Machining precision improving method and device based on multi-direction elastic force side pushing jig

Abstract

The invention discloses a machining precision improving method and device based on a multi-direction elastic force side pushing jig, and relates to the technical field of machining precision control. The method comprises the steps that workpiece surface contour information is scanned, and contact supporting coordinates of a plurality of elastic force side pushing assemblies are determined; performing initial processing positioning on the workpiece according to the coordinates; collecting processing data; stress simulation is conducted on the workpiece according to the machining data, and stress simulation data are obtained; inputting the stress simulation data into an elastic force flexible adjustment model for analysis, and obtaining a plurality of flexible driving parameters; and according to the flexible driving parameters, based on the initial machining positioning, flexible driving clamping is conducted. The technical problem that a traditional rigid jig is difficult to dynamically adapt to a complex stress environment due to positioning deviation caused by material deformation and load fluctuation in the machining process is solved, the positioning stability and deformation compensation capacity of a workpiece in dynamic machining are improved through real-time force impedance analysis and flexible clamping parameter adjustment, and the machining precision of the workpiece is improved. And the technical effect of high-precision self-adaptive machining is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of machining accuracy control, and specifically to a method and device for improving machining accuracy based on a multi-directional elastic side-thrust fixture. Background Art

[0002] In the field of precision machining, the performance of the workpiece clamping system directly affects the machining accuracy and surface quality. Traditional rigid fixtures lack dynamic adjustment capabilities, which can easily cause workpiece positioning deviation, vibration or local deformation when machining complex surfaces or bearing variable loads. Such problems are particularly prominent in high-precision welding, cutting or additive manufacturing processes. In the prior art, fixed fixtures are difficult to adapt to slight differences in the geometry of the workpiece, and the passive elastic support structure cannot make real-time adjustments to the dynamic force changes during the machining process, resulting in accumulated machining errors. In addition, conventional clamping schemes usually rely on empirical layout design, lack quantitative analysis methods based on the actual contour of the workpiece and the machining load, and it is difficult to achieve the optimal clamping force distribution. Summary of the invention

[0003] The present application solves the technical problem that traditional rigid fixtures are difficult to dynamically adapt to complex stress environments due to positioning deviation caused by material deformation and load fluctuation during processing by providing a method and device for improving processing accuracy based on a multi-directional elastic side-thrust fixture.

[0004] The present application provides a method for improving machining accuracy based on a multi-directional elastic side thrust fixture, wherein the multi-directional elastic side thrust fixture comprises a plurality of elastic side thrust components, each of which comprises a flexible driver, a cushion layer and an embedded sensor sheet, and the method comprises: scanning surface contour information of a workpiece, and determining contact support coordinates of the plurality of elastic side thrust components according to the surface contour information; performing initial machining positioning of the workpiece according to the contact support coordinates; collecting machining data of the workpiece, wherein the machining data comprises machining equipment information and machining load information; performing force simulation prediction on the workpiece according to the machining data, obtaining force simulation data, inputting the force simulation data into an elastic flexibility adjustment model for force impedance analysis, and obtaining a plurality of flexible drive parameters corresponding to the plurality of elastic side thrust components; and performing flexible drive clamping based on the initial machining positioning according to the plurality of flexible drive parameters.

[0005] The present application also provides a device for improving the processing accuracy based on a multi-directional elastic side-pushing fixture. The multi-directional elastic side-pushing fixture includes a plurality of elastic side-pushing components, and each elastic side-pushing component includes a flexible driver, a cushion layer, and an embedded sensing sheet. It includes: a coordinate determination module: scanning the surface contour information of the workpiece and determining the contact support coordinates of the plurality of elastic side-pushing components according to the surface contour information; a processing positioning module: performing initial processing positioning on the workpiece according to the contact support coordinates; a data acquisition module: acquiring the processing data of processing the workpiece, where the processing data includes processing equipment information and processing load information; a force impedance analysis module: performing a force simulation prediction on the workpiece according to the processing data, obtaining force simulation data, inputting the force simulation data into an elastic flexible adjustment model for force impedance analysis, and obtaining a plurality of flexible drive parameters corresponding to the plurality of elastic side-pushing components; a flexible drive clamping module: performing flexible drive clamping based on the initial processing positioning according to the plurality of flexible drive parameters.

[0006] It is intended to solve the technical problem that traditional rigid fixtures are difficult to dynamically adapt to complex force environments due to material deformation and load fluctuations during the processing, resulting in positioning offsets. By first scanning the surface contour information of the workpiece and determining the contact support coordinates of the plurality of elastic side-pushing components according to the surface contour information; then, scanning the surface contour information of the workpiece again and determining the contact support coordinates of the plurality of elastic side-pushing components according to the surface contour information; further, acquiring the processing data of processing the workpiece, where the processing data includes processing equipment information and processing load information; after that, performing a force simulation prediction on the workpiece according to the processing data, obtaining force simulation data, inputting the force simulation data into an elastic flexible adjustment model for force impedance analysis, and obtaining a plurality of flexible drive parameters corresponding to the plurality of elastic side-pushing components; finally, performing flexible drive clamping based on the initial processing positioning according to the plurality of flexible drive parameters. The technical effect of improving the positioning stability and deformation compensation ability of the workpiece in dynamic processing and realizing high-precision adaptive processing is achieved through real-time force impedance analysis and flexible clamping parameter adjustment. Description of the Drawings

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the device according to the embodiments of the present application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0008] Figure 1Schematic flow diagram of a method for improving the processing accuracy based on a multi-directional elastic side-pushing jig provided by an embodiment of the present application.

