Fixture monitoring method and system for parts processing

Abstract

The present invention provides a fixture monitoring method and system for part processing, which relates to the field of fixture monitoring technology, including: determining the part clamping method, building a clamping database based on the clamping dynamic characteristics; training a state memory unit, including a parallel state memory branch mapped to the part clamping method; reading fixture monitoring data, pre-processing and performing point cloud distribution data screening; combining the target clamping method, matching the target memory branch, performing dynamic characteristic analysis, and determining a first monitoring result; performing a working condition eccentricity analysis based on spatial position and fixture limit, and determining a second monitoring result; and using the first monitoring result and the second monitoring result as the fixture monitoring result. The present invention solves the technical problems that the existing fixture monitoring method is incomplete in monitoring the processing conditions of workpieces under different clamping methods, and has limitations in data processing, resulting in poor monitoring accuracy and low intelligence.

Description

Technical Field

[0001] The present invention relates to the technical field of fixture monitoring, and in particular to a fixture monitoring method and system for parts processing. Background Art

[0002] In fields involving high-precision machining or high requirements for fixture stability, such as aerospace, automobile manufacturing, electronic equipment and other fields, fixtures are one of the key factors to ensure product machining accuracy and stability. However, traditional fixture monitoring methods still have some technical problems. On the one hand, existing fixture monitoring methods usually only focus on one or a few common clamping methods, which is difficult to meet the diverse clamping needs in the machining process of different workpieces, resulting in incomplete monitoring of the workpiece machining conditions under different clamping methods; on the other hand, existing fixture monitoring methods have limitations in data processing and mechanical analysis, making it difficult to accurately analyze complex workpiece clamping conditions, which makes it difficult to cope with various changes in complex machining processes. Summary of the Invention

[0003] This application provides a fixture monitoring method and system for parts processing, aiming to solve the technical problems that existing fixture monitoring methods usually only consider one or a few clamping methods, resulting in incomplete monitoring of workpiece processing conditions under different clamping methods, and limitations in data processing, resulting in poor monitoring accuracy and low intelligence.

[0004] The first aspect disclosed in the present application provides a fixture monitoring method for part processing, the method comprising: determining a part clamping method, building a clamping database based on clamping dynamic characteristics, wherein the part clamping method includes at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping; based on the clamping database, training a state memory unit, wherein the state memory unit includes a parallel state memory branch mapped to the part clamping method; reading fixture monitoring data of the target part, pre-processing and performing point cloud distribution data screening, wherein the point cloud distribution data is based on the center of gravity of the part , with multi-level preset step lengths and key points as distribution screening criteria; combined with the target clamping method, matching the target memory branch based on the state memory unit, performing dynamic characteristic analysis on the point cloud distribution data, and determining the first monitoring result; reading the part processing task and determining the eccentricity tolerance range, performing working condition eccentricity analysis based on spatial position and fixture limit, and determining the second monitoring result, wherein the working condition eccentricity includes part clamping eccentricity and relative eccentricity based on the tool clamping part and the part clamping part; based on the first monitoring result and the second monitoring result, as the fixture monitoring result of the target part.

[0005] The second aspect disclosed in the present application provides a fixture monitoring system for part processing, the system is used for the above-mentioned fixture monitoring method for part processing, the system includes: a clamping database building module, the clamping database building module is used to determine the part clamping method, and build a clamping database based on clamping dynamic characteristics, wherein the part clamping method includes at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping; a state memory unit training module, the state memory unit training module is used to train the state memory unit based on the clamping database, the state memory unit includes a parallel state memory branch mapped to the part clamping method; a point cloud distribution data screening module, the point cloud distribution data screening module is used to read the fixture monitoring data of the target part, pre-process and perform point cloud distribution data screening, wherein the point The cloud distribution data is based on the center of gravity of the part, and is used as a distribution screening standard with multi-level preset step lengths and key points; a dynamic characteristic analysis module is used to combine the target clamping method, match the target memory branch based on the state memory unit, perform dynamic characteristic analysis on the point cloud distribution data, and determine the first monitoring result; a working condition eccentricity analysis module is used to read the part processing task and determine the eccentricity tolerance range, perform working condition eccentricity analysis based on spatial position and fixture limit, and determine the second monitoring result, wherein the working condition eccentricity includes the part clamping eccentricity and the relative eccentricity based on the tool clamping part and the part clamping part; a fixture monitoring result acquisition module is used to use the first monitoring result and the second monitoring result as the fixture monitoring result of the target part.

[0006] The third aspect disclosed in the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements any step of the first aspect disclosed in the present application when executing the computer program.

