Intelligent processing control method and system for milling machine

Abstract

The present invention provides a method and system for intelligent processing control of a milling machine, which includes the following steps: modeling the tool, fixture and workpiece entities in the machine tool processing environment as dynamic graph structure nodes; updating the weights of the dynamic graph structure edges based on the characteristic changes of the data nodes; establishing a hypergraph model of multiple physical fields with each data node as a supernode; performing modal analysis on the hypergraph model to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generating a homogeneous transformation matrix; adjusting the tool parameters based on the attitude error parameters and translation error parameters obtained from the homogeneous transformation matrix. The multi-physical field hypergraph model is constructed by connecting the supernodes with hyperedges, quantifying the coupling strength of the thermal field, stress field and vibration field with dynamic weights, capturing the phase coupling effect of thermal expansion and vibration acceleration, adjusting the tool compensation parameters, and improving the processing accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of milling machines, and in particular to an intelligent processing control method and system for milling machines. Background Art

[0002] CNC milling machine, also known as CNC (Computer Numerical Control) milling machine, refers to a milling machine controlled by electronic digital signals.

[0003] The machining accuracy control of CNC milling machines usually uses single physical field modeling (such as finite element thermal analysis or vibration modal analysis) to locally compensate for errors, but such methods are difficult to characterize the dynamic interaction between temperature field, stress field and vibration field. Especially in high-speed machining scenarios, the instantaneous temperature rise of the tool and the workpiece will cause thermal expansion of the material, and the vibration in the fixture area will further aggravate the structural deformation, resulting in problems such as tool chipping and workpiece deformation. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for intelligent machining control of a milling machine, aiming to solve the technical problem of poor tool error compensation accuracy in the prior art.

[0005] In order to achieve the above-mentioned object, in a first aspect, the present invention provides: a milling machine intelligent processing control method, comprising the following steps:

[0006] The tool, fixture and workpiece entities in the machine tool processing environment are modeled as dynamic graph structure nodes, the data collected by the temperature sensor, vibration sensor and fiber grating are integrated into the dynamic graph structure nodes to generate data nodes, and the data change relationship between the data nodes is defined as the dynamic graph structure edge;

[0007] Based on the characteristic changes of the data nodes, the weights of the edges of the dynamic graph structure are updated;

[0008] Each of the data nodes is used as a supernode, and multiple supernodes are connected to form a hyperedge that represents the interactive relationship of the thermal-mechanical-vibration physical field to establish a hypergraph model of multiple physical fields, and the dynamic weight of the hyperedge is obtained based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field;

[0009] The hypergraph model is reduced in order by using the superelement modal synthesis method, and independent modal analysis is performed based on the reduced substructure to extract the thermal expansion coefficient matrix and vibration modal matrix, and generate a homogeneous transformation matrix.

[0010] The tool parameters are adjusted by obtaining posture error parameters and translation error parameters based on the homogeneous transformation matrix.

[0011] According to one aspect of the above technical solution, the step of updating the weight of the edge of the dynamic graph structure specifically includes:

[0012] Based on the temperature sensor, the temperature difference between the tool cutting area and the workpiece processing area is obtained in real time, and based on the vibration sensor, the vibration acceleration value of the fixture fixing area is obtained in real time;

[0013] The weights of the edges of the dynamic graph structure are updated according to the temperature difference value and the vibration acceleration value.

[0014] According to one aspect of the above technical solution, the calculation expression for updating the weight of the dynamic graph structure edge according to the temperature difference value and the vibration acceleration value is:

[0015] ;

[0016] In the formula, is the temperature difference, is the temperature attenuation coefficient, is the temperature error weight coefficient, , is the vibration acceleration value of the m and n nodes on the fixture fixing area, is the vibration error weight coefficient, is the vibration suppression coefficient, is the strain error between temperature nodes, is the strain error weight coefficient, is the coupling strength coefficient.

[0017] According to one aspect of the above technical solution, the step of obtaining the dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field specifically includes:

[0018] The dynamic weight of the hyperedge is obtained based on the weight of the edge of the dynamic graph structure according to the following calculation formula:

[0019] ;

[0020] In the formula, is the weight of the hyperedge H, is the physical field strength of node v, is the contribution weight of node v, is the geometric mean of all dynamic graph structure edges within the hyperedge, is the node’s collaborative adjustment parameter, is the rate of change of the total strength of the node, Adjust parameters for node change trends.

