Intelligent manufacturing and processing precision improving method and system based on graph optimization

Abstract

The invention discloses an intelligent manufacturing machining precision improving method and system based on graph optimization. The method comprises the steps that a machining chain from a workpiece to a tool in an intelligent manufacturing unit is established; the absolute position relation of all devices in the intelligent manufacturing unit is calibrated according to the serial sequence of the machining chains, and an ideal pose model of the tool is established; the motion precision of equipment on the machining chain is calibrated, the motion precision is introduced into the ideal pose model of the tool, and an actual pose model of the tool is established; a machining precision model is obtained according to the ideal pose model of the tool and the actual pose model of the tool; based on the machining precision model, the machining precision of the workpiece at different clamping positions is calculated, and a graph optimization model is established; and selecting a processing path which meets the processing precision requirement and consumes the shortest time based on the graph optimization model. The machining precision can be improved, the machining quality is guaranteed, and meanwhile higher production efficiency is achieved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology, specifically relating to a method and system for improving the processing accuracy of intelligent manufacturing based on graph optimization. Background Technology

[0002] Robotic intelligent manufacturing cells are widely used in various processing, assembly, and handling tasks. Among these, robotic milling and grinding processes require very high machining accuracy from the cells, as this directly affects product quality. However, most existing robotic manufacturing systems struggle to accurately control machining precision when faced with complex motion paths, collaborative work of multiple components, and dynamic environments.

[0003] Because a robotic intelligent manufacturing unit consists of multiple devices, the overall system's processing accuracy is affected not only by the motion accuracy of each device but also by the assembly accuracy of the components. Assembly errors, transmission errors, and joint clearances between components accumulate within the system, resulting in a complex and difficult-to-accurate accuracy model. During production, a single robotic intelligent manufacturing unit cannot simultaneously meet both the workpiece's accuracy requirements and the production cycle time; improving accuracy may increase costs and reduce production efficiency.

[0004] Furthermore, the machining accuracy of robotic intelligent manufacturing units is difficult to determine in the early stages of design. Due to the complex working environment, numerous unit components, and the difficulty in controlling component accuracy, the accuracy after unit production often deviates significantly from the design goals. Therefore, how to improve production efficiency and determine the machining accuracy at any point within the entire workspace while meeting accuracy requirements has become an urgent issue.

[0005] Chinese invention patent CN102785128B discloses an online detection system and method for machining accuracy of parts on CNC lathes. The detection system includes a chuck, a workpiece, and a lathe probe. The workpiece is mounted on the chuck, and the lathe probe is mounted beside the workpiece. The signal output terminal of the lathe probe is connected to the controller of the detection system. The detection method includes the following steps: 1) Installing the lathe probe beside the workpiece; 2) Calibrating the lathe probe; 3) Planning the measurement path and generating codes for basic geometric parts and curved surface parts; 4) The controller of the detection system compensates for the thermal deformation temperature error of the part and performs machining error analysis. This invention allows for automatic measurement of the dimensions and positional accuracy of the machined part without removing it from the machine tool after machining. The cutting tool is replaced with a contact probe, and the detection path of the probe is automatically planned based on the geometric contour of the workpiece and the detection items. The detection code is generated and driven through a communication interface with the CNC lathe, achieving automatic measurement of the dimensions and positional accuracy of the machined part. However, this invention lacks measurement methods for factors other than thermal deformation during workpiece processing, and cannot detect the accuracy of the equipment itself; measurement requires changing the processing tool and replacing it with a contact probe, which increases the difficulty of use, and the installation error introduced during the replacement process is not calculated; measurement at the same station after production increases the cost of use and reduces processing efficiency.

[0006] Chinese patent application CN113704933A discloses a comprehensive spatial machining error modeling method for CNC cylindrical grinding machines based on differential motion relationships between coordinate systems. This method is based on the differential motion theory between adjacent coordinate systems, combined with robot forward kinematics theory and previous low-order body theory of multi-body systems, to construct the forward motion topology of the CNC cylindrical grinding machine. It obtains the homogeneous transformation matrix between adjacent bodies of each moving component; through this homogeneous matrix, it obtains the homogeneous transformation matrix of the tool relative to the coordinate systems of each moving component; by performing geometric error term analysis on the CNC cylindrical grinding machine, it obtains the differential motion vectors of the geometric errors of each motion axis, and then derives the expressions for the differential motion vectors of the geometric errors of the translational and rotary axes. This invention solves the problem of complex calculations in previous spatial error models and has the advantages of simple and fast calculation, flexible application, and innovation. However, this invention application only performs precision analysis on the motion of a single cylindrical grinding machine, and is limited to the calculation of dimensional errors between parts of a single device. It has limited applicability and is not suitable for robotic intelligent manufacturing units with large spatial distribution, many components, and long processing chains. At the same time, this solution only provides a method for calculating spatial errors and cannot guide the equipment in production on how to meet the production requirements of higher precision workpieces and improve efficiency. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for improving the processing accuracy of intelligent manufacturing based on graph optimization, which can achieve higher production efficiency while improving processing accuracy and ensuring processing quality.