[0007] The fourth aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, which implements any step of the first aspect disclosed in the present application when the computer program is executed by a processor.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By integrating various clamping methods such as mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping, the applicability and monitoring accuracy of the monitoring system are improved; by training the state memory unit, which includes parallel state memory branches mapped to different clamping methods, effective identification and matching of different clamping states are achieved, thereby improving the intelligence of the monitoring system; by screening point cloud distribution data and analyzing dynamic characteristics, accurate monitoring and analysis of the workpiece clamping conditions are achieved, providing a reliable basis for subsequent regulation and optimization; by analyzing working condition eccentricity and regulating based on fixture limits, effective monitoring and regulation of part clamping eccentricity and the relative eccentricity between the tool clamping part and the part clamping part are achieved, thereby improving processing accuracy and stability. In summary, the fixture monitoring method for part processing effectively solves the limitations of existing fixture monitoring methods by integrating various clamping methods, applying state memory units, screening point cloud data and dynamic characteristics analysis, as well as working condition eccentricity analysis and fixture regulation, thereby improving the intelligence, accuracy and stability of the fixture monitoring system.

[0010] 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

[0011] Figure 1 A schematic flow chart of a fixture monitoring method for parts processing provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a fixture monitoring system for parts processing provided in an embodiment of the present application;

[0013] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0014] Explanation of the accompanying symbols: clamping database building module 10, state memory unit training module 20, point cloud distribution data screening module 30, dynamic characteristics analysis module 40, working condition eccentricity analysis module 50, fixture monitoring result acquisition module 60. DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a fixture monitoring method for parts processing, which solves the technical problems that existing fixture monitoring methods usually only consider one or a few clamping methods, resulting in incomplete monitoring of the workpiece processing conditions under different clamping methods, and limitations in data processing, resulting in poor monitoring accuracy and low intelligence.

[0016] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0017] like Figure 1 As shown, an embodiment of the present application provides a fixture monitoring method for part processing, the method comprising:

[0018] Determine the part clamping method and build a clamping database based on the clamping dynamic characteristics, wherein the part clamping method includes at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping.

[0019] Determine the part clamping method. The part clamping methods include at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping. Among them, mechanical clamping is to use mechanical clamps, such as vises, chucks, etc., to clamp parts through mechanical force. This method is often used in processing occasions that require high precision and high stability; hydraulic clamping is to use the pressure generated by the hydraulic system to clamp parts through hydraulic clamps. This method is suitable for occasions that require large clamping force and fast operation; electromagnetic clamping is to use the magnetic force generated by electromagnets to clamp metal parts. This method is suitable for fast clamping and disassembly, and is not easy to damage the surface of the parts; vacuum adsorption clamping is to use negative pressure through vacuum suction cups to adsorb and clamp parts. It is usually used for thin plates or parts with smooth surfaces.

[0020] Dynamic characteristics describe the behavioral features of an object under force and motion, including force, vibration, etc. Through historical data, experiments or simulations, data such as clamping force, clamping stiffness, vibration characteristics, etc. under different clamping methods are collected. The collected data are classified according to the clamping method and stored in the database. A clamping database is established to provide data support for fixture monitoring in part processing.

[0021] Furthermore, the building of a clamping database based on clamping dynamic characteristics includes:

[0022] Read historical clamping data to mine transmission characteristics and transmission relationships, wherein the transmission relationship includes a power source transmission relationship and a power attenuation relationship; mine transmission constraint factors for the transmission characteristics and determine the transmission constraint relationship; integrate the transmission characteristics, the transmission relationship, and the transmission constraint relationship to build the clamping database.

[0023] Extract historical clamping data from the recording system. These data include the clamping force, response time, friction loss, etc. of different types of clamps under various working conditions. Explore the transmission characteristics. Transmission characteristics are characteristics that describe the force transmission method and efficiency in the clamping system. Specifically, analyze the various connection points in the clamping system to determine their role in the clamping process. For example, the transmission efficiency of the clamping force, that is, the force transmission efficiency from the power source, such as a hydraulic pump, to the various parts of the clamp; the clamping force response, that is, the response speed and stability of the clamp after the clamping force is applied; the characteristics of each connection point, that is, analyze the mechanical characteristics of each connection point in the clamping system, such as hinges and sliders, including stiffness, friction coefficient, etc.

[0024] Excavate the transmission relationship. The transmission relationship includes the power source transmission relationship and the power attenuation relationship. The power source transmission relationship includes analyzing the force transmission path and efficiency from the power source, such as a hydraulic pump, motor, etc., to each part of the clamp. For example, in the force transmission path from the hydraulic pump to the clamping claw, the transmission efficiency of each component; the power attenuation relationship includes analyzing the force attenuation caused by factors such as friction and deformation during the force transmission process. For example, the friction loss under different working conditions and its impact on the clamping force.

