. According to the fitting relationship model, the target milling index is converted into an objective function. The objective function is a function of the feed parameter, with the purpose of optimizing the processing evaluation parameters, for example, minimizing the surface roughness or maximizing the processing efficiency, defining the constraints of the feed parameters, including the upper and lower limits of the cutting speed, feed speed, cutting depth, etc., selecting a suitable optimization algorithm, such as genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, etc., initializing the parameters of the optimization algorithm, including population size, number of iterations, convergence criteria, etc., taking the initial feed scheme set as the initial solution set of the optimization algorithm, through these initial schemes, the algorithm starts to search for the optimal solution, in each iteration, the optimization algorithm calculates the evaluation value of the current feed scheme according to the objective function, adjusts the feed control parameters according to the evaluation value, updates the parameter value of the optimization algorithm, gradually approaches the optimal solution, and judges whether the convergence conditions are met, such as the number of iterations reaches the upper limit or the change in the evaluation value is less than the set threshold, and determines whether to continue the iteration according to the judgment result.

[0028] After optimization, the candidate optimal solution is finally generated, which is verified to ensure that it meets all constraints and can achieve the expected target milling indicators, ensuring that it can be applied in actual production. After verification, the final target feeding solution is output.

[0029] A feed control module 70 is used to transfer the target workpiece to a third working area, wherein the third working area includes a milling component set and a workpiece fixing component, the target workpiece is fixed according to the workpiece fixing component, and the feed of the milling component set is controlled according to the target feeding scheme.

[0030] Transfer the target workpiece to the third working area, and adjust the position and clamping force of the workpiece fixing assembly according to the size and shape of the target workpiece to ensure that the workpiece is stable and immobile. Check the status of the milling assembly, including the wear of the tool, etc., and replace and install the tool if necessary according to the tool selection parameters in the target feed plan. According to the feed control parameters in the target feed plan, such as cutting speed, feed speed, cutting depth, cutting path, etc., configure them in the CNC system, load the corresponding machining program to match it with the target feed plan, start the milling assembly, and perform machining operations according to the set feed control parameters. In this way, the feeding process of the milling assembly can be accurately controlled to ensure the machining quality and efficiency of the target workpiece.

[0031] Furthermore, if Figure 2 As shown, the system also includes an image acquisition result acquisition module to perform the following operation steps: The second working area also includes a rotating table; geometric data of the target workpiece and a first contact surface are acquired, wherein the first contact surface is an initial contact surface between the target workpiece and the rotating table; a rotation angle sequence is generated according to the geometric data, and the rotating table is controlled to rotate the target workpiece according to the rotation angle sequence; after each rotation operation, the target workpiece is imaged by the workpiece image acquisition component until the first contact surface is reached again, and a plurality of surface images of the target workpiece are output; and the plurality of surface images are aligned to obtain the image acquisition result.

[0032] Specifically, the second working area includes not only a workpiece image acquisition component and a workpiece feature recognition component, but also a rotating table. After the target workpiece is transferred to the second working area, it is placed on the rotating table.

[0033] Using sensors installed in the second working area, such as laser scanners and 3D cameras, geometric data of the target workpiece is obtained, including the size and shape of the workpiece. The obtained geometric data is processed and analyzed, for example, a 3D model of the workpiece is generated using 3D software to determine the initial contact surface between the target workpiece and the turntable, that is, the initial placement surface of the workpiece on the turntable.

[0034] According to the acquired geometric data, the structural features of the target workpiece are analyzed, the key surfaces that need to be imaged are determined, the rotation angle required for each key surface is calculated, and a rotation angle sequence is generated. For example, if images of four sides need to be collected, the rotation angle sequence is 90 degrees, 180 degrees, and 270 degrees.

[0035] According to the generated rotation angle sequence, the rotation angle of the rotary table is set, the rotary table is started, and the target workpiece is rotated in sequence according to the set angle sequence.