[0008] This invention provides the following technical solution:

[0009] Firstly, a method for improving the processing accuracy of intelligent manufacturing based on graph optimization is provided, including:

[0010] Establish a processing chain from workpiece to cutting tool in intelligent manufacturing units;

[0011] The absolute positional relationship of each device in the intelligent manufacturing unit is determined according to the serial order of the processing chain, and an ideal pose model of the cutting tool is established.

[0012] The motion accuracy of the equipment on the machining chain is calibrated, and the motion accuracy is incorporated into the ideal pose model of the tool to establish the actual pose model of the tool.

[0013] The machining accuracy model is obtained based on the ideal pose model and the actual pose model of the tool.

[0014] Based on the machining accuracy model, the machining accuracy of the workpiece at different clamping positions is calculated, and a graphical optimization model is established.

[0015] The graph optimization model is used to select the machining path that meets the machining accuracy requirements and has the shortest processing time.

[0016] Furthermore, the step of determining the absolute positional relationship of each device in the intelligent manufacturing unit according to the serial order of the processing chain includes:

[0017] The intelligent manufacturing unit is abstracted as a multi-body motion system, and a coordinate system is established for each device;

[0018] The relationship between the coordinate systems of each device is calibrated according to the serial order of the processing chain, and the calibrated positional relationship is abstracted into a relative coordinate system relationship.

[0019] Furthermore, the ideal pose model of the cutting tool is represented as follows:

[0020] ;

[0021] Among them, T ideal For the ideal position of the cutting tool, T1, T2, T3, T4...T i These represent the pose relationships of the 1st, 2nd, 3rd, 4th...ith device coordinate systems in the machining chain relative to the previous device coordinate system;

[0022] If the changes in the six-dimensional attitude of the i-th device coordinate system relative to the previous device coordinate system are respectively Then Ti Represented as:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] Where x, y, z represent the spatial displacement of the i-th device coordinate system relative to the previous device coordinate system; R represents the rotation angle of the i-th device coordinate system relative to the previous device coordinate system around the x, y, z axes; x (α), R y (β) 、R z (γ) represents the rotation matrix about the x, y, and z axes, respectively.

[0028] Furthermore, the method for calibrating the motion accuracy of the equipment on the machining chain includes:

[0029] The accuracy of a single device is represented by a six-dimensional attitude variation feature matrix. If the motion accuracy of the end-effector relative to its base coordinate system in the six-dimensional attitude of the intelligent manufacturing unit is... Then its homogeneous matrix of motion accuracy Represented as:

[0030] ;

[0031] The motion accuracy of equipment in the intelligent manufacturing unit is determined according to the processing chain sequence and expressed in the form of a six-dimensional attitude change feature matrix. Then, the motion accuracy of the i-th equipment is... Represented as ,in, These represent the dynamic accuracy of the six postures.

[0032] Furthermore, the actual pose model of the cutting tool is represented as follows:

[0033] ;

[0034] Among them, T real The actual position of the tool, T1, T2, T3, T4...T i These represent the pose relationships of the 1st, 2nd, 3rd, 4th...ith device coordinate systems in the machining chain relative to the previous device coordinate system. These represent the motion accuracy of the 1st, 2nd, 3rd, 4th...ith devices, respectively.

[0035] Furthermore, the machining accuracy model is expressed as:

[0036] ;

[0037] Among them, P error For machining accuracy, T real T represents the actual position of the cutting tool. ideal This is the ideal position for the cutting tool.

[0038] Furthermore, the method for establishing the graph optimization model includes:

[0039] Initialize the processing space editing graph, create nodes and edges, then the edge weight W is represented as:

[0040] ;

[0041] Where t is the movement time from one clamping position to another. To account for the change in machining accuracy from one clamping position to another, Time weight is used for time calculations, and precision weight is used for accuracy calculations.