[0025] Identify transmission constraints, including mechanical constraints, environmental constraints, material property factors, power source property factors, etc., conduct data analysis on transmission constraints, and use historical data and experimental data to verify the impact of these factors on transmission characteristics. That is, measure the performance of the transmission system under different conditions through experiments, for example, by changing the temperature to measure the change in friction force, and verify the impact of ambient temperature on clamping force. Based on data analysis and experimental results, establish a transmission constraint relationship, which can describe the performance changes of the transmission system under different constraint conditions.

[0026] By integrating transmission characteristics, transmission relationships, and transmission constraint relationships, a comprehensive clamping database is built. This database can be used to optimize fixture design, improve the control accuracy of the clamping process, and provide a reference for practical applications. For example, based on data on transmission characteristics and constraint relationships, the performance of the fixture under different working conditions can be predicted to guide actual operations.

[0027] Based on the clamping database, a state memory unit is trained, wherein the state memory unit includes parallel state memory branches mapped to the part clamping mode.

[0028] The state memory unit is a model that can memorize and identify the state of the fixture. It can be understood as a neural network model with memory function. The state memory unit includes parallel state memory branches mapped to the part clamping method. The parallel state memory branches are independent state memory models constructed for each clamping method. These models work in parallel and process the data of the corresponding clamping methods respectively.

[0029] The dynamic characteristic data under different clamping methods is extracted from the clamping database, and the data is subjected to feature extraction to form a feature vector suitable for training the model. The processed data is used to train the various parallel state memory branches. During the training process, supervised learning methods can be used to guide model learning through labeled data, such as normal and abnormal states. The trained model is verified using a validation dataset to evaluate its performance, including indicators such as accuracy. Based on the verification results, the model parameters are adjusted to optimize model performance. Through the above steps, an efficient state memory unit is trained, which can monitor and analyze the fixture status in real time under different clamping methods, providing reliable support for the part processing process.

[0030] The fixture monitoring data of the target part is read, pre-processed and the point cloud distribution data is screened, wherein the point cloud distribution data is based on the center of gravity of the part and the multi-level preset step length and key points are used as distribution screening criteria.

[0031] Real-time monitoring data of the target part in the fixture is acquired from sensors and monitoring equipment. This data includes information such as clamping force, position, and vibration, and is then used to obtain fixture monitoring data. Preprocessing of fixture monitoring data involves data cleaning (removing noise, outliers, and invalid data to ensure data quality) and data standardization to keep the data within the same dimensional range for subsequent analysis.

[0032] Perform point cloud distribution data screening. Specifically, calculate the center of gravity position of the part as the reference point of the point cloud distribution, recorded as the first point cloud, determine the key locations on the part, for example, special structural positions, such as inflection points, recorded as the second point cloud distribution, and randomly determine the first step size based on the multi-level preset step size. Use the first point cloud and the second point cloud distribution as the initial points to expand the point cloud, use the preset step size for iterative expansion, and gradually expand the range of the point cloud distribution until the coverage requirements are met. In each expansion iteration, filter out the point cloud data that meets the preset standards, and integrate the point cloud data after multi-level step size expansion and screening to form the final point cloud distribution data.

[0033] Furthermore, the point cloud distribution data screening includes:

[0034] Determine the center of gravity of the part as the first point cloud; determine the position of the key site for clamping the part as the second point cloud distribution; based on the multi-level preset step size, randomly determine the first step size level, take the first point cloud and the second point cloud distribution as the initial points, expand the point cloud based on the first step size level, iterate the point cloud expansion until the coverage requirements are met, and integrate the point cloud distribution to determine the monitoring point cloud distribution; based on the monitoring point cloud distribution, perform pre-processed screening of the fixture monitoring data.

[0035] Determine the center of gravity of the part. For example, obtain a three-dimensional model of the target part through a CAD model, perform mass distribution analysis on the three-dimensional model, calculate the mass of each part, calculate the center of gravity of the part based on the mass distribution, and use the coordinates of the center of gravity of the part as the first point cloud.

[0036] Determine the key locations for part clamping. Specifically, analyze the structure of the part, determine the possible clamping positions and force transmission paths, and identify the key locations on the part. These locations are special structural locations, including inflection points, mutation points, hole edges, etc. Mark the identified key locations on the 3D model and record their coordinates as the second point cloud distribution.

[0037] Define different levels of step sizes to control the expansion range of point cloud data. For example, you can preset three levels of step sizes: small, medium, and large, corresponding to different expansion ranges. This allows you to determine multiple levels of preset step sizes. Randomly select one of the preset step size levels as the initial step size level, i.e., the first step size level.

