Five-axis vertical-horizontal conversion machining center temperature measuring point optimization method based on refined finite element

Abstract

The invention discloses a five-axis vertical-horizontal conversion machining center temperature measuring point optimization method based on a refined finite element, and belongs to the technical field of numerical control machine tool thermal error compensation. The method comprises the following steps: calculating a heat source load and a boundary condition of the five-axis vertical-horizontal conversion machining center; according to the method, Solidworks-Hypmesh-Ansys combination is used for carrying out refined finite element modeling on a part matching surface-part base body of the five-axis vertical and horizontal conversion machining center; s30, a temperature field and a deformation field of the whole machine are obtained through thermal-mechanical coupling analysis, and temperature measuring points are obtained; analyzing temperature sensitive points of the whole machine based on an improved K-value clustering method-grey relational degree method, and optimizing temperature measuring points; temperature acquisition is carried out based on the optimal temperature measuring point, thermal error modeling and thermal error compensation are carried out on the basis, and finally the effectiveness of the temperature sensitive point optimization method is verified in combination with the machining precision of the whole machine. According to the method, the problem that a whole machine thermal characteristic numerical simulation result is not consistent with actually measured data due to automatic mesh division of the joint surface can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of thermal error compensation of CNC machine tools in high-precision manufacturing industry, and in particular to a temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element analysis. Background Art

[0002] As the manufacturing industry develops towards high-speed and high-precision processing, the impact of thermal errors on the accuracy of CNC machine tools is becoming increasingly serious. Studies have shown that about 75% of the geometric errors of machined workpieces are caused by thermal errors of machine tools. In order to reduce the impact of thermal errors and compensate for thermal errors in a timely manner, reasonable temperature measurements are required. However, due to the complex temperature field distribution of the five-axis vertical-horizontal conversion machining center, a large number of temperature sensors are theoretically required to measure the temperature at different positions. The temperature measurement point optimization technology of the five-axis vertical-horizontal conversion machining center involves finding the temperature sensitive points of the whole machine. After the temperature field is obtained by the steady-state thermal analysis of the whole machine, the single use of the temperature cloud map to collect sensitive points is bound to lead to inaccurate results and the loss of some temperature measurement points. Therefore, the temperature measurement point optimization technology also needs to introduce thermal-mechanical coupling analysis, and use the comprehensive influence of the temperature field and thermal deformation field to find all the temperature sensitive points of the whole machine to improve the accuracy and robustness of the thermal error model. At present, the research on temperature measurement point optimization technology for CNC machine tools is mostly based on finite element and clustering algorithms. Thermomechanical coupling finite element analysis methods such as 202011011840.X and 202311076789.4 have been developed, as well as the fuzzy C-mean FCM clustering analysis method for temperature measurement point clustering. However, the existing whole machine thermal-mechanical coupling analysis often uses the automatic grid drawing method, resulting in inaccurate temperature field and deformation field results. The existing clustering method relies heavily on the initial point and cannot handle clusters of different shapes and sizes well. Considering the visibility and operability of the finite element simulation method, how to propose an accurate finite element simulation analysis method has always been a key problem in the field of thermal error compensation technology for CNC machine tools. Summary of the invention

[0003] The purpose of the present invention is to provide a temperature measurement point optimization method for a five-axis vertical-horizontal conversion machining center based on refined finite element analysis, so as to solve the problems of poor simulation model accuracy, incomplete clustering, high test cost and difficult application.

[0004] The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to the present invention specifically comprises the following steps:

[0005] First, we need to calculate the heat source load and boundary conditions of the five-axis vertical-horizontal conversion machining center. The heat sources of the five-axis vertical-horizontal conversion machining center include the guide rail slider pair, the main shaft and other bearing pairs, the screw nut pair and the motor. The specific heat source heat generation formula is:

[0006]

[0007] Among them, M1 is the total friction torque of the bearing, n1 is the bearing speed, M2 is the friction torque of the screw nut, n2 is the speed of the ball screw, μ is the dynamic friction coefficient, W is the load perpendicular to the friction surface of the guide rail, ν is the sliding speed, J is the thermal equivalent, and η is the efficiency of the motor.