[0036] After each rotation to the specified angle, the workpiece image acquisition component is used to capture the image of the target workpiece, and the surface image data at different angles are recorded. The rotation operation and image acquisition are repeated until the rotating table returns to the initial contact surface position again, and all surface image data during the entire rotation process are recorded.

[0037] The collected images are preprocessed, including denoising and chromatic aberration correction, etc., and multiple surface images are aligned using image processing algorithms to ensure that the boundaries and details of each image can be accurately matched. For example, key feature points are detected in each image, and the transformation matrix between images, such as translation, rotation, and scaling, is calculated. The images are aligned to a unified coordinate system, and the aligned images are spliced ​​in the 3D model to generate a complete surface image of the target workpiece.

[0038] Furthermore, the system further comprises a surface image acquisition module to perform the following operation steps: When the workpiece is at the first contact surface for the first time, the workpiece image acquisition component is started to acquire an image of the target workpiece to obtain a first surface image; according to the first rotation angle, the rotating table is controlled to rotate the target workpiece to acquire a second contact surface; the second contact surface is compared with the first contact surface, and if the comparison fails, the image acquisition and rotation operations are continued; until the workpiece is at the first contact surface again, the image acquisition and rotation operations are stopped, and the multiple surface images are output.

[0039] Specifically, when the workpiece is at the first contact surface position for the first time, the workpiece image acquisition component is started to acquire and record the first surface image at the initial contact surface. According to the generated rotation angle sequence, a first rotation angle is obtained, the first rotation angle is the first one in the rotation angle sequence, that is, the initial rotation angle corresponding to the initial position, and the rotary table is controlled to rotate the target workpiece to a specified angle according to the first rotation angle, so that the workpiece rotates to the second contact surface position.

[0040] Compare the current second contact surface after rotation with the first contact surface, for example, compare the second contact surface image with the first contact surface image. If the match fails, it means that the workpiece has not rotated back to the initial position. According to the rotation angle sequence, control the rotating stage to continue rotating the target workpiece, continue to collect images at each new rotation position, record surface image data, and perform contact surface comparison again after each rotation and image collection until the comparison is successful. When the workpiece is in the first contact surface position again, stop the image collection and rotation operation, and output multiple surface images collected during the whole process.

[0041] Furthermore, the system further includes a workpiece structure feature set acquisition module to perform the following operation steps: A structural feature recognition model is pre-constructed, wherein the structural feature recognition model includes a plurality of structural feature recognition channels, the plurality of structural feature recognition channels are connected in parallel, and the structural feature recognition model is embedded in the workpiece feature recognition component; feature extraction is performed on the image acquisition result to determine a plurality of key surface features for workpiece feature recognition; matching is performed on the plurality of structural feature recognition channels based on the plurality of key surface features, and synchronous structural feature recognition is performed on the plurality of structural feature recognition channels obtained by matching to obtain a workpiece structural feature set.

[0042] Specifically, multiple structural feature recognition channels are constructed, each channel is used to identify a specific type of structural features. These channels can be constructed based on machine learning or deep learning algorithms, such as convolutional neural networks (CNN), support vector machines (SVM), etc. Multiple recognition channels are configured in parallel to ensure that the model can simultaneously process outputs from different feature recognition channels.

[0043] The collected images are preprocessed, including denoising and enhancement, to improve image quality. Image processing algorithms, such as edge detection and corner detection, are used to extract preliminary features from the preprocessed images. The extracted preliminary features are analyzed to determine several key surface features for workpiece feature recognition. These key features are representative structural features, such as holes, grooves, edges, etc.

[0044] The extracted key surface features are matched with the identification features of each structural feature recognition channel, and the most suitable recognition channel is selected. For example, the feature descriptor is used to calculate the similarity, and several structural feature recognition channels with the highest similarity are selected. The image data is processed in parallel by several structural feature recognition channels obtained by matching, and synchronous structural feature recognition is performed. The output results of multiple recognition channels are integrated to generate a set of workpiece structural features.