[0009] Figure 2 Schematic structural diagram of a device for improving the processing accuracy based on a multi-directional elastic side-pushing jig provided by an embodiment of the present application.

[0010] Explanation of reference numerals: Coordinate determination module 11, processing positioning module 12, data acquisition module 13, force impedance analysis module 14, flexible drive clamping module 15. Detailed implementation manners

[0011] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0012] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first\second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0014] The embodiment of the present application provides a method for improving the processing accuracy based on a multi-directional elastic side-pushing jig. The multi-directional elastic side-pushing jig includes a plurality of elastic side-pushing components, and each elastic side-pushing component includes a flexible driver, a cushion layer and an embedded sensing sheet. As Figure 1 shown, the method includes: Scanning the surface profile information of the workpiece, and determining the contact support coordinates of the plurality of elastic side-pushing components according to the surface profile information.

[0015] The multi-directional elastic side-pushing fixture is composed of multiple elastic side-pushing components. Each component contains a flexible actuator, a cushion layer, and an embedded sensing chip inside. The flexible actuator is a device that can adjust its shape or force according to external signals. It usually uses adjustable elastic materials (such as shape memory alloys) to provide precise support force. The cushion layer is a flexible layer installed between the component and the workpiece, mainly used to enhance the adaptability between the contact surfaces, ensure uniform contact force under different workpiece shapes, and avoid damage caused by direct contact. The embedded sensing chip is embedded in the fixture, which can real-time monitor the contact pressure and minute displacement changes between the workpiece and the side-pushing component, thereby providing feedback signals to ensure the real-time adaptation of the force distribution and the workpiece shape during the clamping process and guarantee the machining accuracy. These components work together to form a highly flexible and responsive clamping device.

[0016] In the embodiment of the present application, first, the surface of the workpiece is scanned by a scanning device, such as a laser scanner or other sensors, to obtain the surface geometric data of the workpiece, and these data are stored as the surface contour information of the workpiece. Subsequently, according to these surface contour information, the shape and surface features of the workpiece are analyzed, and the contact support coordinates of multiple elastic side-pushing components on the workpiece surface are calculated. These coordinates are determined based on the geometric characteristics of the workpiece surface and the layout requirements of the elastic side-pushing components, aiming to ensure that each side-pushing component can provide support force at the correct position and direction. Through such positioning, it is ensured that the side-pushing components are evenly distributed on the workpiece surface and can accurately apply appropriate pressure and clamping force during the machining process.

[0017] Furthermore, the present application provides a method for determining the contact support coordinates of the multiple elastic side-pushing components according to the surface contour information, including: Performing point cloud filtering processing on the surface contour information to generate a surface point cloud model; aligning the coordinates of the surface point cloud model with the CAD reference model of the workpiece, and using the iterative closest point algorithm for registration to output an optimized surface point cloud model; identifying the priority of the clamping areas of the optimized surface point cloud model to obtain a clamping area sequence, and determining the contact support coordinates of the multiple elastic side-pushing components based on the clamping area sequence.

[0018] Preferably, for the point cloud data stored in the surface profile information, some noise points may be introduced due to measurement errors, reflection interference, etc. These noise points do not represent the actual surface of the workpiece. Therefore, the point cloud data will be filtered. During this process, the point cloud density in the neighborhood of each point will be analyzed, and the abnormal points deviating from most points will be removed. Then, according to the radius range around the point, the points whose distance from the surrounding points is greater than the set threshold will be removed. After removing the noise points, there may be some non-smooth regions in the point cloud model. In order to obtain a smoother and more continuous surface, a smoothing filter algorithm such as Gaussian smoothing, median filtering, etc. can be used to smooth the point cloud. Among them, Gaussian smoothing smooths the point cloud through a Gaussian function, making the point distribution in the local area more uniform; median filtering replaces the coordinates of the current point according to the median of the points in the neighborhood of each point, thereby reducing the influence of irregular or isolated points. After the above filtering process, the obtained point cloud data will more accurately reflect the actual surface shape of the workpiece. At this time, the processed point cloud data will be used to construct a surface point cloud model, which can accurately represent the true geometric shape of the workpiece surface and is used for subsequent machining accuracy control, simulation analysis, and calculation of support coordinates. Subsequently, the obtained surface point cloud model will be aligned with the CAD reference model of the workpiece. To ensure the matching accuracy between the point cloud data and the CAD model, the Iterative Closest Point (ICP) algorithm is used for registration. This algorithm gradually adjusts the relative position of the point cloud model and the CAD model in an iterative manner to minimize the error between the two. After multiple iterations, an optimized surface point cloud model is output to make it perfectly aligned with the CAD reference model. After obtaining the optimized surface point cloud model, the priority identification of the clamping area will be carried out, that is, by analyzing the geometric features of the workpiece surface point cloud model, such as curvature, angle change, etc., to determine which areas are suitable for clamping, thereby generating a priority sequence of the clamping area. Finally, according to these priority sequences, the contact support coordinates of multiple elastic side-pushing components are determined to ensure that each component can provide the best support force at the appropriate position during the machining process.