[0042] By combining time and precision using a weighted approach, the objective function is obtained:

[0043] ;

[0044] Among them, W i, j t represents the edge weight of path ij. i, j This represents the time taken for path ij. This indicates the change in machining accuracy for path ij.

[0045] Furthermore, the graph-based optimization model selects the machining path that meets the machining accuracy requirements and has the shortest processing time, including:

[0046] Using an optimization algorithm based on Dijkstra's algorithm, the distance from all nodes to the starting point is initialized to positive infinity, and the starting point is set to 0;

[0047] At each step, select the node u with the smallest distance among the unvisited nodes, and update the distance between node u and its neighboring node v:

[0048] ;

[0049] Where d[v] represents the minimum total cost from the starting point to node u in the current record, which is an estimate that is continuously updated during the search process to approach the optimal value; d[u] represents the total cost from the starting point to the current node u, W u, v Represents the edge weights of path uv;

[0050] Choose the path with the smaller total cost from the two possible paths, that is, choose the optimal path from the currently known path to node 𝑣;

[0051] If the machining accuracy P of node v v Less than the minimum accuracy requirement P min If so, then skip updating d[v].

[0052] Furthermore, it also includes:

[0053] During processing, the processing accuracy and time data of the equipment are acquired in real time, and the graph optimization model is dynamically updated; when the equipment status changes, the edge weights W are recalculated; and the time weights are adjusted according to actual needs. And precision weight μ; if time priority is given, then increase the time weight. If precision is the priority, the precision weight μ is increased to improve machining accuracy.

[0054] Secondly, a graph optimization-based intelligent manufacturing process accuracy improvement system is provided, comprising:

[0055] The machining chain establishment module is used to establish the machining chain from workpiece to tool in the intelligent manufacturing unit;

[0056] The ideal pose model establishment module is used to calibrate the absolute positional relationship of each device in the intelligent manufacturing unit according to the serial order of the machining chain, and to establish the ideal pose model of the cutting tool.

[0057] The actual pose model establishment module is used to calibrate the motion accuracy of the equipment on the machining chain and introduce the motion accuracy into the ideal pose model of the tool to establish the actual pose model of the tool.

[0058] The machining accuracy model building module is used to obtain the machining accuracy model based on the ideal pose model and the actual pose model of the tool.

[0059] The graph optimization model building module is used to calculate the machining accuracy of the workpiece at different clamping positions based on the machining accuracy model, and to build a graph optimization model.

[0060] The path selection module is used to select the machining path that meets the machining accuracy requirements and has the shortest processing time based on the graph optimization model.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] (1) This invention abstracts the intelligent manufacturing unit into a multi-body motion system. By establishing the ideal pose model of the tool and the actual pose model of the tool, the machining accuracy model is calculated, thereby enabling the calculation of the machining accuracy of any point in the workspace of the intelligent manufacturing unit, which helps to improve the machining accuracy of the workpiece.

[0063] (2) Based on the machining accuracy model, this invention calculates the machining accuracy of the workpiece at different clamping positions and establishes a graph optimization model. Based on the graph optimization model, a machining path that meets the accuracy requirements and has the fastest production cycle can be combined, thereby improving machining accuracy and ensuring machining quality while achieving higher production efficiency.

[0064] (3) By adjusting the clamping position, the present invention can meet the processing of workpieces with different precision requirements, enabling a single intelligent manufacturing unit to complete the processing of more types of workpieces, reducing the idle time of the unit, reducing the procurement needs of new equipment, and has the advantages of cost reduction and efficiency improvement.

[0065] (4) This invention establishes a machining chain from workpiece to tool in an intelligent manufacturing unit, calibrates the absolute positional relationship of each device in the intelligent manufacturing unit according to the serial order of the machining chain, and establishes an ideal pose model of the tool. By calibrating the motion accuracy of the devices on the machining chain and introducing the motion accuracy into the ideal pose model of the tool, the actual pose model of the tool is established, and thus the machining accuracy model is obtained. It is not only applicable to robot intelligent manufacturing units, but also to intelligent detection and intelligent manufacturing units of other multi-motion systems or similar multi-motion systems, and has high versatility. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating the intelligent manufacturing processing accuracy improvement method based on graph optimization in Embodiment 1 of the present invention.