[0045] Furthermore, the system also includes a structural feature recognition model building module to perform the following operation steps: Taking the first key surface feature as a constraint, sample collection is performed to obtain a first sample image data set; multiple first associated structural features in the first key surface feature are used to perform feature recognition marking on the first sample image data set to obtain a first structural feature marking result set; the first sample image data set and the first structural feature marking result set are used to perform structural feature recognition channel training to obtain a first structural feature recognition channel; based on the first structural feature recognition channel, the structural feature recognition model of the multiple structural feature recognition channels in parallel is constructed.

[0046] Specifically, the first key surface features for sample collection are randomly selected, which are significant structural features on the workpiece, such as specific holes, grooves, edges, etc., and the specific parameters of the first key surface features, such as size, shape, position, etc., are clarified as constraints for sample collection. A workpiece sample containing the first key surface feature is selected, and an image acquisition device is configured to perform comprehensive image acquisition on the workpiece sample to obtain a first sample image data set.

[0047] Based on the first key surface feature, multiple first associated structural features are determined, and these features include geometric details or related structural features around the first key surface feature. Feature recognition and marking tools, such as image processing software, automatic marking algorithms, etc., are configured, and multiple first associated structural features in the first key surface feature are used to perform feature recognition and marking on the first sample image data set to form a first structural feature marking result set.

[0048] The first sample image data set and the corresponding first structural feature labeling result set are divided into a training data set and a test data set, for example, the ratio is 80% training and 20% testing, and the model type is selected, such as convolutional neural network CNN, support vector machine SVM, etc., to train the structural feature recognition channel, use the training data set to train the model, optimize the model parameters through the back propagation algorithm, minimize the loss function, use the cross-validation technology to verify the model, adjust the model parameters, and avoid overfitting and underfitting; use the test data set to evaluate the model performance, calculate the accuracy and other indicators, and further adjust and optimize the model according to the evaluation results to improve its recognition accuracy. Finally, a trained first structural feature recognition channel model is obtained.

[0049] Similar to the training method of the first channel, different feature labeling result sets and sample image data sets are used to train multiple structural feature recognition channels, design a parallel structural feature recognition model architecture, connect multiple independent structural feature recognition channels in parallel, and fuse the recognition results of multiple channels.

[0050] Furthermore, the system further includes an initial feeding scheme set acquisition module to perform the following operation steps: Interact with historical milling machine feed control records to obtain a preset feed plan library; traverse the preset feed plan library according to the material information to obtain a first initial feed plan set, wherein the first initial feed plan set includes multiple cutting speed parameters, multiple feed speed parameters, and multiple tool selection parameters mapped to multiple material features in the material information; traverse the preset feed plan library according to the workpiece structure feature set to obtain a second initial feed plan set, wherein the second initial feed plan set includes multiple cutting path parameters, multiple cutting depth parameters, and multiple feed mode parameters mapped to multiple workpiece structure features; combine the first initial feed plan set and the second initial feed plan set to obtain the initial feed plan set.

[0051] Specifically, historical milling machine feed control records are collected from the system. These records include detailed data of past workpieces, such as material type, cutting speed, feed speed, tool selection, processing results, etc. The collected historical records are sorted and cleaned to remove incomplete or erroneous records to ensure data accuracy. The historical records are sorted and organized according to the workpiece material type, workpiece structural characteristics, etc., and a structured feed plan database is constructed.

[0052] Receive material information of the target workpiece, including detailed characteristics such as material type, hardness, and heat treatment status. Based on the received material information, search for a feed plan that meets the material information in a preset feed plan library to form a first initial feed plan set. The plans in the first initial feed plan set include key parameters such as cutting speed parameters, feed speed parameters, and tool selection parameters.

[0053] According to the set of workpiece structural features, feed schemes that meet the conditions are searched in the preset feed scheme library, such as the best cutting path for different structural features, the recommended cutting depth values ​​for different structural features, and the selection of appropriate feeding methods according to the structural features, such as down milling, reverse milling, and plunge milling. The screened feed schemes that meet the workpiece structural features are summarized to form a second initial feed scheme set. Each scheme in the second initial feed scheme set includes key parameters such as cutting path parameters, cutting depth parameters, and feeding method parameters.