[0019] Furthermore, the present application provides a method for determining the contact support coordinates of the multiple elastic side-pushing components based on the clamping area sequence. The contact support coordinates include a contact parameter vector. The method for obtaining the contact parameter vector includes: Determining the local curvature and normal vector of each support point based on the clamping area sequence, and calculating the contact parameter vectors corresponding to the multiple elastic side-pushing components; wherein, the contact parameter vector corresponding to each elastic side-pushing component includes a pushing direction and an angle.

[0020] Optionally, when determining the contact support coordinates of multiple elastic side-pushing components based on the clamping area sequence, first analyze each area according to the optimized clamping area sequence. Each clamping area is divided into multiple support points, which are key positions on the workpiece surface and are usually selected according to geometric features, such as positions with a large surface curvature or areas related to machining interference. Subsequently, for each support point, calculate its local curvature and normal vector. The local curvature reflects the degree of bending of the workpiece surface at this point and is usually obtained by fitting the surface point cloud and calculating the curvature value of the neighborhood around this point; the normal vector is a vector perpendicular to the workpiece surface, indicating the direction of the surface orientation of the support point and is usually obtained by calculating the local plane or surface of the surface point cloud. Once the local curvature and normal vector of the support point are obtained, the contact parameter vector corresponding to each elastic side-pushing component is calculated based on this information. The contact parameter vector mainly includes the pushing direction and angle. The pushing direction determines the direction of the force applied by the side-pushing component. When the local curvature is less than the corresponding threshold, it is considered that the pushing direction is consistent with the normal vector direction. On the contrary, the adjustment parameter is calculated by multiplying the local curvature by a constant coefficient. This constant coefficient is used to adjust the relationship between the curvature and the offset of the pushing direction. Then, the adjustment parameter is multiplied by the surface tangent direction (the direction perpendicular to the normal vector) and added to the normal vector to obtain the pushing direction to ensure that the supporting force can effectively act on the workpiece surface; the angle is the inclination angle relative to the normal vector of the support point and is used to represent the positional relationship between the side-pushing component and the workpiece surface. It is calculated according to the formula where N is the normal vector and D is the pushing direction. After that, the calculated pushing direction and angle are organized into a contact parameter vector, and this contact parameter vector is used as the contact support coordinate. In this way, the contact support coordinates of each elastic side-pushing component can be accurately determined, and accurate parameters can be provided for the subsequent flexible drive clamping process, so as to ensure that each side-pushing component can provide uniform supporting force at the best angle and direction during the machining process.

[0021] Furthermore, the present application provides a method for identifying the clamping area priority of the optimized surface point cloud model, including: Calling the optimized surface point cloud model to mark the defective areas of the surface area; analyzing the curvature change and geometric mutation positions of the non-defective areas to obtain the regional structure stability; analyzing the machining interference sensitive areas of the non-defective areas to obtain the machining interference risk; evaluating the clamping priority according to the regional structure stability and the machining interference risk to obtain the priority evaluation result, and outputting the clamping area sequence in descending order according to the priority evaluation result.

[0022] Optionally, an optimized surface point cloud model is used to perform a detailed analysis of different regions of the workpiece surface. By calculating the normal vector of each point, regions with abnormal surface changes are identified. For example, places where the direction of the normal vector suddenly changes may indicate surface defects. By calculating the local curvature, regions where the local curvature is greater than the abnormal curvature limit value are identified. The surfaces of these regions usually have drastic changes and may be manifestations of cracks or depressions. By marking the identified regions above (such as with colors or other visual cues), defective regions of the surface area are obtained. These regions require special attention because they may affect the machining quality of the workpiece. Subsequently, for non-defective regions of the workpiece surface, an analysis of curvature changes and geometric mutation positions is performed. Specifically, for each set of adjacent points, the absolute difference in curvature between these two points is calculated to represent the curvature change, that is, the curvature difference. Then, the curvature differences of all adjacent points are added up to obtain the total curvature change sum. Next, the ratio of the total curvature difference sum to the maximum curvature difference is calculated, and the resulting value represents the relative degree of curvature change. Then, 1 minus this ratio is used to obtain the curvature stability score. After that, the angle between the normal vectors of each set of adjacent points is calculated. If the angle is greater than or equal to the mutation angle, it indicates that there is a geometric mutation between these two points. After calculating all the angles, the number of regions with geometric mutations is counted. Then, the counted number is compared with the tolerated mutation number. If it is less than or equal to the tolerated mutation number, it indicates high geometric stability. At this time, the geometric stability score is set to 1. On the contrary, it indicates that there are more geometric mutations and the workpiece may be prone to deformation during the machining process. At this time, the difference between the number of regions with geometric mutations and the tolerated mutation number is calculated, and then the ratio of the difference to the tolerated mutation number is calculated. Then, 1 minus the calculated ratio is used to obtain the geometric stability score. After completing the analysis of curvature changes and geometric mutation positions, the obtained curvature stability score and geometric stability score are weighted to obtain the regional structure stability score. Further, the machining interference sensitive regions in the non-defective regions are also analyzed. In this step, the number of points with abnormal angles between curvature and normal vectors in each region is counted, and the ratio of the number of abnormal points in each region to the maximum number of abnormal points is calculated to obtain the machining interference risk of each region. Then, 1 minus the regional structure stability score, and the difference and the machining interference risk are combined for weighted calculation to obtain the clamping priority of each region. Then, these clamping priorities are summarized to obtain the priority evaluation result. Finally, the priority evaluation result is sorted in descending order to finally generate a clamping region sequence. In this sequence, the regions with higher priorities represent higher risks, so they need to be clamped first to avoid problems such as deformation.

