Method and system for predicting workpiece milling deformation based on finite element simulation

Abstract

The invention discloses a workpiece milling deformation prediction method and system based on finite element simulation, and belongs to the technical field of milling deformation prediction.The method comprises the steps that milling parameters are input into a milling force regression prediction model to obtain milling force, and the milling parameters are input into a milling temperature regression prediction model to obtain milling temperature; dividing the workpiece into a plurality of grid units which are sequentially arranged according to the milling path, wherein the size of the grid unit corresponding to each analysis step is the same as the volume of the workpiece needing to be removed in the analysis step by the milling path; and performing finite element analysis on the workpiece according to the arrangement sequence of the grid units, applying the milling force and the milling temperature to the grid units corresponding to the analysis steps when each analysis step starts, and removing the grid units corresponding to the analysis steps when the analysis steps end to obtain a milling deformation prediction result. According to the method, the influence of the milling force and the milling temperature on the milling deformation is considered at the same time, and the prediction precision of the milling deformation is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of milling deformation prediction, and more specifically, relates to a method and system for predicting workpiece milling deformation based on finite element simulation. Background Art

[0002] Currently, the main research methods for milling deformation prediction include experimental methods, analytical methods, and finite element simulation methods. Experimental methods can obtain relatively accurate deformation patterns, but they are time-consuming and costly. Analytical methods consider the influence of factors such as tool parameters, workpiece material yield strength, and cutting conditions, but the assumptions and simplifications made during the modeling process reduce prediction accuracy and complicate the modeling process. Finite element simulation methods are cost-effective and can comprehensively consider the influence of tool and workpiece materials, resulting in relatively high deformation prediction accuracy, but the computational cost is high.

[0003] The existing technology proposes a method for predicting the micro-milling deformation of thin-walled parts. Based on finite element cutting simulation, a finite element model of milling processing is established, and the processing deformation is predicted from the perspective of milling force. However, only the influence of milling force during the processing is considered, and the influence of milling heat is not considered, resulting in low accuracy in milling deformation prediction.

[0004] It can be seen that the existing technology has a technical problem of not simultaneously considering the effects of milling force and milling heat on deformation during milling, resulting in low accuracy in milling deformation prediction. Summary of the Invention

[0005] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a method and system for predicting workpiece milling deformation based on finite element simulation, thereby solving the technical problem that the prior art does not simultaneously consider the effects of milling force and milling heat on deformation during milling, resulting in low accuracy in milling deformation prediction.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for predicting workpiece milling deformation based on finite element simulation is provided, comprising:

[0007] The milling parameters related to the deformation of the workpiece during milling are input into the milling force regression prediction model to obtain the milling force, and the milling parameters related to the deformation of the workpiece during milling are input into the milling temperature regression prediction model to obtain the milling temperature;

[0008] The workpiece is divided into multiple grid cells arranged in the order of the milling process path. The size of the grid cell corresponding to each analysis step is the same as the volume of the workpiece that needs to be removed by the milling process path in that analysis step.

[0009] Finite element analysis is performed on the workpiece according to the arrangement order of the grid cells. At the beginning of each analysis step, the milling force and milling temperature are applied to the grid cells corresponding to the analysis step. At the end of the analysis step, the grid cells corresponding to the analysis step are removed to obtain the milling deformation prediction results of the workpiece under the milling process path.

[0010] The milling force regression prediction model and the milling temperature regression prediction model are obtained by the following method:

[0011] The milling parameters related to the workpiece milling deformation are input into the milling finite element model for dynamic simulation to obtain the milling force and milling temperature;

[0012] A linear regression is performed on the milling forces obtained from multiple sets of milling parameters and their dynamic simulations to obtain a milling force regression prediction model. A linear regression is performed on the milling temperatures obtained from multiple sets of milling parameters and their dynamic simulations to obtain a milling temperature regression prediction model.