[0054] The first initial feeding scheme set based on material information and the second initial feeding scheme set based on workpiece structural features are integrated to obtain the initial feeding scheme set.

[0055] Furthermore, the system further includes a first initial feeding scheme set acquisition module to perform the following operation steps: Extracting a first material feature according to the material information, extracting a first recording material feature from the preset feeding scheme library; performing a similarity analysis on the first material feature and the first recording material feature to obtain a first similarity; generating a first adding instruction when the first similarity reaches a preset similarity threshold; and adding a first initial feeding scheme corresponding to the first recording material feature to the first initial feeding scheme set according to the first adding instruction.

[0056] Specifically, a key feature is randomly extracted from the material information as the first material feature. A preset feeding scheme library is accessed, a material feature set corresponding to all records is extracted from the scheme library, and a first recorded material feature is randomly selected therefrom.

[0057] Select a similarity calculation algorithm, such as Euclidean distance, cosine similarity, Manhattan distance, etc., calculate the similarity between the first material feature and the first recorded material feature, and obtain a similarity score, i.e., a first similarity. Set a predetermined similarity threshold as a criterion for judging whether the material features are similar, compare the calculated first similarity with the preset similarity threshold, and if the first similarity reaches or exceeds the preset similarity threshold, it is considered that the first material feature is similar to the first recorded material feature, and generate a first adding instruction to instruct to add the feeding scheme corresponding to the first recorded material feature to the first initial feeding scheme set until all similar material records in the preset feeding scheme library are traversed.

[0058] Furthermore, the system further includes a scheme compensation adjustment module to perform the following operation steps: During the feed control process, data monitoring is performed to obtain feed control monitoring data, wherein the feed control monitoring data includes cutting speed monitoring data, feed speed monitoring data, and cutting depth monitoring data; the cutting speed parameters, feed speed parameters, and cutting depth parameters of the target feed scheme are extracted; the cutting speed monitoring data, the feed speed monitoring data, and the cutting depth monitoring data are aligned and fitted with the cutting speed parameters, the feed speed parameters, and the cutting depth parameters, respectively, to obtain multiple fitting coefficients; when any fitting coefficient exceeds a preset fitting coefficient interval, a compensation coefficient is generated, and the target feed scheme is compensated and adjusted according to the compensation coefficient.

[0059] Specifically, according to the target feeding plan, the feed control system is started to process the target workpiece, and sensors are installed at key positions of the milling machine to monitor the cutting speed, feed speed and cutting depth in real time. Specifically, the cutting speed is monitored in real time, and the speed data of the tool at each moment in the cutting process is recorded; the feed speed is monitored in real time, and the speed data of the workpiece at each moment in the feeding process is recorded; the cutting depth is monitored in real time, and the cutting depth data of the tool in the workpiece at each moment is recorded.

[0060] The control parameters are extracted from the target feed scheme, including cutting speed parameters, feed rate parameters, and cutting depth parameters, which represent the ideal machining parameters.

[0061] The real-time monitoring data and the target parameters are aligned on the time axis so that the monitoring data at each moment corresponds to the target parameters in the same time period. The monitoring data and the target parameters are fitted using a linear regression method. Exemplarily, a fitting formula is determined, such as y=ax, where y is the monitoring data, x is the target parameter, and a is the fitting coefficient.

[0062] The cutting speed monitoring data is fitted with the cutting speed parameters to obtain the cutting speed fitting coefficient, and similarly, the feed speed fitting coefficient and the cutting depth fitting coefficient are obtained. The preset interval thresholds of the fitting coefficients are set, and these thresholds represent the allowable deviation range. Each fitting coefficient is detected to see whether it exceeds the preset interval threshold. If any fitting coefficient exceeds the preset interval threshold, a corresponding compensation coefficient is generated according to the fitting coefficient to adjust the parameters of the target feeding scheme to compensate for the deviation in the actual processing. Specifically, according to the generated compensation coefficient, the cutting speed parameters, feed speed parameters and cutting depth parameters in the target feeding scheme are adjusted to form a new feeding scheme, thereby optimizing the feed control.