[0023] Furthermore, the present application provides that the flexible actuator is an adjustable elastic structure composed of a dielectric elastomer, a shape memory alloy, or a magnetorheological material.

[0024] Preferably, the flexible actuator is an adjustable elastic structure that uses special materials such as dielectric elastomers, shape memory alloys, or magnetorheological materials. These materials have unique elastic and deformation characteristics and can adjust their shape or stiffness under the action of an external electric field, temperature, or magnetic field. Among them, the dielectric elastomer is an electroactive material that can deform under the action of an electric field, has high elasticity and adjustability, and is widely used in drive systems to adjust its shape and stiffness through the control of the electric field. The shape memory alloy is an alloy material that can restore its original shape at a specific temperature. When the temperature changes, the shape memory alloy will change its shape and can precisely adjust its deformation through temperature control, thereby realizing the flexible drive of the workpiece. The magnetorheological material is a material whose physical properties (such as viscosity or stiffness) change under the action of a magnetic field. When a magnetic field is applied, the magnetorheological material can adjust its fluidity, thereby affecting the rigidity or elasticity of the flexible actuator. These materials are used to construct the flexible actuator. By controlling the electric field, temperature, or magnetic field, the actuator can adjust its elastic characteristics according to requirements, thereby realizing the precise clamping and support of the workpiece.

[0025] Further, the present application provides a method for determining the contact support coordinates of the plurality of elastic side push components according to the surface profile information, and the method further includes: Calculating the geometric complexity of the workpiece according to the surface profile information; when the geometric complexity is less than a preset complexity threshold, optimizing the contact support coordinates of the plurality of elastic side push components by using symmetric distribution; when the geometric complexity is greater than or equal to the preset complexity threshold, optimizing the contact support coordinates of the plurality of elastic side push components by using multi-point collaborative distribution.

[0026] Optionally, according to the surface profile information, the geometric complexity of the workpiece is calculated. Geometric complexity is an index to measure the complexity of the workpiece surface shape, and it is usually calculated by analyzing the geometric features of the surface point cloud data. Specifically, the local curvature of the workpiece is statistically calculated to obtain the maximum local curvature and the standard deviation of local curvature; the ratio of the number of points with abnormal normal vector angles to the total number of point clouds is calculated to obtain the geometric mutation ratio. By performing weighted calculation on the maximum local curvature, the standard deviation of local curvature, and the geometric mutation ratio, the geometric complexity of the workpiece is obtained. Subsequently, the calculated geometric complexity is compared with a preset complexity threshold. When the geometric complexity is less than the preset complexity threshold, it indicates that the workpiece surface is relatively smooth and has strong symmetry. At this time, the contact support coordinates of multiple elastic side-pushing components can be optimized by using a symmetric distribution method, that is, according to the symmetric characteristics of the workpiece surface, the elastic side-pushing components are evenly distributed in the symmetric area of the workpiece to reduce the unevenness of the contact force. This method simplifies the contact point selection process and can more efficiently achieve precise clamping. When the geometric complexity is greater than or equal to the preset complexity threshold, it indicates that the workpiece surface is relatively complex and lacks obvious symmetry. Therefore, the contact points cannot be simply determined by using symmetric distribution. At this time, a multi-point collaborative distribution optimization method is adopted. This method analyzes multiple feature points on the workpiece surface, considering factors such as surface curvature, normal vector, and local deformation, to optimize the contact support coordinates of each elastic side-pushing component, so as to be able to more precisely adapt to complex curved surfaces and ensure that each elastic side-pushing component provides the best supporting force according to the specific conditions of different positions. For example, the elastic side-pushing component in the area with low regional complexity (obtained by weighting the local curvature, the standard deviation of local curvature, and the geometric mutation ratio of the area) is adjusted to the area with high regional complexity and without an elastic side-pushing component. Through these two optimization strategies, the optimal contact support coordinates of the elastic side-pushing components can be accurately determined on workpieces with different complexities, so as to ensure the stable clamping and precise machining of the workpiece during the machining process.

[0027] Perform initial machining positioning on the workpiece according to the contact support coordinates.

[0028] In one embodiment, based on the determined contact support coordinates, initial machining positioning of the workpiece is performed. Specifically, the contact support coordinates are the precise positions of multiple support points on the workpiece surface, and these points correspond to the contact positions of the elastic side-pushing components. According to this coordinate information, by controlling the positioning system of the machining equipment, the workpiece is accurately placed within the machining area. The goal of this step is to ensure that the workpiece can be stably positioned at the predetermined position before machining starts, avoiding machining errors caused by inaccurate positioning. During the initial machining positioning process, the position, angle, and posture of the workpiece are adjusted and optimized according to the contact support coordinates to ensure that each support point can precisely contact the corresponding elastic side-pushing component. This not only provides a stable foundation for subsequent machining but also ensures uniform application of the clamping force, thereby improving machining accuracy and the quality of the workpiece.