[0013] Furthermore, the dynamic simulation includes:

[0014] A milling finite element model including the tool and the workpiece is established. In the milling finite element model, the tool is divided into tetrahedral meshes, and the workpiece is divided into hexahedral meshes. The milling parameters related to the workpiece milling deformation are input into the milling finite element model for dynamic simulation to obtain the milling force and milling temperature.

[0015] Furthermore, during the dynamic simulation process, the damage of each unit after the workpiece hexahedron mesh is divided is calculated, and when the damage of the unit is a preset value, the stress of the unit is set to zero.

[0016] Furthermore, after the linear regression obtains the milling force regression prediction model, the milling force error between the predicted value of the milling parameter by the milling force regression prediction model and the result of dynamic simulation using the milling parameter is calculated. When the milling force error is greater than the mechanical threshold, the milling parameter or the milling processing finite element model is adjusted. When the milling force error is less than or equal to the mechanical threshold, the milling force regression prediction model is used for prediction.

[0017] After the milling temperature regression prediction model is obtained by linear regression, the milling temperature error between the predicted value of the milling parameters by the milling temperature regression prediction model and the result of dynamic simulation using the milling parameters is calculated. When the milling temperature error is greater than the temperature threshold, the milling parameters or the milling processing finite element model are adjusted. When the milling temperature error is less than or equal to the temperature threshold, the milling temperature regression prediction model is used for prediction.

[0018] Furthermore, when performing finite element analysis on the workpiece, at the beginning of each analysis step, the milling force is applied to the vertices of the grid unit corresponding to the analysis step, and the milling temperature is applied to the contact surface between the tool and the workpiece in the grid unit corresponding to the analysis step.

[0019] Furthermore, the milling temperature is applied to the contact surface between the tool and the workpiece in the grid unit corresponding to the analysis step in the form of heat flux. The heat flux Q is calculated as:

[0020] Q=k(Tt B )

[0021] Where k is the heat transfer coefficient, T is the milling temperature, t B is the tool temperature.

[0022] Furthermore, the milling parameters related to the deformation of the workpiece during milling include the feed rate, milling speed, radial cutting depth and axial cutting depth of the tool.

[0023] According to another aspect of the present invention, a system for predicting deformation of a workpiece during milling based on finite element simulation is provided, comprising:

[0024] A model building module is used to input milling parameters related to workpiece milling deformation into a milling finite element model for dynamic simulation to obtain milling force and milling temperature; linear regression is performed on multiple sets of milling parameters and the milling force obtained by dynamic simulation to obtain a milling force regression prediction model; linear regression is performed on multiple sets of milling parameters and the milling temperature obtained by dynamic simulation to obtain a milling temperature regression prediction model;

[0025] A regression prediction module is used to input milling parameters related to workpiece milling deformation into a milling force regression prediction model to obtain milling force, and input milling parameters related to workpiece milling deformation into a milling temperature regression prediction model to obtain milling temperature;

[0026] The deformation prediction module is used to divide the workpiece into multiple grid units arranged in the order of the milling processing path. The size of the grid unit corresponding to each analysis step is the same as the volume of the workpiece that needs to be removed in the milling processing path in this analysis step; finite element analysis is performed on the workpiece according to the arrangement order of the grid units. At the beginning of each analysis step, the milling force and milling temperature are applied to the grid unit corresponding to the analysis step. At the end of the analysis step, the grid unit corresponding to the analysis step is removed to obtain the milling deformation prediction result of the workpiece under the milling processing path.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute a method for predicting workpiece milling deformation based on finite element simulation.

[0028] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and wherein when the processor executes the computer program, a method for predicting deformation of a workpiece during milling processing based on finite element simulation is implemented.