[0038] Using the center of gravity (first point cloud) and key locations (second point cloud distribution) of the part as initial points, point cloud expansion is performed based on the selected step size level. Specifically, the first point cloud and the second point cloud distribution are used as the initial point cloud set. For each point in the initial point cloud set, expansion is performed according to the current step size level to generate a new point cloud. The newly generated point cloud is added to the point cloud set, and the expansion process is repeated until the point cloud coverage meets the requirements. The coverage requirement is a preset coverage standard, for example, covering more than 95% of the part surface area. After each iterative expansion, the coverage of the current point cloud set is calculated. If the coverage does not meet the requirements, the expansion continues; otherwise, the expansion stops. After the coverage requirements are met, the point cloud data is integrated to determine the monitoring point cloud distribution.

[0039] For the pre-processed fixture monitoring data, only the monitoring data corresponding to the monitoring point cloud distribution is retained, and other data are ignored.

[0040] In combination with the target clamping mode, the target memory branch based on the state memory unit is matched, the dynamic characteristics of the point cloud distribution data are analyzed, and the first monitoring result is determined.

[0041] Based on the target clamping method of the target part, a matching memory branch is selected as the target memory branch to ensure the targeted analysis. The pre-processed and filtered point cloud distribution data is input into the selected target memory branch. The target memory branch is used to perform dynamic characteristic analysis on the point cloud data to predict and evaluate the dynamic response of the part during the clamping process. The analysis process includes vibration analysis, which identifies the vibration frequency and resonance points that may be generated by the part during the processing and evaluates their impact on the processing quality; stress and strain analysis, which evaluates the stress and strain of the part during the clamping and processing process to determine whether it is within a safe range; and clamping force distribution analysis, which analyzes the distribution of the clamping force on the part to ensure uniform and stable clamping.

[0042] Based on the results of the dynamic characteristics analysis, the first monitoring result is determined. This result includes an assessment of the part clamping status and the identification of potential problems. For example, if the analysis results show that the part is in a normal state under the current hydraulic clamping and processing conditions and all dynamic characteristic indicators are within the safe range, then the first monitoring result is normal; if the analysis results show that the dynamic characteristics exceed the safe range, then the first monitoring result is abnormal and corresponding measures need to be taken.

[0043] Furthermore, after performing dynamic characteristic analysis on the point cloud distribution data, the following steps are included:

[0044] Determine N point cloud monitoring results based on the point cloud distribution data; perform a single point cloud power transmission analysis based on the clamping power connection point on the N point cloud monitoring results to determine the single point monitoring characteristics; perform a point cloud balance analysis on the N point cloud monitoring results to determine the global monitoring characteristics; determine the first monitoring result based on the single point monitoring characteristics and the global monitoring characteristics.

[0045] From the monitoring point cloud distribution, select N key monitoring points, where N is determined based on the actual key location. These monitoring points include the center of gravity, clamping points, inflection points, and other locations of the part. Read the monitoring data corresponding to each monitoring point and organize the data for each monitoring point into a monitoring result, forming N point cloud monitoring results. Each monitoring result includes multiple monitoring parameters, such as vibration frequency and clamping force.

[0046] Identify the clamping power connection point, which is the intermediate structural point between the power source (e.g., hydraulic or pneumatic) and the fixture. Perform a power transmission analysis for each monitoring point. For example, establish a power transmission model to simulate the process of power transmission from the power source through the intermediate structural point to the fixture and then to the workpiece. During the simulation, analyze the degree of power attenuation during the transmission process. For example, calculate the power attenuation value for each monitoring point based on the power transmission model. Based on the results of the power transmission analysis, determine the single-point monitoring characteristics of each monitoring point. These characteristics include power transmission efficiency, clamping force response, and power attenuation.

[0047] Point cloud balance analysis involves checking the spatial distribution balance and numerical balance of the monitoring point cloud data to ensure that the monitoring data is evenly distributed across the entire part and that the data at each monitoring point has no significant deviations. Specifically, the spatial distribution of each monitoring point on the part is analyzed to ensure that the monitoring points cover the critical areas of the entire part. For example, the distance between each monitoring point is calculated to check for overly dense or sparse point cloud distribution. The data values ​​at each monitoring point are analyzed to check for significant anomalies or deviations. For example, statistical methods such as mean, variance, and standard deviation are used to measure the data balance at each monitoring point. The global monitoring characteristics of the part are determined by combining the analysis results of spatial distribution balance and numerical balance.

[0048] The single-point monitoring characteristics are integrated with the global monitoring characteristics to form a comprehensive first monitoring result.

[0049] Read the part processing task and determine the eccentricity tolerance range, perform working condition eccentricity analysis based on spatial position and fixture limit, and determine the second monitoring result, where the working condition eccentricity includes the part clamping eccentricity and the relative eccentricity between the tool clamping part and the part clamping part.

[0050] The current part's machining task information, including machining path, machining speed, required tools, etc., is read from the machining task system. Based on the machining task and the part's geometric characteristics, the allowable eccentricity range, or eccentricity tolerance interval, is determined. This interval defines the degree of eccentricity that can be tolerated during machining to ensure machining accuracy and quality. The eccentricity tolerance interval includes the orientation tolerance interval, which is the allowable eccentricity angle in each direction (e.g., set to ±2 degrees), and the distance tolerance interval, which is the allowable eccentricity distance in each direction (e.g., set to ±0.5 mm).