[0021] According to one aspect of the above technical solution, the steps of reducing the order of the hypergraph model by the super-element modal synthesis method, performing independent modal analysis based on the reduced substructure to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generating the homogeneous transformation matrix specifically include:

[0022] Dividing each entity corresponding to the dynamic graph structure node into a plurality of substructures, performing independent modal analysis on each substructure, and obtaining a local thermal strain modal matrix and a local vibration modal matrix of the substructure;

[0023] Based on the interface degrees of freedom between each substructure, the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure are coupled to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model;

[0024] The thermal deformation error and the vibration displacement error are quantified according to the thermal expansion coefficient matrix and the vibration mode matrix respectively to generate a homogeneous transformation matrix.

[0025] According to one aspect of the above technical solution, based on the interface degrees of freedom between the substructures, the steps of coupling the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model specifically include:

[0026] The transformation displacement vector that expands the local mode to the global coordinates is calculated based on the internal degrees of freedom of the substructure and the interface degrees of freedom between the substructures according to the following calculation formula:

[0027] ;

[0028] ;

[0029] In the formula, is the transformation displacement vector, For fixed interface mode, is the constrained mode, is the unit mapping matrix of the interface degrees of freedom, is the internal degree of freedom of the substructure, is the interface degree of freedom between substructures, are the modal coordinates of the substructure, is the modal matrix of the substructure;

[0030] A transformation matrix is ​​constructed based on the transformation displacement vector, so as to couple the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure based on the transformation matrix to obtain a global thermal expansion coefficient matrix and a vibration modal matrix.

[0031] According to one aspect of the above technical solution, the steps of quantifying the thermal deformation error and the vibration displacement error according to the thermal expansion coefficient matrix and the vibration modal matrix to generate a homogeneous transformation matrix specifically include:

[0032] The thermal deformation error is calculated based on the thermal expansion coefficient matrix according to the following calculation formula:

[0033] ;

[0034] In the formula, is the thermal deformation error, is the thermal expansion coefficient matrix, is the thermal strain driving vector, is the thermal load vector, K is the stiffness matrix;

[0035] The thermal deformation error is calculated based on the vibration mode matrix according to the following calculation formula:

[0036] ;

[0037] In the formula, is the vibration displacement error, is the vibration mode matrix, are the modal coordinates, is the external excitation, M is the mass matrix, C is the damping matrix, j is an imaginary number, is the angular frequency of the excitation force;

[0038] According to the following calculation formula, the coupling strength of thermal deformation error and vibration displacement error is integrated through weighted least squares optimization method, and regularization constraints are performed according to the weight of the hyperedge to obtain the homogeneous transformation matrix:

[0039] ;

[0040] Where C is the homogeneous transformation matrix, Represents the error amount of the dynamic graph structure edge, is the regularization coefficient of the hyperedge.

[0041] According to one aspect of the above technical solution, the step of obtaining the posture error parameter and the translation error parameter based on the homogeneous transformation matrix to adjust the tool parameters specifically includes:

[0042] Correcting the original tool path through the homogeneous transformation matrix to obtain a compensation path;

[0043] An attitude error parameter and a translation error parameter are extracted from the homogeneous transformation matrix, a feedforward control quantity is generated according to the attitude error parameter, and a PID proportional gain is dynamically adjusted according to the norm of the translation error parameter.

[0044] In a second aspect, the present invention provides a milling machine intelligent processing control system, comprising:

[0045] A data module is used to model the tool, fixture and workpiece entities in the machine tool processing environment as dynamic graph structure nodes, fuse the dynamic graph structure nodes with the collected data of the temperature sensor, vibration sensor and fiber grating to generate data nodes, and define the data change relationship between the data nodes as a dynamic graph structure edge;

[0046] An updating module, used to update the weights of the edges of the dynamic graph structure based on the feature changes of the data nodes;

[0047] A coupling module, used to use each of the data nodes as a supernode, connect multiple supernodes to form a hyperedge that characterizes the interactive relationship of the thermal-mechanical-vibration physical field, so as to establish a hypergraph model of multiple physical fields, and obtain a dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field;

[0048] A compensation module is used to reduce the order of the hypergraph model by using the super-element modal synthesis method, perform independent modal analysis based on the reduced substructure to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generate a homogeneous transformation matrix;

[0049] The adjustment module is used to adjust the tool parameters by obtaining the posture error parameters and the translation error parameters based on the homogeneous transformation matrix.