[0063] In summary, the multi-head milling machine feed control system based on workpiece features provided by the embodiment of the present application has the following technical effects: Through the information interaction module, the system can accurately and interactively obtain material information and processing requirement information when the workpiece enters the first working area, ensuring the rationality and accuracy of the processing parameters; the feature recognition module performs comprehensive image acquisition and feature recognition on the workpiece through the image acquisition component and the feature recognition component, and accurately obtains the structural feature set of the workpiece; the solution matching module performs precise matching from the preset feed solution library according to the material information and the workpiece structural feature set, and generates an initial feed solution set to ensure the adaptability of the feed parameters; the fitting relationship establishment module and the feed parameter optimization module optimize the initial feed solution for the target milling index by establishing a function fitting relationship and parameter optimization, obtain the optimal target feed solution, and improve the processing quality and efficiency; the feed control module performs feed control of the milling component according to the target feed solution to ensure the stability and efficiency of the processing process.

[0064] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-head milling machine feed control system based on workpiece features, characterized in that: The multi-head milling machine feed control system based on workpiece features includes: An information interaction module, wherein the information interaction module is used to interactively acquire material information and processing requirement information of the target workpiece when the target workpiece is transferred to the first working area, wherein the processing requirement information includes a target milling index; A workpiece conveying module, the workpiece conveying module is used to convey the target workpiece to a second working area, wherein the second working area includes a workpiece image acquisition component and a workpiece feature recognition component; A feature recognition module, the feature recognition module is used to perform image acquisition on the target workpiece through the workpiece image acquisition component, perform workpiece feature recognition on the image acquisition result through the workpiece feature recognition component, and obtain a workpiece structural feature set; A scheme matching module, wherein the scheme matching module is used to match a feeding scheme in a preset feeding scheme library according to the material information and the workpiece structure feature set, and obtain an initial feeding scheme set; A fitting relationship establishment module, the fitting relationship establishment module is used to establish a functional fitting relationship between a machining evaluation parameter of a target workpiece and a feed control parameter of a target multi-head milling machine, wherein the machining evaluation parameter corresponds to the target milling index; A feed parameter optimization module, wherein the feed parameter optimization module is used to optimize the feed parameters of the initial feed scheme set according to the function fitting relationship and take the target milling index as the optimization target to obtain a target feed scheme; A feed control module, wherein the feed control module is used to transfer the target workpiece to a third working area, wherein the third working area includes a milling component set and a workpiece fixing component, the target workpiece is fixed according to the workpiece fixing component, and the feed of the milling component set is controlled according to the target feeding scheme.

2. The multi-head milling machine feed control system based on workpiece features as claimed in claim 1, characterized in that: The multi-head milling machine feed control system based on workpiece features also includes an image acquisition result acquisition module to perform the following operation steps: The second working area also includes a rotating table; Acquire geometric data of the target workpiece and a first contact surface, wherein the first contact surface is an initial contact surface between the target workpiece and the rotating stage; generating a rotation angle sequence according to the geometric data, and controlling the rotating table to perform a rotation operation on the target workpiece according to the rotation angle sequence; After each rotation operation, the workpiece image acquisition component acquires an image of the target workpiece until the first contact surface is reached again, and then outputs a plurality of surface images of the target workpiece; Perform image alignment on the multiple surface images to obtain the image acquisition result.

3. The multi-head milling machine feed control system based on workpiece features as claimed in claim 2, characterized in that: The multi-head milling machine feed control system based on workpiece features also includes a surface image acquisition module to perform the following operation steps: When the workpiece is at the first contact surface for the first time, the workpiece image acquisition component is started to acquire an image of the target workpiece to obtain a first surface image; According to the first rotation angle, controlling the rotating table to rotate the target workpiece to obtain a second contact surface; comparing the second contact surface with the first contact surface, and if the comparison fails, continuing the image acquisition and rotation operations; When the first contact surface is reached again, the image acquisition and rotation operations are stopped, and the plurality of surface images are output.