[0051] Perform working condition eccentricity analysis, specifically, perform part clamping eccentricity analysis to analyze whether there is eccentricity in the clamping position of the part in the fixture, that is, whether the center of gravity of the part is consistent with the clamping center. If there is eccentricity, calculate the eccentricity distance and eccentricity direction; perform relative eccentricity analysis based on the tool clamping part and the part clamping part, analyze the relative position relationship between the processing tool and the part, and determine the relative eccentricity between the tool clamping part and the part clamping part. Relative eccentricity may cause processing errors and clamping instability.

[0052] The second monitoring result is determined by comprehensively analyzing the eccentricity of the part clamping and the relative eccentricity between the tool and the part. For example, if the eccentricity is within the allowable tolerance range and does not affect the processing quality, the second monitoring result is normal. If the eccentricity exceeds the eccentricity tolerance range, it may cause processing errors or unstable clamping and requires immediate adjustment, then the second monitoring result is abnormal.

[0053] Furthermore, the working condition eccentricity includes part clamping eccentricity, including:

[0054] The geometric structure of the target part is read to determine the center of the arc; based on the center of the arc, the target part is subjected to arc center eccentricity calibration to determine the first eccentricity spatial distance, wherein the eccentricity calibration is performed based on the relative spatial distance between the center of the processed arc and the actual arc center; based on the relative clamping position of the clamp and the target part, a clamp limit deviation analysis is performed to determine the second deviation limit area; based on the first eccentricity spatial distance and the second deviation limit area, the clamping eccentricity result is determined and added to the second monitoring result.

[0055] Read the geometric structure information of the target part from the CAD file or other geometric data source, identify the arc structure of the target part, including the starting point, end point and radius of the arc, calculate the center coordinates of the arc based on the starting point and end point of the arc, usually the center coordinates of the circle, and determine the center of the arc.

[0056] Use measurement tools or analysis software to detect the target part's actual arc center. This actual arc center may deviate from the ideal center due to machining errors or design issues. Using the arc center as the reference point, calculate the relative spatial distance between the machined arc center and the actual arc center. This distance is the first eccentricity spatial distance, which can be expressed as Euclidean distance.

[0057] Analyze the relative clamping position relationship between the fixture and the target part, including the fixture's fixed points and the target part's key support points. Based on the relative clamping positions, analyze the potential deviation of the fixture under limit conditions. For example, combining engineering knowledge with practical analysis, considering the fixture's structural characteristics, working environment, and operating conditions, use computer-aided engineering software to perform fixture deviation analysis. Determine the possible deviation range of the fixture under different working conditions. Based on the deviation analysis results, determine the second deviation limit area of ​​the fixture under limit conditions.

[0058] Combined with the first eccentric space distance and the second deviation limit area, the possible eccentricity of the fixture in actual work is calculated. Computer-aided engineering software is also used to determine the clamping eccentricity result. This result is a quantitative value that represents the degree of clamping eccentricity. The clamping eccentricity result is integrated into the second monitoring result.

[0059] Furthermore, the working condition eccentricity includes the relative eccentricity between the tool holder and the part holder, including:

[0060] The part processing task is read to determine the relative clamping position of the entire processing cycle, wherein the relative clamping position is marked with a processing node; the relative tolerance interval of the eccentricity tolerance interval is identified, wherein the relative tolerance interval includes an orientation tolerance interval and a distance tolerance interval; based on the relative tolerance interval, the relative off-axis calibration is performed on the relative clamping position to determine the relative eccentricity result.

[0061] The processing task information of the current part is read from the processing task system. According to the processing nodes and fixture design, the relative clamping position of the fixture and the part at each processing node is calculated, and the relative clamping position of each processing node is marked for subsequent eccentricity tolerance interval identification.

[0062] The eccentricity tolerance range refers to the allowable range of relative eccentricity between the fixture and the part during the machining process. It includes the orientation tolerance range and the distance tolerance range. The orientation tolerance range determines the allowable deviation range of the fixture in orientation based on the fixture design and machining task. The orientation tolerance range can be an angular range or a direction identifier, indicating the allowable rotational deviation of the fixture during machining. The distance tolerance range determines the allowable deviation range of the fixture in distance based on the fixture design and machining task. The distance tolerance range can be a linear distance or a gap identifier, indicating the allowable linear deviation or gap of the fixture during machining. The relative clamping position of the fixture and the part at each machining node is identified and marked with a tolerance range. Based on the orientation tolerance range and the distance tolerance range, the relative tolerance range at each machining node is determined.