[0050] According to one aspect of the above technical solution, the update module is specifically used for:

[0051] Based on the temperature sensor, the temperature difference between the tool cutting area and the workpiece processing area is obtained in real time, and based on the vibration sensor, the vibration acceleration value of the fixture fixing area is obtained in real time;

[0052] The weights of the edges of the dynamic graph structure are updated according to the temperature difference value and the vibration acceleration value.

[0053] According to one aspect of the above technical solution, the coupling module is specifically used to obtain the dynamic weight of the hyperedge based on the weight of the dynamic graph structure edge according to the following calculation formula:

[0054] ;

[0055] In the formula, is the weight of the hyperedge H, is the physical field strength at node v, is the contribution weight of node v, is the geometric mean of all dynamic graph structure edges within the hyperedge, is the node’s collaborative adjustment parameter, is the rate of change of the total strength of the node, Adjust parameters for node change trends.

[0056] According to one aspect of the above technical solution, the compensation module is specifically used for:

[0057] Dividing each entity corresponding to the dynamic graph structure node into a plurality of substructures, performing independent modal analysis on each substructure, and obtaining a local thermal strain modal matrix and a local vibration modal matrix of the substructure;

[0058] Based on the interface degrees of freedom between each substructure, the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure are coupled to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model;

[0059] The thermal deformation error and the vibration displacement error are quantified according to the thermal expansion coefficient matrix and the vibration mode matrix respectively to generate a homogeneous transformation matrix.

[0060] According to one aspect of the above technical solution, the compensation module is further used to calculate the transformation displacement vector that expands the local mode into the global coordinate based on the internal degrees of freedom of the substructure and the interface degrees of freedom between the substructures according to the following calculation formula:

[0061] ;

[0062] ;

[0063] In the formula, is the transformation displacement vector, For fixed interface mode, is the constrained mode, is the unit mapping matrix of the interface degrees of freedom, is the internal degree of freedom of the substructure, is the interface degree of freedom between each substructure, are the modal coordinates of the substructure, is the modal matrix of the substructure;

[0064] A transformation matrix is ​​constructed based on the transformation displacement vector, so as to couple the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure based on the transformation matrix to obtain a global thermal expansion coefficient matrix and a vibration modal matrix.

[0065] According to one aspect of the above technical solution, the compensation module is further used to calculate the thermal deformation error based on the thermal expansion coefficient matrix according to the following calculation formula:

[0066] ;

[0067] In the formula, is the thermal deformation error, is the thermal expansion coefficient matrix, is the thermal strain driving vector, is the thermal load vector, K is the stiffness matrix;

[0068] The thermal deformation error is calculated based on the vibration mode matrix according to the following calculation formula:

[0069] ;

[0070] In the formula, is the vibration displacement error, is the vibration mode matrix, are the modal coordinates, is the external excitation, M is the mass matrix, C is the damping matrix, j is an imaginary number, is the angular frequency of the excitation force;

[0071] According to the following calculation formula, the coupling strength of thermal deformation error and vibration displacement error is integrated through weighted least squares optimization method, and regularization constraints are performed according to the weight of the hyperedge to obtain the homogeneous transformation matrix:

[0072] ;

[0073] Where C is the homogeneous transformation matrix, Represents the error amount of the dynamic graph structure edge, is the regularization coefficient of the hyperedge.

[0074] According to one aspect of the above technical solution, the adjustment module is specifically used to: modify the original tool path through the homogeneous transformation matrix to obtain a compensation path;

[0075] An attitude error parameter and a translation error parameter are extracted from the homogeneous transformation matrix, a feedforward control quantity is generated according to the attitude error parameter, and a PID proportional gain is dynamically adjusted according to the norm of the translation error parameter.