4. The multi-head milling machine feed control system based on workpiece features as claimed in claim 1, characterized in that: The multi-head milling machine feed control system based on workpiece features also includes a workpiece structure feature set acquisition module to perform the following operation steps: Pre-constructing a structural feature recognition model, wherein the structural feature recognition model includes a plurality of structural feature recognition channels, the plurality of structural feature recognition channels are connected in parallel, and the structural feature recognition model is embedded in the workpiece feature recognition component; Extracting features from the image acquisition results to determine a number of key surface features for workpiece feature recognition; The plurality of structural feature recognition channels are matched based on the plurality of key surface features, and synchronous structural feature recognition is performed on the plurality of structural feature recognition channels obtained by matching to obtain a workpiece structural feature set.

5. The multi-head milling machine feed control system based on workpiece features as claimed in claim 2, characterized in that: The multi-head milling machine feed control system based on workpiece features also includes a structural feature recognition model building module to perform the following operation steps: Taking the first key surface feature as a constraint, performing sample collection to obtain a first sample image data set; Using a plurality of first associated structural features in the first key surface features to perform feature recognition marking on the first sample image data set to obtain a first structural feature marking result set; Using the first sample image data set and the first structural feature labeling result set, perform structural feature recognition channel training to obtain a first structural feature recognition channel; Based on the first structural feature recognition channel, the structural feature recognition model in which the multiple structural feature recognition channels are connected in parallel is constructed.

6. The multi-head milling machine feed control system based on workpiece features as claimed in claim 1, characterized in that: The multi-head milling machine feed control system based on workpiece features also includes an initial feed plan set acquisition module to perform the following operation steps: Interactive historical milling machine feed control records to obtain a library of preset feed plans; Traversing the preset feeding scheme library according to the material information to obtain a first initial feeding scheme set, wherein the first initial feeding scheme set includes a plurality of cutting speed parameters, a plurality of feeding speed parameters, and a plurality of tool selection parameters mapped to a plurality of material features in the material information; Traversing the preset feeding scheme library according to the workpiece structural feature set to obtain a second initial feeding scheme set, wherein the second initial feeding scheme set includes a plurality of cutting path parameters, a plurality of cutting depth parameters, and a plurality of feeding mode parameters mapped to a plurality of workpiece structural features; The initial feeding scheme set is obtained by combining the first initial feeding scheme set and the second initial feeding scheme set.

7. The multi-head milling machine feed control system based on workpiece features as claimed in claim 6, characterized in that: The multi-head milling machine feed control system based on workpiece features further includes a first initial feed scheme set acquisition module to perform the following operation steps: extracting a first material feature according to the material information, and extracting a first recording material feature from the preset feeding scheme library; Performing a similarity analysis on the first material feature and the first recording material feature to obtain a first similarity; When the first similarity reaches a preset similarity threshold, generating a first adding instruction; According to the first adding instruction, the first initial feeding scheme corresponding to the first recording material feature is added to the first initial feeding scheme set.

8. The multi-head milling machine feed control system based on workpiece features as claimed in claim 6, characterized in that: The multi-head milling machine feed control system based on workpiece features also includes a scheme compensation adjustment module to perform the following operation steps: Performing data monitoring during the feed control process to obtain feed control monitoring data, wherein the feed control monitoring data includes cutting speed monitoring data, feed speed monitoring data, and cutting depth monitoring data; Extracting cutting speed parameters, feed rate parameters, and cutting depth parameters of the target feed scheme; Align and fit the cutting speed monitoring data, the feed speed monitoring data, and the cutting depth monitoring data with the cutting speed parameter, the feed speed parameter, and the cutting depth parameter, respectively, to obtain a plurality of fitting coefficients; When any fitting coefficient exceeds a preset fitting coefficient interval, a compensation coefficient is generated, and a compensation adjustment is performed on the target feeding scheme according to the compensation coefficient.

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