[0029] Collect machining data for machining the workpiece, where the machining data includes machining equipment information and machining load information.

[0030] In one embodiment, during the machining process, machining data of the workpiece is collected in real time, and this data includes machining equipment information and machining load information. Machining equipment information refers to equipment parameters and status information related to the machining process. For example, the operating speed of the machining tool, the spindle speed, the status of the cutting tool, the working status of the machining equipment, etc. This information helps monitor the operation of the equipment and ensures that the equipment performs machining under the best working conditions. Machining load information includes physical quantities such as the force, pressure, and vibration endured by the workpiece during the machining process. This load information can be collected through sensors to help understand the actual mechanical effects on the workpiece during machining and ensure that the load during the machining process is within a safe range, avoiding overload or uneven force distribution, which may affect the machining quality of the workpiece. By collecting this machining data, the machining process can be monitored and adjusted in real time, thereby optimizing process parameters and ensuring machining accuracy and efficiency.

[0031] Perform force simulation prediction on the workpiece according to the machining data to obtain force simulation data, and input the force simulation data into the elastic flexible adjustment model for force impedance analysis to obtain multiple flexible drive parameters corresponding to the multiple elastic side-pushing components.

[0032] In one embodiment, according to the collected machining data, first, a finite element simulation model is used to simulate various external forces acting on the workpiece during machining, such as cutting force, vibration, temperature change, etc., and predict the deformation and force distribution of the workpiece under different loads, obtaining a set of force simulation data. This data contains information such as the deformation, stress distribution, and possible vibration vectors of the workpiece in each force state. Subsequently, the force simulation data is input into the elastic force flexible adjustment model for force impedance analysis to calculate the flexible drive parameters required for each elastic force side-pushing component, such as the amplitude of the flexible clamping force of the side-pushing component (i.e., the intensity of the supporting force), the pushing direction (the direction in which the supporting force is applied), and the response time (the time required for supporting force adjustment). With these parameters, it can be ensured that each elastic force side-pushing component dynamically adjusts the supporting force according to the deformation of the workpiece surface to achieve precise clamping and machining control.

[0033] Furthermore, the present application provides a method for performing force simulation prediction on the workpiece according to the machining data to obtain force simulation data, including: Construct a finite element simulation model of the workpiece in the machining coordinate system; based on the machining data in the same machining coordinate system, perform machining load simulation on the finite element simulation model to obtain the force simulation data, where the force simulation data includes deformation data, vibration vectors, and stress distribution of the workpiece under various loads.

[0034] Preferably, a finite element simulation model of the workpiece in the machining coordinate system is constructed. This process first requires converting the surface point cloud model of the workpiece into a finite element model, including dividing the workpiece surface into a series of small elements (such as triangular or quadrilateral elements), and assigning material properties to each element according to the physical properties of the workpiece (such as elastic modulus, density, yield strength of the material, etc.). Then, the machining coordinate system is set according to the placement position of the workpiece in the actual machining to ensure that the simulation model is consistent with the actual machining environment. In addition, the model also needs to consider boundary conditions and constraint conditions, such as the fixed position of the workpiece, the contact situation between the machining tool and the workpiece, etc. These conditions provide the basis for subsequent simulation calculations. After constructing the finite element simulation model, the machining data in the same machining coordinate system is used as input and applied to the finite element simulation model for load simulation. By applying these loads in the finite element simulation model, the finite element simulation model can simulate the influence of various forces on the workpiece during the machining process, and calculate the physical responses of the workpiece such as deformation, stress, and vibration. By recording the calculation results of the finite element simulation model, force simulation data is obtained. This force simulation data includes deformation data of the workpiece under different loads (describing how the workpiece surface changes under the action of external forces), vibration vectors (representing the vibration characteristics and directions of the workpiece during the machining process), and stress distribution (describing the stress states of different regions inside the workpiece). These data are crucial for subsequent machining process optimization and clamping adjustment, and can help predict possible defects during the machining process, thereby providing a basis for precisely controlling the machining process and improving machining accuracy.

[0035] Furthermore, the present application provides a method for inputting the force simulation data into an elastic flexible adjustment model for force impedance analysis to obtain a plurality of flexible drive parameters corresponding to the plurality of elastic side push components. The method includes: Associatively mapping the force simulation data to the contact support coordinates corresponding to each elastic side push component to obtain the local force states corresponding to the plurality of elastic side push components; constructing a local force-displacement relationship model according to the flexible response characteristics of the contact support coordinates. The local force-displacement relationship model is used to perform impedance error analysis based on the local force states corresponding to the plurality of elastic side push components, and calculate the flexible clamping force amplitude, propulsion direction, and response time corresponding to each elastic side push component; outputting the flexible clamping force amplitude, propulsion direction, and response time as flexible drive parameters.