[0076] Compared with the prior art, the beneficial effects of the present invention are as follows: by modeling the tool, fixture and workpiece as dynamic graph structure nodes and integrating multi-source sensor data such as temperature, vibration and fiber grating, the problem of mismatch between static parameters and dynamic working conditions in traditional modeling is solved, and a multi-physical field hypergraph model is constructed by connecting hypernodes through hyperedges. The coupling strength of the thermal field, stress field and vibration field is quantified with dynamic weights, and the phase coupling effect of thermal expansion and vibration acceleration is captured to adjust the tool compensation parameters and improve the machining accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1It is a flow chart of the intelligent machining control method for a milling machine in the first embodiment of the present invention;

[0078] Figure 2 It is a structural block diagram of the intelligent processing control system for a milling machine in the second embodiment of the present invention;

[0079] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0080] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0081] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0082] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0083] Embodiment 1

[0084] See also Figure 1 , which is a flow chart of the intelligent machining control method for a milling machine in the first embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method comprises the following steps:

[0085] Step S100, modeling the tools, fixtures and workpiece entities in the machine tool processing environment as dynamic graph structure nodes, integrating the data collected by temperature sensors, vibration sensors and fiber Bragg gratings into the dynamic graph structure nodes to generate data nodes, and defining the data change relationship between data nodes as dynamic graph structure edges. Specifically, the temperature sensor, vibration sensor and fiber Bragg grating are used to collect temperature data, vibration acceleration data and strain data, respectively. The data node attributes of the tools include geometric features, material features and temperature status, etc. The data node attributes of the fixture include clamping stiffness and vibration acceleration status, and the data node attributes of the workpiece include geometric features, material features and temperature status, etc. Structural edge representation

[0086] Step S200: updating the weights of the edges of the dynamic graph structure based on the feature changes of the data nodes.

[0087] Specifically, the step of updating the weight of the edge of the dynamic graph structure includes:

[0088] Based on the temperature sensor, the temperature difference between the tool cutting area and the workpiece processing area is obtained in real time, and based on the vibration sensor, the vibration acceleration value of the fixture fixing area is obtained in real time;

[0089] The weights of the edges of the dynamic graph structure are updated according to the temperature difference value and the vibration acceleration value.

[0090] Specifically, the above temperature difference value is obtained by collecting temperature data of the tool cutting area and the workpiece processing area, and performing sliding average filtering to suppress high-frequency noise; the vibration acceleration value includes vibration signals of two different measuring points in the fixture fixing area. The vibration signal is transformed by FFT, the main frequency band energy is extracted, and normalized to the acceleration amplitude.

[0091] Furthermore, in this embodiment, the calculation expression for updating the weight of the dynamic graph structure edge according to the temperature difference value and the vibration acceleration value is:

[0092] ;

[0093] In the formula, is the temperature difference, is the temperature attenuation coefficient, is the temperature error weight coefficient, , is the vibration acceleration value of the m and n nodes on the fixture fixing area, is the vibration error weight coefficient, is the vibration suppression coefficient, is the strain error between temperature nodes, is the strain error weight coefficient, is the coupling strength coefficient.

[0094] Step S300, taking each of the data nodes as a supernode, connecting multiple supernodes to form a hyperedge that characterizes the interactive relationship of the thermal-mechanical-vibration physical field, so as to establish a hypergraph model of multiple physical fields, and obtaining the dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field.

[0095] Preferably, in this embodiment, the step of obtaining the dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field specifically includes:

[0096] The dynamic weight of the hyperedge is obtained based on the weight of the edge of the dynamic graph structure according to the following calculation formula:

[0097] ;

[0098] In the formula, is the weight of the hyperedge H, is the physical field strength of node v, is the contribution weight of node v, is the geometric mean of all dynamic graph structure edges within the hyperedge, is the node’s collaborative adjustment parameter, is the rate of change of the total strength of the node, The parameters are adjusted according to the change trend of the node. It can be understood that in some application scenarios of this embodiment, when the weight of the hyperedge H is greater than the threshold, it indicates that the coupling strength of the current thermal-mechanical-vibration physical field is high, and the tool is slowed down or stopped.

[0099] Step S400, reducing the order of the hypergraph model by a super-element modal synthesis method, performing independent modal analysis based on the reduced substructure to extract a thermal expansion coefficient matrix and a vibration modal matrix, and generating a homogeneous transformation matrix.

[0100] Specifically, in this embodiment, the steps of reducing the order of the hypergraph model by the super-element modal synthesis method, performing independent modal analysis based on the reduced substructure to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generating the homogeneous transformation matrix specifically include:

[0101] Dividing each entity corresponding to the dynamic graph structure node into a plurality of substructures, performing independent modal analysis on each substructure, and obtaining a local thermal strain modal matrix and a local vibration modal matrix of the substructure;

[0102] Based on the interface degrees of freedom between each substructure, the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure are coupled to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model;

[0103] The thermal deformation error and the vibration displacement error are quantified according to the thermal expansion coefficient matrix and the vibration mode matrix respectively to generate a homogeneous transformation matrix.