[0008] The boundary conditions of the five-axis vertical-horizontal conversion machining center are the natural convection coefficient h1 between the whole machine and the air, the forced convection coefficient h2 between the main shaft and the air when the bearings rotate at high speed, the forced convection coefficient h3 between the screw nut pair and the air when it rotates, and the forced convection coefficient h4 between the motor and the air cooling. The specific functional relationship is

[0009] q=hA(T s -T f )

[0010] Where A is the area perpendicular to the heat flow direction, T s -T f is the temperature difference on the surface of an object.

[0011] Secondly, a fine finite element modeling method of the part mating surface-part matrix of the Solidwroks-HyperMesh-Ansys joint is proposed. The main joint surfaces of the whole machine are meshed and layered. On the basis of the automatic mesh of the whole machine, the mesh layering nodes are determined, and the mesh size and mesh density of the main joint surfaces such as the guide slider pair, the screw nut pair and the bearing pair are finely adjusted. Other parts remain unchanged, and the nodes are connected to each other.

[0012] On this basis, material property parameters are added, initial boundary conditions, forced convection coefficients and contact states are set, and the temperature field and deformation field of the whole machine are obtained through thermal-mechanical coupling analysis.

[0013] Then, based on the improved K value clustering method-grey correlation method, the steady-state temperature sensitive points of the whole machine are analyzed to determine the optimal temperature measurement point and optimize the temperature measurement point of the whole machine. Based on the improved K value clustering method, several initial temperature sensitive points of the whole machine are clustered and analyzed in turn to select different types of temperature sensitive point sets. Secondly, the grey correlation algorithm is used to obtain the grey correlation degree of each temperature sensitive point in each type of set. Finally, the temperature sensitive points of each type are sorted based on the obtained grey correlation value, and the temperature sensitive point with the highest grey correlation value of each type is taken as the optimal measurement point to obtain the position of the temperature measurement point of the whole machine.

[0014] Finally, temperature is collected and thermal error modeling and thermal error compensation are carried out on this basis. Finally, the effectiveness of the temperature sensitive point optimization method is verified by comparing the processing accuracy of the whole machine.

[0015] The present invention provides a temperature measurement point optimization method for a five-axis vertical-horizontal conversion machining center based on refined finite element analysis, the purpose of which is to provide accurate temperature measurement points for thermal error modeling of the whole machine and to accurately perform thermal error compensation for the whole machine.

[0016] The above technical objectives of the present disclosure are achieved through the following technical solutions:

[0017] The beneficial effects of the present disclosure are as follows: the temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element analysis described in the present invention is to find the temperature measurement points by comprehensively considering the influence of temperature and thermal deformation on the temperature measurement points on the basis of the refined finite element model, and optimize the temperature measurement points based on the clustering algorithm. The analysis results are visualized and the method is convenient for engineering application, including the calculation of heat source load and boundary conditions, the proposed Solidwroks-HyperMesh-Ansys joint part matching surface-part matrix refined finite element modeling method, the whole machine thermal-mechanical coupling analysis to obtain steady-state temperature measurement points, and the temperature measurement point optimization based on the improved K value clustering algorithm-grey correlation method. Finally, the temperature is collected at the optimal temperature measurement point, and the thermal error modeling and thermal error compensation of the whole machine are carried out on this basis. Finally, the effectiveness of the temperature sensitive point optimization method is verified by comparing the machining accuracy of the whole machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart for analyzing the optimization method of temperature measurement points in a five-axis vertical-horizontal conversion machining center based on refined finite element analysis.

[0019] Figure 2 A grid layer diagram for the rail slider.

[0020] Figure 3 This is a flow chart for optimizing temperature measurement points based on the improved K-value clustering method-grey correlation method.

[0021] Figure 4 This is a comparison chart of machining accuracy before and after measuring point optimization. DETAILED DESCRIPTION

[0022] Combine the following Figure 1 The flowchart of the temperature measurement point optimization method of the five-axis vertical-horizontal conversion machining center based on refined finite element analysis describes the technical solution in the embodiment of the present invention in more detail. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without proposing creative work are all within the scope of protection of the present invention.