[0063] Relative off-axis calibration is a method used to determine the relative eccentricity result. The basic idea is to compare the relative clamping position with the relative tolerance range to determine the eccentricity between the fixture and the part. Specifically, from the relative clamping position of the entire machining cycle, the specific machining nodes that need to be calibrated relative off-axis are selected, and based on the relative tolerance range, the corresponding tolerance range is determined. The relative clamping position is compared and analyzed with the relative tolerance range to determine the relative eccentricity result. If the relative clamping position is within the relative tolerance range, it is determined to meet the tolerance requirements and the relative eccentricity result is normal; if the relative clamping position exceeds the relative tolerance range, it is determined to not meet the tolerance requirements and requires further processing.

[0064] The first monitoring result and the second monitoring result are used as the fixture monitoring result of the target part.

[0065] Integrate the first and second monitoring results and conduct a comprehensive analysis, taking into account the mutual influence of various indicators. For example, although the clamping force is evenly distributed, if the eccentricity is large, it may still lead to stress concentration during processing, which requires further verification. In this way, a complete target part fixture monitoring result is formed to provide comprehensive guidance for the part processing process.

[0066] Furthermore, after determining the fixture monitoring result of the target part, the following steps are included:

[0067] Based on the fixture monitoring results, the clamping control factors are determined; based on the clamping control factors, multi-factor control optimization analysis is performed with factor control collision as a constraint to determine a composite control scheme; based on the composite control scheme, the fixture clamping feedback control is performed based on the target part.

[0068] Analyze the fixture monitoring results, including monitoring data such as clamping eccentricity, fixture limit deviation, and clamping force. Based on the monitoring results, determine the key control factors that affect the clamping performance of the fixture, such as clamping force adjustment, fixture structure adjustment, fixture position adjustment, etc., prioritize the determined clamping control factors, and determine the priority based on the degree of their impact on the clamping performance and the difficulty of control.

[0069] The clamping control factors are used as control parameters to avoid fixture collision and conflict problems during the processing. Multi-factor control optimization algorithms, such as genetic algorithms and simulated annealing algorithms, are used to combine and optimize the fixture control factors. The mutual influence and trade-offs between different factors are considered to seek the optimal clamping control scheme. According to the results of the optimization analysis, a composite control scheme is determined, including a combination control scheme of multiple factors such as clamping force adjustment, fixture structure adjustment, and fixture position adjustment.

[0070] The determined composite control scheme is applied to the actual fixture clamping operation, and the fixture clamping feedback control is performed according to the specific processing process and process requirements of the target parts. That is, the effect of the control scheme after implementation is evaluated, including changes in indicators such as clamping stability, processing accuracy, and efficiency. According to the feedback results and actual conditions, the composite control scheme is adjusted in time to ensure the stability and optimization of the fixture clamping performance, thereby improving processing efficiency and product quality.

[0071] In summary, the fixture monitoring method for parts processing provided by the embodiments of the present application has the following technical effects:

[0072] 1. By integrating multiple clamping methods such as mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping, the application scope and monitoring accuracy of the monitoring system are improved;

[0073] 2. By training the state memory unit, which includes parallel state memory branches mapped to different clamping modes, effective recognition and matching of different clamping states is achieved, improving the intelligence of the monitoring system;

[0074] 3. Through point cloud distribution data screening and dynamic characteristics analysis, accurate monitoring and analysis of workpiece clamping conditions are achieved, providing a reliable basis for subsequent regulation and optimization;

[0075] 4. Through working condition eccentricity analysis and control based on fixture limit, effective monitoring and control of part clamping eccentricity and relative eccentricity between tool clamping part and part clamping part are achieved, thereby improving processing accuracy and stability.

[0076] In summary, this fixture monitoring method for parts processing effectively solves the limitations of existing fixture monitoring methods by integrating multiple clamping methods, applying state memory units, point cloud data screening and dynamic characteristics analysis, as well as working condition eccentricity analysis and fixture control, and improves the intelligence, accuracy and stability of the fixture monitoring system.

[0077] Based on the same inventive concept as the fixture monitoring method for parts processing in the above embodiment, Figure 2 As shown, an embodiment of the present application provides a fixture monitoring system for part processing, the system comprising:

[0078] A clamping database building module 10 is used to determine a part clamping method and build a clamping database based on clamping dynamic characteristics, wherein the part clamping method includes at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping;

[0079] A state memory unit training module 20, wherein the state memory unit training module 20 is used to train a state memory unit based on the clamping database, wherein the state memory unit includes parallel state memory branches mapped to the part clamping mode;

[0080] A point cloud distribution data screening module 30 is used to read fixture monitoring data of a target part, pre-process and screen the point cloud distribution data, wherein the point cloud distribution data is based on the center of gravity of the part and uses multi-level preset fill steps and key points as distribution screening criteria;

[0081] a dynamic characteristic analysis module 40 for performing a dynamic characteristic analysis on the point cloud distribution data in combination with a target clamping mode and matching the target memory branch based on the state memory unit to determine a first monitoring result;

[0082] A working condition eccentricity analysis module 50 is configured to read a part processing task and determine an eccentricity tolerance range, perform working condition eccentricity analysis based on spatial position and fixture limits, and determine a second monitoring result, wherein the working condition eccentricity includes part clamping eccentricity and relative eccentricity between the tool clamping portion and the part clamping portion;

[0083] The fixture monitoring result acquisition module 60 is used to obtain the fixture monitoring result of the target part based on the first monitoring result and the second monitoring result.