[0104] Preferably, in this embodiment, based on the interface degrees of freedom between the substructures, the steps of coupling the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model specifically include:

[0105] The transformation displacement vector that expands the local mode to the global coordinates is calculated based on the internal degrees of freedom of the substructure and the interface degrees of freedom between the substructures according to the following calculation formula:

[0106] ;

[0107] ;

[0108] In the formula, is the transformation displacement vector, For fixed interface mode, is the constrained mode, is the unit mapping matrix of the interface degrees of freedom, is the internal degree of freedom of the substructure, is the interface degree of freedom between each substructure, are the modal coordinates of the substructure, is the modal matrix of the substructure;

[0109] A transformation matrix is ​​constructed based on the transformation displacement vector, so as to couple the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure based on the transformation matrix to obtain a global thermal expansion coefficient matrix and a vibration modal matrix.

[0110] Preferably, in this embodiment, the step of quantifying the thermal deformation error and the vibration displacement error according to the thermal expansion coefficient matrix and the vibration mode matrix to generate a homogeneous transformation matrix specifically includes:

[0111] The thermal deformation error is calculated based on the thermal expansion coefficient matrix according to the following calculation formula:

[0112] ;

[0113] In the formula, is the thermal deformation error, is the thermal expansion coefficient matrix, is the thermal strain driving vector, is the thermal load vector, K is the stiffness matrix;

[0114] The thermal deformation error is calculated based on the vibration mode matrix according to the following calculation formula:

[0115] ;

[0116] In the formula, is the vibration displacement error, is the vibration mode matrix, are the modal coordinates, is the external excitation, M is the mass matrix, C is the damping matrix, j is an imaginary number, is the angular frequency of the excitation force;

[0117] According to the following calculation formula, the coupling strength of thermal deformation error and vibration displacement error is integrated through weighted least squares optimization method, and regularization constraints are performed according to the weight of the hyperedge to obtain the homogeneous transformation matrix:

[0118] ;

[0119] Where C is the homogeneous transformation matrix, represents the error amount of the dynamic graph structure edge (depending on the physical field of the dynamic graph structure edge), is the regularization coefficient of the hyperedge.

[0120] Step S500, adjusting tool parameters by obtaining posture error parameters and translation error parameters based on the homogeneous transformation matrix.

[0121] Specifically, in this embodiment, the step of obtaining the posture error parameter and the translation error parameter based on the homogeneous transformation matrix to adjust the tool parameters specifically includes:

[0122] Correcting the original tool path through the homogeneous transformation matrix to obtain a compensation path;

[0123] An attitude error parameter and a translation error parameter are extracted from the homogeneous transformation matrix, a feedforward control quantity is generated according to the attitude error parameter, and a PID proportional gain is dynamically adjusted according to the norm of the translation error parameter.

[0124] For ease of understanding, the expression of the above homogeneous transformation matrix is:

[0125] ;

[0126] In the formula, is the translation error parameter, and R is the attitude error parameter.

[0127] In this embodiment, the translation error parameters and posture error parameters in the homogeneous transformation matrix are used to correct the tool path in advance to complete feedforward compensation; then the actual position of the tool is measured in real time by the encoder, the error amount between the correction path position and the actual position of the tool is calculated, and the feed speed correction amount is generated according to the error amount through PID adjustment. At the same time, the cutting force fluctuation is detected by the vibration sensor, and the spindle speed or angle is adjusted in real time to suppress high-frequency disturbances; the proportional gain is adjusted in real time according to the norm of the translation error parameter. By increasing the proportional gain, rapid convergence can be achieved, and reducing the proportional gain can avoid overshoot.

[0128] In summary, the intelligent processing control method for milling machines in the above embodiments of the present invention solves the problem of mismatch between static parameters and dynamic working conditions in traditional modeling by modeling tools, fixtures and workpieces as dynamic graph structure nodes and integrating multi-source sensor data such as temperature, vibration and fiber grating. A multi-physical field hypergraph model is constructed by connecting hypernodes through hyperedges, and the coupling strength of thermal fields, stress fields and vibration fields is quantified with dynamic weights. The phase coupling effect of thermal expansion and vibration acceleration is captured to adjust tool compensation parameters and improve processing accuracy.