[0023] Figure 1 The flow chart of the disclosed method is as follows: Figure 1As shown in the figure, the temperature measurement point optimization method of the five-axis vertical-horizontal conversion machining center based on refined finite element analysis is to find the temperature measurement points by comprehensively considering the influence of temperature and thermal deformation on the temperature measurement points on the basis of the refined finite element model, and optimize the temperature measurement points based on the clustering algorithm. The analysis results are visualized and easy to apply in engineering, including the calculation of heat source load and boundary conditions, the proposed Solidwroks-HyperMesh-Ansys joint part matching surface-part matrix refined finite element modeling method, the whole machine thermal-mechanical coupling analysis to obtain steady-state temperature measurement points, and the temperature measurement point optimization based on the improved K value clustering algorithm-grey correlation method. Finally, the temperature is collected at the best temperature measurement point, and the thermal error modeling and thermal error compensation of the whole machine are carried out on this basis. Finally, the effectiveness of the temperature sensitive point optimization method is verified by comparing the machining accuracy of the whole machine.

[0024] Firstly, the heat source analysis and boundary condition analysis of the five-axis vertical-horizontal conversion machining center are carried out. The analysis shows that the heat source of the whole machine includes the guide rail slider pair, the main shaft bearing pair, the lead screw nut pair and the motor, that is,

[0025]

[0026] Among them, M1 is the total friction torque of the bearing, n1 is the bearing speed, M2 is the friction torque of the screw nut, n2 is the speed of the ball screw, μ is the dynamic friction coefficient, W is the load perpendicular to the friction surface of the guide rail, ν is the sliding speed, J is the thermal equivalent, and η is the efficiency of the motor.

[0027] At the same time, the boundary conditions include the natural convection coefficient h1 between the whole machine and the air, the forced convection coefficient h2 between the main shaft and the air when the bearings rotate at high speed, the forced convection coefficient h3 between the screw nut pair and the air when it rotates, and the forced convection coefficient h4 between the motor and the air cooling. The convection formula is:

[0028] q=hA(T s -T f )

[0029] Where A is the area perpendicular to the heat flow direction, T s -T f is the temperature difference on the surface of an object.

[0030] Figure 2 Refine the mesh map for the guide rail slider, such as Figure 2As shown. The combination of the guide rail and the slider is layered based on Hypermesh, and the axial transition is used near the joint surface: the mesh is transitioned axially, that is, the Surface Edit function is used on the solid surface of the guide rail and slider to cut out the axial transition mesh auxiliary torus, and the Lines function and the Ruled function are used to construct the axial transition unit mesh. Based on the transition mesh, a hexahedral transition mesh is mapped along the mesh nodes on the transition torus. On this basis, material property parameters are added. For large parts such as columns, bed bodies and beams, cast iron materials are used. For bearings and lead screws, bearing steel is used. Structural steel is used for other parts. Initial and boundary conditions are set, and natural convection is added to the surface of the entire machine in the steady-state thermal module. At the same time, heat loads and forced convection coefficients are added to the above-mentioned heat sources. Finally, the contact state of the guide rail and the slider is set to no separation. The temperature field and deformation field of the entire machine are obtained through thermal coupling analysis, and several temperature measurement points are obtained.

[0031] Figure 3 This is a flowchart based on the improved K-value clustering method-grey correlation method. It analyzes the initial temperature sensitive points of the whole machine, determines the optimal temperature measurement point, and optimizes the temperature measurement point of the whole machine. Specifically, based on the improved K-value clustering method, the steady-state temperature sensitive points of the whole machine are clustered and analyzed in turn, and different types of temperature sensitive point sets are selected. Secondly, the grey correlation algorithm is used to obtain the grey correlation degree of each temperature sensitive point in each type of set. Finally, the temperature sensitive points of each type are sorted based on the obtained grey correlation value, and the temperature sensitive point with the highest grey correlation value of each type is taken as the optimal measurement point to obtain the position of the temperature measurement point of the whole machine.