[0084] Furthermore, the system further includes a clamping database building module to perform the following operation steps:

[0085] Read historical clamping data to explore transmission characteristics and transmission relationships, including power source transmission relationship and power attenuation relationship;

[0086] Mining transmission constraint factors for the transmission characteristics and determining transmission constraint relationships;

[0087] The transmission characteristics, the transmission relationship and the transmission constraint relationship are integrated to build the clamping database.

[0088] Furthermore, the system also includes a fixture monitoring data screening module to perform the following steps:

[0089] Determine the center of gravity of the part as the first point cloud;

[0090] Determine the key locations of part clamping as the second point cloud distribution;

[0091] Based on the multi-level preset step size, a first step size level is randomly determined, and the first point cloud and the second point cloud distribution are used as initial points. The point cloud is expanded based on the first step size level, and the point cloud expansion is iterated until the coverage requirement is met, and the point cloud distribution is integrated to determine the monitoring point cloud distribution;

[0092] The pre-processed fixture monitoring data is screened based on the monitoring point cloud distribution.

[0093] Furthermore, the system further includes a first monitoring result determination module to perform the following operation steps:

[0094] Determining N point cloud monitoring results based on the point cloud distribution data;

[0095] Performing a single point cloud power transmission analysis based on the clamping power connection point on the N point cloud monitoring results to determine the single point monitoring characteristics;

[0096] Performing point cloud balance analysis on the N point cloud monitoring results to determine global monitoring characteristics;

[0097] The first monitoring result is determined based on the single-point monitoring characteristic and the global monitoring characteristic.

[0098] Furthermore, the system further includes a second monitoring result acquisition module to perform the following operation steps:

[0099] Reading the geometric structure of the target part and determining the center of the arc;

[0100] Taking the arc center as a reference, performing arc center eccentricity verification on the target part to determine a first eccentricity spatial distance, wherein the eccentricity verification is performed based on the relative spatial distance between the machining arc center and the actual arc center;

[0101] Based on the relative clamping position between the fixture and the target part, performing a fixture limit deviation analysis to determine a second deviation limit area;

[0102] Based on the first eccentric space distance and the second deviation limit area, a clamping eccentricity result is determined and added to the second monitoring result.

[0103] Furthermore, the system further includes a relative eccentricity result determination module to perform the following operation steps:

[0104] Reading the part processing task, determining the relative clamping position of the entire processing cycle, wherein the relative clamping position is marked with a processing node;

[0105] Identifying a relative tolerance interval of the eccentricity tolerance interval, wherein the relative tolerance interval includes an azimuth tolerance interval and a distance tolerance interval;

[0106] Based on the relative tolerance range, a relative off-axis calibration is performed on the relative clamping position to determine a relative eccentricity result.

[0107] Furthermore, the system also includes a clamping feedback control module to perform the following steps:

[0108] Determining clamping control factors based on the clamp monitoring results;

[0109] Based on the clamping control elements, taking element control collision as a constraint, a multi-element control optimization analysis is performed to determine a composite control scheme;

[0110] Based on the composite control scheme, clamping feedback control of the target part is performed.

[0111] Through the above detailed description of the fixture monitoring method for parts processing in this specification, those skilled in the art can clearly understand the fixture monitoring system for parts processing in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant matters, please refer to the method part description.

[0112] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. The computer program is executed by the processor to implement a fixture monitoring method for part processing.