[0129] Embodiment 2

[0130] The second embodiment of the present application also provides a milling machine intelligent processing control system, which is used to implement the embodiments and preferred implementations, and will not be repeated hereafter. As used below, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware of a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0131] like Figure 2 As shown, the system includes: a data module 100 , an update module 200 , a coupling module 300 , a compensation module 400 and an adjustment module 500 .

[0132] The data module 100 is used to model the tool, fixture and workpiece entities in the machine tool processing environment as dynamic graph structure nodes, fuse the collected data of the temperature sensor, vibration sensor and fiber grating to generate data nodes, and define the data change relationship between the data nodes as the dynamic graph structure edge;

[0133] An updating module 200, configured to update the weights of the edges of the dynamic graph structure based on the feature changes of the data nodes;

[0134] A coupling module 300 is used to use each of the data nodes as a supernode, connect multiple supernodes to form a hyperedge that characterizes the interactive relationship of the thermal-mechanical-vibration physical field, so as to establish a hypergraph model of multiple physical fields, and obtain a dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field;

[0135] The compensation module 400 is used to reduce the order of the hypergraph model by a super-element modal synthesis method, perform independent modal analysis based on the reduced substructure to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generate a homogeneous transformation matrix;

[0136] The adjustment module 500 is used to adjust the tool parameters by obtaining the posture error parameters and the translation error parameters based on the homogeneous transformation matrix.

[0137] Preferably, in this embodiment, the updating module 200 is specifically used for:

[0138] Based on the temperature sensor, the temperature difference between the tool cutting area and the workpiece processing area is obtained in real time, and based on the vibration sensor, the vibration acceleration value of the fixture fixing area is obtained in real time;

[0139] The weights of the edges of the dynamic graph structure are updated according to the temperature difference value and the vibration acceleration value.

[0140] Preferably, in this embodiment, the coupling module 300 is specifically used to obtain the dynamic weight of the hyperedge based on the weight of the dynamic graph structure edge according to the following calculation formula:

[0141] ;

[0142] In the formula, is the weight of the hyperedge H, is the physical field strength at node v, is the contribution weight of node v, is the geometric mean of all dynamic graph structure edges within the hyperedge, is the node’s collaborative adjustment parameter, is the rate of change of the total strength of the node, Adjust parameters for node change trends.

[0143] Preferably, in this embodiment, the compensation module 400 is specifically used for:

[0144] Dividing each entity corresponding to the dynamic graph structure node into a plurality of substructures, performing independent modal analysis on each substructure, and obtaining a local thermal strain modal matrix and a local vibration modal matrix of the substructure;

[0145] Based on the interface degrees of freedom between each substructure, the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure are coupled to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model;

[0146] The thermal deformation error and the vibration displacement error are quantified according to the thermal expansion coefficient matrix and the vibration mode matrix respectively to generate a homogeneous transformation matrix.

[0147] Preferably, in this embodiment, the compensation module 400 is further used to calculate the transformation displacement vector that expands the local mode into the global coordinate based on the internal degrees of freedom of the substructure and the interface degrees of freedom between the substructures according to the following calculation formula:

[0148] ;

[0149] ;

[0150] In the formula, is the transformation displacement vector, For fixed interface mode, is the constrained mode, is the unit mapping matrix of the interface degrees of freedom, is the internal degree of freedom of the substructure, is the interface degree of freedom between substructures, are the modal coordinates of the substructure, is the modal matrix of the substructure;

[0151] A transformation matrix is ​​constructed based on the transformation displacement vector, so as to couple the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure based on the transformation matrix to obtain a global thermal expansion coefficient matrix and a vibration modal matrix.