[0036] Optionally, after obtaining the force simulation data, the force simulation data is associated and mapped with the contact support coordinates corresponding to each elastic side-pushing component, that is, the simulation data corresponding to each elastic side-pushing component is mapped to the corresponding contact support coordinates to obtain the local force state corresponding to each elastic side-pushing component. The purpose of this step is to determine the actual force state of each elastic side-pushing component on the workpiece surface and evaluate the mechanical performance of each contact point on the workpiece surface during the machining process. Subsequently, according to the flexible response characteristics (such as elastic modulus, deformation ability, restoring force, damping characteristics, etc.) of the elastic side-pushing components corresponding to each contact support coordinate, sample data with errors within the tolerance range is called, including sample local force states and sample displacement parameters. Subsequently, the basic structure of the local force-displacement relationship model is constructed, which can be generated by initializing models such as feedforward neural networks, deep neural networks, support vector machines, Gaussian process regression, etc. Exemplarily, if a feedforward neural network is selected, the sample local force state and sample displacement parameters are input into the local force-displacement relationship model, and iterative training is performed through steps such as forward propagation, loss calculation (mean square error), backpropagation, and parameter optimization (Adam optimizer). After that, the local force states corresponding to multiple elastic side-pushing components are sequentially input into the corresponding local force-displacement relationship models for impedance error analysis. The purpose of the impedance error analysis is to calculate the possible mechanical mismatches (such as the asynchrony of force and displacement) that may occur in each elastic side-pushing component during actual operation, so as to calculate the target displacement corresponding to each elastic side-pushing component. Then, the target displacement corresponding to each elastic side-pushing component is input into the elastic flexibility adjustment model (the construction method is similar to the construction of the above local force-displacement relationship model, and the data used is sample displacement and sample flexible drive parameters) for mapping to obtain the flexible drive parameters of each elastic side-pushing component, including the amplitude of the flexible clamping force, the propulsion direction, and the response time. These parameters can accurately reflect the performance of each elastic side-pushing component during the machining process and are used to guide the actual drive and control of the elastic side-pushing components to achieve precise workpiece clamping and machining process control.

[0037] Perform flexible drive clamping based on the multiple flexible drive parameters according to the initial machining positioning.

[0038] In one embodiment, after obtaining multiple flexible driving parameters, these parameters will be applied to the actual clamping operation. Specifically, first, the initial position and angle of the workpiece during the clamping process are determined through initial machining positioning. Then, according to the calculated flexible driving parameters, each elastic side-pushing component is controlled to apply an appropriate clamping force at a specific position. Each side-pushing component will determine how much supporting force to apply according to the corresponding clamping force amplitude, and ensure that the applied force is exerted in the correct direction according to the pushing direction, ensuring uniform and effective force transmission during the clamping process. In addition, according to the response time, the reaction speed of the elastic side-pushing component is adjusted to ensure that the clamping force reaches a stable state in a short time, avoiding clamping instability or errors caused by response delays. Through the precise control of these flexible driving parameters, the entire clamping process can achieve precise support and firm fixation of the workpiece, ensuring that the workpiece does not displace or deform during the machining process, thereby effectively improving the machining accuracy and quality.

[0039] Further, the present application provides flexible driving clamping based on the initial machining positioning according to the multiple flexible driving parameters. The method further includes: Using the embedded sensing chip to collect the surface micro-displacement information and contact pressure during the workpiece clamping process in real time; comparing the micro-displacement information with the target displacement of the elastic flexible adjustment model, calculating the current clamping error, and adjusting the input signal of the flexible driver based on the clamping error for feedback control.

[0040] Preferably, during the clamping process of the workpiece, the embedded sensing chip will collect the micro-displacement information and contact pressure on the surface of the workpiece in real time. Among them, the micro-displacement information reflects the deformation of the workpiece surface during the clamping process, while the contact pressure is used to measure the contact tightness between the side-pushing component and the workpiece surface. Subsequently, the collected micro-displacement information will be compared with the target displacement input into the elastic force flexible adjustment model. The target displacement is calculated based on the local force-displacement relationship model, which represents the ideal surface deformation state of the workpiece during the clamping process. By comparing the micro-displacement and the target displacement, the current clamping error can be calculated, that is, the deviation between the actual surface of the workpiece and the ideal surface. Once the clamping error is obtained, the input signal of the flexible actuator will be adjusted according to this error. This process is called feedback control. The goal of feedback control is to reduce the clamping error by adjusting the working state of the flexible actuator, ensuring that the workpiece is firmly clamped and will not undergo excessive deformation or misalignment. The adjustment method is to add the clamping error to the target displacement, and then input it into the elastic force flexible adjustment model again to obtain new flexible drive parameters, and use these new flexible drive parameters as a new input signal to adjust the flexible actuator, such as changing the magnitude of the clamping force applied by the side-pushing component, the direction of the applied force, or optimizing the response speed, to achieve a more precise clamping effect. Through this dynamic feedback control, the clamping force can be continuously monitored and adjusted during the workpiece clamping process, ensuring the stability and machining accuracy of the workpiece during the machining process.

[0041] In the above text, reference is made to Figure 1 The method for improving the machining accuracy based on the multi-directional elastic force side-pushing fixture according to the embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe the device for improving the machining accuracy based on the multi-directional elastic force side-pushing fixture according to the embodiment of the present invention.

[0042] The device for improving the machining accuracy based on the multi-directional elastic force side-pushing fixture according to the embodiment of the present invention is used to solve the technical problem that the traditional rigid fixture causes positioning deviation due to material deformation and load fluctuation during the machining process and is difficult to dynamically adapt to complex stress environments. It achieves the technical effect of improving the positioning stability and deformation compensation ability of the workpiece in dynamic machining through real-time force impedance analysis and flexible clamping parameter adjustment, and realizing high-precision adaptive machining. The device for improving the machining accuracy based on the multi-directional elastic force side-pushing fixture includes: a coordinate determination module 11, a machining positioning module 12, a data acquisition module 13, a force impedance analysis module 14, and a flexible drive clamping module 15.