[0032] Finally, the temperature is collected and the thermal error modeling and thermal error compensation of the whole machine are carried out on this basis. Finally, the effectiveness of the temperature sensitive point optimization method is verified by comparing the machining accuracy of the whole machine. Figure 4 shown.

Claims

1. A temperature measurement point optimization method for a five-axis vertical-horizontal conversion machining center based on refined finite element, characterized in that: The following steps are involved: Step 1: Calculate the heat source load and boundary conditions of the five-axis vertical-horizontal conversion machining center; Step 2: Propose a Solidworks-HyperMesh-Ansys joint part mating surface-part matrix refined finite element modeling method; Including: constructing a simplified finite element analysis model based on the three-dimensional model of the five-axis vertical-horizontal conversion machining center, using hypmesh to finely divide the mesh of the guide slider pair, the screw nut pair and the bearing pair joint surface, automatically dividing the mesh of the remaining parts, and checking the mesh nodes; Step 3: Obtain the temperature field and deformation field of the whole machine through thermal-mechanical coupling analysis, and determine the temperature sensitive points of the whole machine under steady state; including: adding material property parameters, setting initial boundary conditions, forced convection coefficients and contact states; Step 4: Analyze the temperature sensitive points of the whole machine based on the improved K value clustering method-grey correlation method and optimize the temperature measurement points. Step 5: Based on the optimal temperature measurement point, thermal error modeling and thermal error compensation are performed, and finally the effectiveness of the temperature sensitive point optimization method is verified by combining the whole machine processing accuracy.

2. The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to claim 1 is characterized in that: The heat sources in step 1 include the guide rail slider pair, the main shaft bearing pair, the lead screw nut pair and the motor.

3. The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to claim 1 is characterized in that: The boundary conditions in step 2 are the natural convection coefficient h1 between the whole machine and the air, the forced convection coefficient h2 between the spindle bearing and the air when rotating at high speed, the forced convection coefficient h3 between the screw nut pair and the air when rotating, and the forced convection coefficient h4 between the motor and the air cooling. The specific functional relationship q is as follows: q=hA(T s -T f ) (1) Where A is the area perpendicular to the heat flow direction, T s -T f is the temperature difference on the surface of an object.

4. The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to claim 1 is characterized in that: In step 2, a Solidworks-HyperMesh-Ansys joint part mating surface-part matrix refined finite element modeling method is proposed, that is, the main mating surfaces of the whole machine are meshed in layers, and on the basis of automatic meshing, the mesh layering nodes are determined, and the mesh size and mesh density of the guide rail slider pair, the screw nut pair and the bearing pair mating surface are finely adjusted. Other parts remain unchanged, and at the same time, the nodes are ensured to be connected to each other.

5. The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to claim 1 is characterized in that: In step 3, a steady-state thermal analysis of the whole machine is first performed, the resulting heat load is directly loaded, and boundary conditions are set to obtain the steady-state temperature field of the whole machine. Subsequently, a structural analysis is performed on the steady-state analysis results, and fixed supports are set to obtain the thermal deformation field of the whole machine. Finally, the temperature sensitive points of the whole machine in the steady state are obtained based on the temperature cloud map and the thermal deformation cloud map.

6. The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to claim 1 is characterized in that: In step 4, cluster analysis is performed on the steady-state temperature sensitive points of the whole machine based on the improved K-value clustering method, and sets of temperature sensitive points of different categories are selected; the grey correlation algorithm is used to obtain the grey correlation degree of each temperature sensitive point in each set, and the temperature sensitive points of each category are sorted based on the obtained grey correlation value. The temperature sensitive point with the highest grey correlation value in each category is taken as the optimal measuring point, and the position of the temperature measuring point of the whole machine is obtained.

7. The temperature measurement point optimization method of a five-axis vertical-horizontal conversion machining center based on refined finite element according to claim 1 is characterized in that: In step 5, temperature is collected based on the optimal temperature measurement point, and thermal error modeling and thermal error compensation are performed on the whole machine on this basis. Finally, the effectiveness of the temperature sensitive point optimization method is verified by comparing the machining accuracy of the whole machine.

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