[0152] Preferably, in this embodiment, the compensation module 400 is further used to calculate the thermal deformation error based on the thermal expansion coefficient matrix according to the following calculation formula:

[0153] ;

[0154] In the formula, is the thermal deformation error, is the thermal expansion coefficient matrix, is the thermal strain driving vector, is the thermal load vector, K is the stiffness matrix;

[0155] The thermal deformation error is calculated based on the vibration mode matrix according to the following calculation formula:

[0156] ;

[0157] In the formula, is the vibration displacement error, is the vibration mode matrix, are the modal coordinates, is the external excitation, M is the mass matrix, C is the damping matrix, j is an imaginary number, is the angular frequency of the excitation force;

[0158] According to the following calculation formula, the coupling strength of thermal deformation error and vibration displacement error is integrated through weighted least squares optimization method, and regularization constraints are performed according to the weight of the hyperedge to obtain the homogeneous transformation matrix:

[0159] ;

[0160] Where C is the homogeneous transformation matrix, Represents the error amount of the dynamic graph structure edge, is the regularization coefficient of the hyperedge.

[0161] Preferably, in this embodiment, the adjustment module 500 is specifically used to: modify the original tool path through the homogeneous transformation matrix to obtain a compensation path;

[0162] An attitude error parameter and a translation error parameter are extracted from the homogeneous transformation matrix, a feedforward control quantity is generated according to the attitude error parameter, and a PID proportional gain is dynamically adjusted according to the norm of the translation error parameter.

[0163] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the 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.

[0164] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A milling machine intelligent processing control method, characterized in that: The following steps are involved: The tool, fixture and workpiece entities in the machine tool processing environment are modeled as dynamic graph structure nodes, the data collected by the temperature sensor, vibration sensor and fiber grating are integrated into the dynamic graph structure nodes to generate data nodes, and the data change relationship between the data nodes is defined as the dynamic graph structure edge; Based on the characteristic changes of the data nodes, the weights of the edges of the dynamic graph structure are updated; Each of the data nodes is used as a supernode, and multiple supernodes are connected to form a hyperedge that represents the interactive relationship of the thermal-mechanical-vibration physical field to establish a hypergraph model of multiple physical fields, and the dynamic weight of the hyperedge is obtained based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field; The hypergraph model is reduced in order by using the superelement modal synthesis method, and independent modal analysis is performed based on the reduced substructure to extract the thermal expansion coefficient matrix and vibration modal matrix, and generate a homogeneous transformation matrix. The tool parameters are adjusted by obtaining posture error parameters and translation error parameters based on the homogeneous transformation matrix.

2. The intelligent processing control method for a milling machine according to claim 1, characterized in that: The step of updating the weight of the edge of the dynamic graph structure specifically includes: Based on the temperature sensor, the temperature difference between the tool cutting area and the workpiece processing area is obtained in real time, and based on the vibration sensor, the vibration acceleration value of the fixture fixing area is obtained in real time; The weights of the edges of the dynamic graph structure are updated according to the temperature difference value and the vibration acceleration value.

3. The intelligent processing control method for a milling machine according to claim 2, characterized in that: The calculation expression for updating the weight of the dynamic graph structure edge according to the temperature difference value and the vibration acceleration value is: ； In the formula, is the temperature difference, is the temperature attenuation coefficient, is the temperature error weight coefficient, , is the vibration acceleration value of the m and n nodes on the fixture fixing area, is the vibration error weight coefficient, is the vibration suppression coefficient, is the strain error between temperature nodes, is the strain error weight coefficient, is the coupling strength coefficient.

4. The intelligent processing control method for a milling machine according to claim 3 is characterized in that: The step of obtaining the dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field specifically includes: The dynamic weight of the hyperedge is obtained based on the weight of the edge of the dynamic graph structure according to the following calculation formula: ； In the formula, is the weight of the hyperedge H, is the physical field strength at node v, is the contribution weight of node v, is the geometric mean of all dynamic graph structure edges within the hyperedge, is the node’s collaborative adjustment parameter, is the rate of change of the total strength of the node, Adjust parameters for the changing trend of nodes.

5. The intelligent processing control method for a milling machine according to claim 4, characterized in that: The steps of reducing the order of the hypergraph model by the superelement modal synthesis method, performing independent modal analysis based on the reduced substructure to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generating the homogeneous transformation matrix specifically include: Dividing each entity corresponding to the dynamic graph structure node into a plurality of substructures, performing independent modal analysis on each substructure, and obtaining a local thermal strain modal matrix and a local vibration modal matrix of the substructure; Based on the interface degrees of freedom between each substructure, the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure are coupled to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model; The thermal deformation error and the vibration displacement error are quantified according to the thermal expansion coefficient matrix and the vibration mode matrix respectively to generate a homogeneous transformation matrix.