[0067] Figure 2 This is a schematic diagram of the robot intelligent manufacturing unit abstracted as a multi-body motion system in Embodiment 2 of the present invention;

[0068] Figure 3 This is a schematic diagram of the relative coordinate system of the multibody motion system of the robot intelligent manufacturing unit in Embodiment 3 of the present invention. Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0070] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0071] Example 1

[0072] like Figure 1 As shown in the figure, this embodiment provides a method for improving the processing accuracy of intelligent manufacturing based on graph optimization, and the steps are as follows:

[0073] Step 1: Establish the machining chain from workpiece to cutting tool in the intelligent manufacturing unit.

[0074] Step 2: According to the serial order of the processing chain, calibrate the absolute positional relationship of each device in the intelligent manufacturing unit, and establish the ideal pose model of the cutting tool.

[0075] Step 2.1: Abstract the intelligent manufacturing unit into a multi-body motion system and establish a coordinate system for each device.

[0076] Step 2.2: According to the serial order of the processing chain, calibrate the relationship between the coordinate systems of each device, and abstract the calibrated positional relationship into a relative coordinate system relationship.

[0077] Step 2.3: Using the homogeneous eigenvalue matrix to describe the pose and motion transformation between adjacent coordinate systems, the ideal pose model of the tool is represented as:

[0078] ;

[0079] Among them, T ideal For the ideal position of the cutting tool, T1, T2, T3, T4...T i These represent the pose relationships of the 1st, 2nd, 3rd, 4th...ith device coordinate systems in the machining chain relative to the previous device coordinate system;

[0080] If the changes in the six-dimensional attitude of the i-th device coordinate system relative to the previous device coordinate system are respectively Then T i Represented as:

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] Where x, y, z represent the spatial displacement of the i-th device coordinate system relative to the previous device coordinate system; R represents the rotation angle of the i-th device coordinate system relative to the previous device coordinate system around the x, y, z axes; x (α), R y (β) 、R z (γ) represents the rotation matrix about the x, y, and z axes, respectively.

[0086] Step 3: Calibrate the motion accuracy of the equipment on the machining chain, and incorporate the motion accuracy into the ideal pose model of the tool to establish the actual pose model of the tool.

[0087] Step 3.1: Use the six-dimensional attitude variation feature matrix to represent the accuracy of a single device. If the motion accuracy of the end tool relative to its base coordinate system in the six-dimensional attitude of the intelligent manufacturing unit is... Then its homogeneous matrix of motion accuracy Represented as:

[0088] .

[0089] Step 3.2: calibrate the motion accuracy of the equipment in the intelligent manufacturing unit according to the processing chain sequence, and represent it in the form of a six-dimensional attitude change feature matrix. Then, the motion accuracy of the i-th equipment is... Represented as ,in, These represent the dynamic accuracy of the six postures.

[0090] Step 3.3: Incorporate the motion accuracy of each device from Step 3.2 into the ideal pose model of the tool according to the machining chain sequence, and establish the actual pose model of the tool, as shown below:

[0091] ;

[0092] Among them, T real The actual position of the tool, T1, T2, T3, T4...T i These represent the pose relationships of the 1st, 2nd, 3rd, 4th...ith device coordinate systems in the machining chain relative to the previous device coordinate system. These represent the motion accuracy of the 1st, 2nd, 3rd, 4th...ith devices, respectively.

[0093] Step 4: Obtain the machining accuracy model based on the ideal pose model and the actual pose model of the tool.

[0094] Specifically, the cutting tool is the final execution tool for machining the workpiece. The pose and error between the tool and the workpiece determine the machining accuracy of the entire intelligent manufacturing unit. Each component of the intelligent manufacturing unit affects the pose and error between the tool and the workpiece. Therefore, the accuracy of the unit at a certain point is composed of the final pose and error of the tool at that point. By establishing an accuracy model that combines the absolute ideal position with the accuracy error, and representing the actual position at a certain point in the form of "endpoint pose + six-dimensional error components," the machining accuracy at that point can be obtained by subtracting the ideal pose from the actual pose. Thus, the machining accuracy model is expressed as:

[0095] ;

[0096] Among them, Perror For machining accuracy, T real T represents the actual position of the cutting tool. ideal This is the ideal position for the cutting tool.

[0097] Step 5: Based on the machining accuracy model, calculate the machining accuracy of the workpiece at different clamping positions and establish a graphical optimization model.