[0043] Coordinate determination module 11: Scanning the surface contour information of the workpiece, and determining the contact support coordinates of the multiple elastic side-pushing components according to the surface contour information; Machining positioning module 12: Performing initial machining positioning on the workpiece according to the contact support coordinates; Data acquisition module 13: Collecting the machining data for machining the workpiece, where the machining data includes machining equipment information and machining load information; Force impedance analysis module 14: Performing force simulation prediction on the workpiece according to the machining data, obtaining force simulation data, inputting the force simulation data into the elastic flexible adjustment model for force impedance analysis, and obtaining multiple flexible drive parameters corresponding to the multiple elastic side-pushing components; Flexible drive clamping module 15: Performing flexible drive clamping based on the initial machining positioning according to the multiple flexible drive parameters.

[0044] Furthermore, the coordinate determination module 11 further includes: Performing point cloud filtering processing on the surface contour information to generate a surface point cloud model; Aligning the coordinates of the surface point cloud model with the CAD reference model of the workpiece, performing registration using the iterative closest point algorithm, and outputting the optimized surface point cloud model; Identifying the priority of the clamping areas of the optimized surface point cloud model to obtain a clamping area sequence, and determining the contact support coordinates of the multiple elastic side-pushing components based on the clamping area sequence.

[0045] Furthermore, the coordinate determination module 11 further includes: Determining the local curvature and normal vector of each support point based on the clamping area sequence, and calculating the contact parameter vectors corresponding to the multiple elastic side-pushing components; Among them, the contact parameter vector corresponding to each elastic side-pushing component includes a pushing direction and an angle.

[0046] Furthermore, the coordinate determination module 11 further includes: Invoking the optimized surface point cloud model to mark the defective areas of the surface area; Analyzing the curvature change and geometric mutation positions of the non-defective areas to obtain the regional structure stability; Analyzing the machining interference sensitive areas of the non-defective areas to obtain the machining interference risk; Evaluating the clamping priority according to the regional structure stability and the machining interference risk, obtaining the priority evaluation result, and outputting the clamping area sequence in descending order according to the priority evaluation result.

[0047] Furthermore, the coordinate determination module 11 further includes: The flexible driver is an adjustable elastic structure based on dielectric elastomers, shape memory alloys or magnetorheological materials.

[0048] Furthermore, the coordinate determination module 11 further includes: Calculate the geometric complexity of the workpiece according to the surface profile information; when the geometric complexity is less than a preset complexity threshold, optimize the contact support coordinates of the multiple elastic side-pushing components by using symmetric distribution; when the geometric complexity is greater than or equal to the preset complexity threshold, optimize the contact support coordinates of the multiple elastic side-pushing components by using multi-point collaborative distribution.

[0049] Further, the force impedance analysis module 14 further includes: Construct a finite element simulation model of the workpiece in the machining coordinate system; based on the machining data in the same machining coordinate system, perform machining load simulation on the finite element simulation model to obtain the force simulation data, where the force simulation data includes deformation data, vibration vectors, and stress distributions of the workpiece under various loads.

[0050] Further, the force impedance analysis module 14 further includes: Associate and map the force simulation data to the contact support coordinates corresponding to each elastic side-pushing component to obtain the local force states corresponding to the multiple elastic side-pushing components; construct a local force-displacement relationship model according to the flexible response characteristics of the contact support coordinates, and the local force-displacement relationship model is used to perform impedance error analysis according to the local force states corresponding to the multiple elastic side-pushing components, and calculate the flexible clamping force amplitude, pushing direction, and response time corresponding to each elastic side-pushing component; output the flexible clamping force amplitude, pushing direction, and response time as flexible driving parameters.

[0051] Further, the flexible driving and clamping module 15 further includes: Use the embedded sensing chip to real-time collect the surface micro-displacement information and contact pressure during the workpiece clamping process; compare the micro-displacement information with the target displacement of the elastic flexible adjustment model, calculate the current clamping error, and adjust the input signal of the flexible driver based on the clamping error for feedback control.

[0052] The machining accuracy improvement device based on the multi-direction elastic side-pushing jig provided by the embodiment of the present invention can execute the machining accuracy improvement method based on the multi-direction elastic side-pushing jig provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0053] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server. The included various units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0054] The above specific embodiments do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A method for improving machining accuracy based on a multi-directional elastic side-thrust fixture, characterized in that: The multi-directional elastic side thrust fixture comprises a plurality of elastic side thrust components, each of which comprises a flexible driver, a cushion layer and an embedded sensor sheet, and the method comprises: Scanning the surface profile information of the workpiece, and determining the contact support coordinates of the plurality of elastic side thrust assemblies according to the surface profile information; Performing initial processing positioning on the workpiece according to the contact support coordinates; Collecting processing data of the workpiece, wherein the processing data includes processing equipment information and processing load information; Perform force simulation prediction on the workpiece according to the processing data, obtain force simulation data, input the force simulation data into the elastic flexibility adjustment model to perform force impedance analysis, and obtain multiple flexible driving parameters corresponding to the multiple elastic thrust assemblies; Flexible drive clamping is performed based on the initial processing positioning according to the multiple flexible drive parameters.

2. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 1, characterized in that: Determining the contact support coordinates of the plurality of elastic thrust assemblies according to the surface profile information, the method comprising: Performing point cloud filtering processing on the surface contour information to generate a surface point cloud model; Aligning the coordinates of the surface point cloud model with the CAD reference model of the workpiece, performing registration using an iterative closest point algorithm, and outputting an optimized surface point cloud model; The clamping area priority of the optimized surface point cloud model is identified to obtain a clamping area sequence, and the contact support coordinates of the plurality of elastic thrust assemblies are determined based on the clamping area sequence.

3. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 2, characterized in that: The contact support coordinates of the plurality of elastic thrust assemblies are determined based on the clamping area sequence, the contact support coordinates including a contact parameter vector, and a method for obtaining the contact parameter vector includes: Determine the local curvature and normal vector of each support point based on the clamping area sequence, and calculate the contact parameter vectors corresponding to the plurality of elastic thrust assemblies; The contact parameter vector corresponding to each elastic thrust component includes a thrust direction and an angle.

4. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 2, characterized in that: Identify the clamping area priorities of the optimized surface point cloud model by: Call the optimized surface point cloud model to mark the defective areas of the surface area; The curvature change and geometric mutation position of the non-defective area are analyzed to obtain the regional structural stability; Perform machining interference sensitive area analysis on non-defective areas to obtain machining interference risks; A clamping priority evaluation is performed according to the structural stability of the region and the processing interference risk, a priority evaluation result is obtained, and a clamping region sequence is outputted by arranging the priority evaluation result in descending order.

5. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 1, characterized in that: Performing force simulation prediction on the workpiece according to the processing data to obtain force simulation data, the method includes: Constructing a finite element simulation model of the workpiece in a machining coordinate system; Based on the processing data in the same processing coordinate system, the processing load simulation is performed on the finite element simulation model to obtain the force simulation data, wherein the force simulation data includes deformation data, vibration vector and stress distribution of the workpiece under various loads.

6. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 5, characterized in that: The force simulation data is input into the elastic flexibility adjustment model to perform force impedance analysis, and a plurality of flexible driving parameters corresponding to the plurality of elastic thrust assemblies are obtained. The method includes: Associatively mapping the force simulation data to the contact support coordinates corresponding to each elastic thrust assembly, and obtaining the local force states corresponding to the multiple elastic thrust assemblies; According to the flexible response characteristics of the contact support coordinates, a local force-displacement relationship model is constructed, and the local force-displacement relationship model is used to perform impedance error analysis according to the local force states corresponding to the multiple elastic thrust assemblies, and calculate the flexible clamping force amplitude, propulsion direction and response time corresponding to each elastic thrust assembly; The flexible clamping force amplitude, propulsion direction and response time are output as flexible drive parameters.

7. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 1, characterized in that: Performing flexible drive clamping based on the initial processing positioning according to the multiple flexible drive parameters, the method further includes: The embedded sensor sheet is used to collect surface micro-displacement information and contact pressure in real time during the workpiece clamping process; The micro-displacement information is compared with the target displacement of the elastic flexibility adjustment model, the current clamping error is calculated, and the input signal of the flexible driver is adjusted based on the clamping error to perform feedback control.

8. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 1, characterized in that: The flexible actuator is an adjustable elastic structure based on dielectric elastomer, shape memory alloy or magnetorheological material.

9. The method for improving machining accuracy based on a multi-directional elastic side thrust fixture according to claim 2, characterized in that: Determining contact support coordinates of the plurality of elastic thrust assemblies according to the surface profile information, the method further comprising: Calculating the geometric complexity of the workpiece according to the surface profile information; When the geometric complexity is less than a preset complexity threshold, optimizing the contact support coordinates of the plurality of elastic thrust assemblies by using symmetric distribution; When the geometric complexity is greater than or equal to the preset complexity threshold, the contact support coordinates of the multiple elastic thrust assemblies are optimized using multi-point collaborative distribution.

10. A processing precision improvement device based on a multi-directional elastic side-thrust fixture, characterized in that: The multi-directional elastic side thrust fixture comprises a plurality of elastic side thrust components, each of which comprises a flexible driver, a cushion layer and an embedded sensor sheet. The device is used to implement the method for improving machining accuracy based on the multi-directional elastic side thrust fixture according to any one of claims 1 to 9, comprising: Coordinate determination module: scanning the surface profile information of the workpiece, and determining the contact support coordinates of the plurality of elastic thrust assemblies according to the surface profile information; Processing positioning module: performing initial processing positioning on the workpiece according to the contact support coordinates; Data acquisition module: collects processing data of the workpiece, wherein the processing data includes processing equipment information and processing load information; Force impedance analysis module: performs force simulation prediction on the workpiece according to the processing data, obtains force simulation data, inputs the force simulation data into the elastic flexibility adjustment model to perform force impedance analysis, and obtains multiple flexible driving parameters corresponding to the multiple elastic thrust assemblies; Flexible drive clamping module: performs flexible drive clamping based on the initial processing positioning according to the multiple flexible drive parameters.

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