6. The intelligent processing control method for a milling machine according to claim 5, characterized in that: Based on the interface degrees of freedom between the substructures, the steps of coupling the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure to obtain the thermal expansion coefficient matrix and the vibration modal matrix corresponding to the global model specifically include: The transformation displacement vector that expands the local mode to the global coordinates is calculated based on the internal degrees of freedom of the substructure and the interface degrees of freedom between the substructures according to the following calculation formula: ； ； In the formula, is the transformation displacement vector, For fixed interface mode, is the constrained mode, is the unit mapping matrix of the interface degrees of freedom, is the internal degree of freedom of the substructure, is the interface degree of freedom between each substructure, are the modal coordinates of the substructure, is the modal matrix of the substructure; A transformation matrix is ​​constructed based on the transformation displacement vector, so as to couple the local thermal strain modal matrix and the local vibration modal matrix corresponding to each substructure based on the transformation matrix to obtain a global thermal expansion coefficient matrix and a vibration modal matrix.

7. The intelligent processing control method for a milling machine according to claim 5, characterized in that: The steps of quantifying the thermal deformation error and the vibration displacement error according to the thermal expansion coefficient matrix and the vibration mode matrix to generate a homogeneous transformation matrix specifically include: The thermal deformation error is calculated based on the thermal expansion coefficient matrix according to the following calculation formula: ； In the formula, is the thermal deformation error, is the thermal expansion coefficient matrix, is the thermal strain driving vector, is the thermal load vector, K is the stiffness matrix; The thermal deformation error is calculated based on the vibration mode matrix according to the following calculation formula: ； In the formula, is the vibration displacement error, is the vibration mode matrix, are the modal coordinates, is the external excitation, M is the mass matrix, C is the damping matrix, j is an imaginary number, is the angular frequency of the excitation force; According to the following calculation formula, the coupling strength of thermal deformation error and vibration displacement error is integrated through weighted least squares optimization method, and regularization constraints are performed according to the weight of the hyperedge to obtain the homogeneous transformation matrix: ； Where C is the homogeneous transformation matrix, Represents the error amount of the dynamic graph structure edge, is the regularization coefficient of the hyperedge.

8. The intelligent processing control method for a milling machine according to claim 1, characterized in that: The step of obtaining the posture error parameter and the translation error parameter based on the homogeneous transformation matrix to adjust the tool parameters specifically includes: Correcting the original tool path through the homogeneous transformation matrix to obtain a compensation path; An attitude error parameter and a translation error parameter are extracted from the homogeneous transformation matrix, a feedforward control quantity is generated according to the attitude error parameter, and a PID proportional gain is dynamically adjusted according to the norm of the translation error parameter.

9. An intelligent processing control system for a milling machine, characterized in that: include: A data module is used to model the tool, fixture and workpiece entities in the machine tool processing environment as dynamic graph structure nodes, fuse the dynamic graph structure nodes with the collected data of the temperature sensor, vibration sensor and fiber grating to generate data nodes, and define the data change relationship between the data nodes as a dynamic graph structure edge; An updating module, used to update the weights of the edges of the dynamic graph structure based on the feature changes of the data nodes; A coupling module, used to use each of the data nodes as a supernode, connect multiple supernodes to form a hyperedge that characterizes the interactive relationship of the thermal-mechanical-vibration physical field, so as to establish a hypergraph model of multiple physical fields, and obtain a dynamic weight of the hyperedge based on the weight of each of the dynamic graph structure edges to quantify the coupling strength of the thermal-mechanical-vibration physical field; A compensation module is used to reduce the order of the hypergraph model by using the super-element modal synthesis method, perform independent modal analysis based on the reduced substructure to extract the thermal expansion coefficient matrix and the vibration modal matrix, and generate a homogeneous transformation matrix; The adjustment module is used to adjust the tool parameters by obtaining the posture error parameters and the translation error parameters based on the homogeneous transformation matrix.

10. The intelligent processing control system for milling machine according to claim 9, characterized in that: The update module is specifically used for: Based on the temperature sensor, the temperature difference between the tool cutting area and the workpiece processing area is obtained in real time, and based on the vibration sensor, the vibration acceleration value of the fixture fixing area is obtained in real time; The weights of the edges of the dynamic graph structure are updated according to the temperature difference value and the vibration acceleration value.

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