[0098] Step 5.1: When processing a specific workpiece in the processing space of the robot intelligent manufacturing unit, the coordinates of the point to be processed in the world coordinate system are different when the workpiece is clamped in different positions of the intelligent manufacturing unit. Therefore, the processing accuracy of different clamping positions is also different. The processing accuracy of the workpiece to be processed at all clamping positions is statistically analyzed through the processing accuracy model in Step 4.

[0099] Step 5.2: Initialize the machining space editing diagram, creating nodes and edges. Nodes represent clamping positions or key points in the trajectory within the machining space, with attributes including coordinates, accuracy error range, and required time. Edges represent the transformation relationship between two nodes. The edge weight W is then expressed as:

[0100] ;

[0101] Where t is the movement time from one clamping position to another. To account for the change in machining accuracy from one clamping position to another, is the time weight, and μ is the precision weight.

[0102] Step 5.3: By combining time and accuracy in a weighted manner, the objective function, i.e., the graph optimization model, is obtained:

[0103] ;

[0104] Among them, W i, j t represents the edge weight of path ij. i, j This represents the time taken for path ij. This indicates the change in machining accuracy for path ij.

[0105] Step 6: Select the machining path that meets the machining accuracy requirements and has the shortest processing time based on the graph optimization model.

[0106] Step 6.1: Using an optimization algorithm based on Dijkstra's algorithm, initialize the distance from all nodes to the starting point to positive infinity, and set the starting point to 0.

[0107] Step 6.2: In each step, select the node u with the smallest distance among the unvisited nodes, and update the distance between node u and its neighboring node v:

[0108] ;

[0109] Where d[v] represents the minimum total cost from the starting point to node u in the current record, which is an estimate that is continuously updated during the search process to approach the optimal value; d[u] represents the total cost from the starting point to the current node u, W u, v Represents the edge weights of path uv;

[0110] Choose the path with the smaller total cost from the two possible paths, that is, choose the optimal path from the currently known path to node 𝑣;

[0111] If the machining accuracy P of node v v Less than the minimum accuracy requirement P min If so, then skip updating d[v].

[0112] Step 6.3: Finally, find a path that meets the accuracy requirements while minimizing the time consumption, and output the optimal processing path, processing accuracy, and processing time.

[0113] During processing, the processing accuracy and time data of the equipment are acquired in real time, and the graph optimization model is dynamically updated; when the equipment status changes, the edge weights W are recalculated; and the time weights are adjusted according to actual needs. And precision weight μ; if time priority is given, then increase the time weight. This reduces processing time; if accuracy is prioritized, the accuracy weight μ is increased to improve processing accuracy. This achieves multi-objective optimization of the processing path.

[0114] Example 2

[0115] by Figure 2 Taking the robot intelligent manufacturing unit shown as an example, the graph optimization-based intelligent manufacturing processing accuracy improvement method of Embodiment 1 is used to select the processing path that meets the processing accuracy requirements and has the shortest processing time.

[0116] The intelligent manufacturing unit includes: workpiece, positioner, camera, robot, ground rail, and cutting tool. The processing chain from workpiece to cutting tool in the intelligent manufacturing unit is established as: workpiece-positioner-workpiece-camera-robot-ground rail-robot-cutting tool.

[0117] like Figure 2 As shown, the intelligent manufacturing unit is abstracted as a multi-body motion system, and a coordinate system is established for each device. In the figure, {T} is the tool coordinate system, {C} is the camera coordinate system, {B} is the robot coordinate system, {M} is the ground track coordinate system, {P} is the positioner coordinate system, and {G} is the workpiece coordinate system.

[0118] The relationship between the coordinate systems of each device is calibrated according to the serial order of the processing chain. The calibrated positional relationships are then abstracted into relative coordinate system relationships, as shown in the diagram below. Figure 3 As shown.

[0119] The ideal position of the cutting tool is as follows:

[0120] .

[0121] The actual position of the cutting tool is as follows:

[0122] .

[0123] Assume the motion accuracy of each component device in the intelligent manufacturing unit is as follows:

[0124] 3D camera calibration error: (0.1, 0.1, 0.1, 0.02, 0.02, 0.02);

[0125] Robot repeatability error: (0.05, 0.05, 0.05, 0, 0, 0);

[0126] Tool error: (0, 0, 0.05, 0, 0, 0);

[0127] Camera scanning error: (0.05, 0.05, 0.05, 0, 0, 0);

[0128] Repeat positioning error of the positioner: (0, 0, 0, 0.05, 0, 0);

[0129] Ground track repeatability error: (0.05, 0, 0, 0, 0, 0).