[0113] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0114] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one 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 is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fixture monitoring method for parts processing, characterized in that: The method comprises: Determine a part clamping method and build a clamping database based on clamping dynamic characteristics, wherein the part clamping method includes at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping; Based on the clamping database, a state memory unit is trained, wherein the state memory unit includes parallel state memory branches mapped to the part clamping modes, and the parallel state memory branches are independent state memory models constructed for each clamping mode; Read the fixture monitoring data of the target part, pre-process it and perform point cloud distribution data screening, wherein the point cloud distribution data is based on the center of gravity of the part and uses multi-level preset fill steps and key points as distribution screening criteria; The point cloud distribution data screening includes: Determine the center of gravity of the part as the first point cloud; Determine the key locations of part clamping as the second point cloud distribution; Based on the multi-level preset step size, a first step size level is randomly determined, and the first point cloud and the second point cloud distribution are used as initial points. The point cloud is expanded based on the first step size level, and the point cloud expansion is iterated until the coverage requirement is met, and the point cloud distribution is integrated to determine the monitoring point cloud distribution; Based on the monitoring point cloud distribution, screening the pre-processed fixture monitoring data; In combination with the target clamping mode, the target memory branch based on the state memory unit is matched, the point cloud distribution data is subjected to dynamic characteristic analysis, and a first monitoring result is determined; After performing dynamic characteristic analysis on the point cloud distribution data, the following steps are included: Determining N point cloud monitoring results based on the point cloud distribution data; Performing a single point cloud power transmission analysis based on the clamping power connection point on the N point cloud monitoring results to determine the single point monitoring characteristics; Performing point cloud balance analysis on the N point cloud monitoring results to determine global monitoring characteristics; Determining the first monitoring result based on the single-point monitoring characteristic and the global monitoring characteristic; Read the part processing task and determine the eccentricity tolerance range, perform working condition eccentricity analysis based on spatial position and fixture limit, and determine the second monitoring result, where the working condition eccentricity includes the part clamping eccentricity and the relative eccentricity between the tool clamping part and the part clamping part; The relative eccentricity between the tool holding portion and the part holding portion includes: Reading the part processing task, determining the relative clamping position of the entire processing cycle, wherein the relative clamping position is marked with a processing node; Identifying a relative tolerance interval of the eccentricity tolerance interval, wherein the relative tolerance interval includes an azimuth tolerance interval and a distance tolerance interval; Based on the relative tolerance range, performing relative off-axis verification on the relative clamping position to determine a relative eccentricity result; The first monitoring result and the second monitoring result are used as the fixture monitoring result of the target part.

2. The method according to claim 1, wherein The construction of a clamping database based on clamping dynamic characteristics includes: Read historical clamping data to explore transmission characteristics and transmission relationships, including power source transmission relationship and power attenuation relationship; Mining transmission constraint factors for the transmission characteristics and determining transmission constraint relationships; The transmission characteristics, the transmission relationship and the transmission constraint relationship are integrated to build the clamping database.

3. The method according to claim 1, wherein The working condition eccentricity includes part clamping eccentricity, including: Reading the geometric structure of the target part and determining the center of the arc; Taking the arc center as a reference, performing arc center eccentricity verification on the target part to determine a first eccentricity spatial distance, wherein the eccentricity verification is performed based on the relative spatial distance between the machining arc center and the actual arc center; Based on the relative clamping position between the fixture and the target part, performing a fixture limit deviation analysis to determine a second deviation limit area; Based on the first eccentric space distance and the second deviation limit area, a clamping eccentricity result is determined and added to the second monitoring result.

4. The method according to claim 1, wherein After determining the fixture monitoring result of the target part, it includes: Determining clamping control factors based on the clamp monitoring results; Based on the clamping control elements, taking element control collision as a constraint, a multi-element control optimization analysis is performed to determine a composite control scheme; Based on the composite control scheme, clamping feedback control of the fixture based on the target part is performed.

5. A fixture monitoring system for parts processing, characterized in that: A system for implementing the fixture monitoring method for parts processing according to any one of claims 1 to 4, comprising: A clamping database building module, wherein the clamping database building module is used to determine a part clamping method and build a clamping database based on clamping dynamic characteristics, wherein the part clamping method includes at least mechanical clamping, hydraulic clamping, electromagnetic clamping, and vacuum adsorption clamping; A state memory unit training module, the state memory unit training module is used to train a state memory unit based on the clamping database, the state memory unit including parallel state memory branches mapped to the part clamping mode; A point cloud distribution data screening module is used to read fixture monitoring data of a target part, pre-process and screen the point cloud distribution data, wherein the point cloud distribution data is based on the center of gravity of the part and uses multi-level preset fill steps and key points as distribution screening criteria; a dynamic characteristic analysis module, the dynamic characteristic analysis module being configured to combine a target clamping mode with a target memory branch based on the state memory unit, perform a dynamic characteristic analysis on the point cloud distribution data, and determine a first monitoring result; a working condition eccentricity analysis module, which is used to read the part processing task and determine the eccentricity tolerance range, perform working condition eccentricity analysis based on spatial position and fixture limit, and determine the second monitoring result, wherein the working condition eccentricity includes the part clamping eccentricity and the relative eccentricity between the tool clamping portion and the part clamping portion; A fixture monitoring result acquisition module is used to obtain the fixture monitoring result of the target part based on the first monitoring result and the second monitoring result.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the fixture monitoring method for part processing according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fixture monitoring method for part processing according to any one of claims 1 to 4 are implemented.

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