[0130] Calculate the six-dimensional accuracy of each point in the workspace. For example, the accuracy at point (0, 0, 700) is (0.173871, 0.0925492, -0.173735).

[0131] Suppose that in a robotic intelligent manufacturing unit, there are three clamping positions A, B, and C, and the target is machining point P, with a required machining accuracy of P. min =0.06, the total time consumption should be minimized; the theoretical accuracy P of each clamping position is known. i Time weights are set for the time t and accuracy change ΔP between the node and its neighboring nodes. =1, precision weight μ=5, path parameters are shown in Table 1 below.

[0132] Table 1 Path Parameters

[0133] The example contains four nodes: A, B, C, and P. Each node's attributes include its position coordinates and the current theoretical accuracy P. iThe edge contains the time t and the accuracy change ΔP between adjacent nodes, and the edge weight W is shown in Table 2 below.

[0134] Table 2 Weights of each path edge

[0135] Set the starting point A, the target point P, and all nodes. , indicating that the initial minimum cost is unknown; set d[A]=0; select nodes according to d[v].

[0136] When v = P is visited, it means that the path from A to P with the minimum processing accuracy and time cost has been found; the optimal path can be determined to be... Total time t total =6 seconds, and can meet the minimum accuracy requirements, wherein:

[0137] A→B: Time taken 3 seconds, accuracy change ΔP=0.02;

[0138] B→C: Time taken 2 seconds, accuracy change ΔP=0.03;

[0139] C→P: Time taken 1 second, accuracy change ΔP=0.01.

[0140] The theoretical accuracy P = 0.06, which meets the minimum requirement P. min =0.06.

[0141] Example 3

[0142] Based on the same inventive concept as Embodiment 1, this embodiment provides a graph optimization-based intelligent manufacturing process accuracy improvement system, including:

[0143] The machining chain establishment module is used to establish the machining chain from workpiece to tool in the intelligent manufacturing unit;

[0144] The ideal pose model establishment module is used to calibrate the absolute positional relationship of each device in the intelligent manufacturing unit according to the serial order of the machining chain, and to establish the ideal pose model of the cutting tool.

[0145] The actual pose model establishment module is used to calibrate the motion accuracy of the equipment on the machining chain and introduce the motion accuracy into the ideal pose model of the tool to establish the actual pose model of the tool.

[0146] The machining accuracy model building module is used to obtain the machining accuracy model based on the ideal pose model and the actual pose model of the tool.

[0147] The graph optimization model building module is used to calculate the machining accuracy of the workpiece at different clamping positions based on the machining accuracy model, and to build a graph optimization model.

[0148] The path selection module is used to select the machining path that meets the machining accuracy requirements and has the shortest processing time based on the graph optimization model.

[0149] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0150] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0154] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for improving the processing accuracy of intelligent manufacturing based on graph optimization, characterized in that, include: Establish a processing chain from workpiece to cutting tool in intelligent manufacturing units; The absolute positional relationship of each device in the intelligent manufacturing unit is determined according to the serial order of the processing chain, and an ideal pose model of the cutting tool is established. The motion accuracy of the equipment on the machining chain is calibrated, and the motion accuracy is incorporated into the ideal pose model of the tool to establish the actual pose model of the tool. The machining accuracy model is obtained based on the ideal pose model and the actual pose model of the tool. Based on the machining accuracy model, the machining accuracy of the workpiece at different clamping positions is calculated, and a graphical optimization model is established. The graph optimization model is used to select the machining path that meets the machining accuracy requirements and has the shortest processing time.

2. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The step of determining the absolute positional relationship of each device in the intelligent manufacturing unit according to the serial order of the processing chain includes: The intelligent manufacturing unit is abstracted as a multi-body motion system, and a coordinate system is established for each device; The relationship between the coordinate systems of each device is calibrated according to the serial order of the processing chain, and the calibrated positional relationship is abstracted into a relative coordinate system relationship.

3. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The ideal pose model of the cutting tool is represented as follows: ； Among them, T ideal For the ideal position of the cutting tool, T1, T2, T3, T4...T i These represent the pose relationships of the 1st, 2nd, 3rd, 4th...ith device coordinate systems in the machining chain relative to the previous device coordinate system; If the changes in the six-dimensional attitude of the i-th device coordinate system relative to the previous device coordinate system are respectively Then T i Represented as: ； ； ； ； Where x, y, z represent the spatial displacement of the i-th device coordinate system relative to the previous device coordinate system; R represents the rotation angle of the i-th device coordinate system relative to the previous device coordinate system around the x, y, z axes; x (α), R y (β) 、R z (γ) represents the rotation matrix about the x, y, and z axes, respectively.

4. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The method for calibrating the motion accuracy of equipment on the machining chain includes: The accuracy of a single device is represented by a six-dimensional attitude variation feature matrix. If the motion accuracy of the end-effector relative to its base coordinate system in the six-dimensional attitude of the intelligent manufacturing unit is... Then its homogeneous matrix of motion accuracy Represented as: ； The motion accuracy of equipment in the intelligent manufacturing unit is determined according to the processing chain sequence and expressed in the form of a six-dimensional attitude change feature matrix. Then, the motion accuracy of the i-th equipment is... Represented as ,in, These represent the dynamic accuracy of the six postures.

5. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The actual pose model of the cutting tool is represented as follows: ； Among them, T real The actual position of the tool, T1, T2, T3, T4...T i These represent the pose relationships of the 1st, 2nd, 3rd, 4th...ith device coordinate systems in the machining chain relative to the previous device coordinate system. These represent the motion accuracy of the 1st, 2nd, 3rd, 4th...ith devices, respectively.

6. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The machining accuracy model is represented as follows: ； Among them, P error For machining accuracy, T real T represents the actual position of the cutting tool. ideal This is the ideal position for the cutting tool.

7. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The method for establishing a graph optimization model includes: Initialize the processing space editing graph, create nodes and edges, then the edge weight W is represented as: ； Where t is the movement time from one clamping position to another. To account for the change in machining accuracy from one clamping position to another, Time weights are used, and precision weights are used. By combining time and precision using a weighted approach, the objective function is obtained: ； Among them, W i, j Let t represent the edge weights of path ij. i, j This represents the time taken for path ij. This indicates the change in machining accuracy for path ij.

8. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, The graph-based optimization model selects the machining path that meets the machining accuracy requirements and has the shortest processing time, including: Using an optimization algorithm based on Dijkstra's algorithm, the distance from all nodes to the starting point is initialized to positive infinity, and the starting point is set to 0; At each step, select the node u with the smallest distance among the unvisited nodes, and update the distance between node u and its neighboring node v: ； Where d[v] represents the minimum total cost from the starting point to node u in the current record, which is an estimate that is continuously updated during the search process to approach the optimal value; d[u] represents the total cost from the starting point to the current node u, W u, v Represents the edge weights of path uv; Choose the path with the smaller total cost from the two possible paths, that is, choose the optimal path from the currently known path to node 𝑣; If the machining accuracy P of node v v Less than the minimum accuracy requirement P min If so, then skip updating d[v].

9. The method for improving intelligent manufacturing processing accuracy based on graph optimization according to claim 1, characterized in that, Also includes: During the processing, the processing accuracy and time data of the equipment are acquired in real time, and the graph optimization model is dynamically updated; when the equipment status changes, the edge weights W are recalculated. Adjust time weights according to actual needs. And precision weight μ; if time priority is given, then increase the time weight. If precision is the priority, the precision weight μ is increased to improve machining accuracy.

10. A smart manufacturing process accuracy improvement system based on graph optimization, characterized in that, include: The machining chain establishment module is used to establish the machining chain from workpiece to tool in the intelligent manufacturing unit; The ideal pose model establishment module is used to calibrate the absolute positional relationship of each device in the intelligent manufacturing unit according to the serial order of the machining chain, and to establish the ideal pose model of the cutting tool. The actual pose model establishment module is used to calibrate the motion accuracy of the equipment on the machining chain and introduce the motion accuracy into the ideal pose model of the tool to establish the actual pose model of the tool. The machining accuracy model building module is used to obtain the machining accuracy model based on the ideal pose model and the actual pose model of the tool. The graph optimization model building module is used to calculate the machining accuracy of the workpiece at different clamping positions based on the machining accuracy model, and to build a graph optimization model. The path selection module is used to select the machining path that meets the machining accuracy requirements and has the shortest processing time based on the graph optimization model.

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