[0029] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0030] (1) At the beginning of each analysis step, the present invention applies the milling force and milling temperature to the grid unit corresponding to the analysis step, and removes the grid unit corresponding to the analysis step at the end of the analysis step, which well simulates the continuous removal process of chips during the milling process. It belongs to static implicit analysis, which can reduce the calculation cost and improve the calculation efficiency. The workpiece is divided into multiple grid units arranged in the order of the milling process path, so that the accuracy and versatility of the simulation can be improved when performing finite element analysis. The size of the grid unit corresponding to each analysis step is the same as the volume of the workpiece that needs to be removed in the milling process step, which can reduce the workload of finite element analysis and thus improve the prediction efficiency. The regression prediction model of milling force and milling heat established by the present invention can quickly and accurately convert the milling parameters related to the milling deformation of the workpiece into milling force and milling temperature, and convert the milling simulation process that requires explicit dynamic analysis into a static implicit analysis process with multiple analysis steps, which greatly reduces the simulation calculation amount and improves the prediction efficiency. Since the influence of milling force and milling temperature on milling deformation is considered at the same time, the prediction accuracy of milling deformation is improved.

[0031] (2) The present invention divides the tool and workpiece models into grids and selects the grid size according to the hardware equipment conditions. The tool adopts a tetrahedral grid, and the workpiece adopts a C3D8RT hexahedral grid to ensure the simulation accuracy. When the damage of the unit is the preset value, the stress of the unit is set to zero, and it is regarded as the chip treatment separated from the workpiece. It can be seen that the present invention simulates the workpiece milling process by constructing a milling finite element model synchronized with the actual processing process, comprehensively considering the comprehensive influence of milling force and milling heat on the processing process, and ensuring that accurate simulation data of milling force and milling temperature can be collected. At the same time, since there is no need to carry out a large number of experiments, the consumption of tools and workpieces is reduced.

[0032] (3) The present invention uses error analysis to verify the reliability of the regression prediction model to verify whether it meets the verification principles of regression analysis and whether the accuracy of the regression prediction model meets the requirements. If the error analysis results do not meet the verification principles, it means that the accuracy of the regression prediction model does not meet the requirements, and it is necessary to adjust the milling parameters or the milling finite element model to obtain a regression prediction model that meets the accuracy requirements.

[0033] (4) The present invention applies the milling force to the vertices of the mesh cells corresponding to the analysis step and the milling temperature to the contact surface between the tool and the workpiece within the mesh cells corresponding to the analysis step, simulating the actual thermal-mechanical load application method and improving the reliability and accuracy of the prediction results. The thermal load is applied to the contact surface in the form of heat flow rather than temperature, which is more consistent with the thermodynamic governing equations and closer to the actual physical process, further improving the reliability and accuracy of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a method for predicting deformation of a workpiece during milling based on finite element simulation provided by an embodiment of the present invention;

[0035] Figure 2 This is a milling temperature data acquisition diagram provided by an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of machining deformation measurement data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0038] like Figure 1 As shown, a method for predicting workpiece milling deformation based on finite element simulation includes:

[0039] The milling parameters related to the deformation of the workpiece during milling are input into the milling force regression prediction model to obtain the milling force, and the milling parameters related to the deformation of the workpiece during milling are input into the milling temperature regression prediction model to obtain the milling temperature;

[0040] The workpiece is divided into multiple grid cells arranged in the order of the milling process path. The size of the grid cell corresponding to each analysis step is the same as the volume of the workpiece that needs to be removed by the milling process path in that analysis step.

[0041] Finite element analysis is performed on the workpiece according to the arrangement order of the grid cells. At the beginning of each analysis step, the milling force and milling temperature are applied to the grid cells corresponding to the analysis step. At the end of the analysis step, the grid cells corresponding to the analysis step are removed to obtain the milling deformation prediction results of the workpiece under the milling process path.

[0042] The milling force regression prediction model and the milling temperature regression prediction model are obtained by the following method:

[0043] The milling parameters related to the workpiece milling deformation are input into the milling finite element model for dynamic simulation to obtain the milling force and milling temperature;

[0044] A linear regression is performed on the milling forces obtained from multiple sets of milling parameters and their dynamic simulations to obtain a milling force regression prediction model. A linear regression is performed on the milling temperatures obtained from multiple sets of milling parameters and their dynamic simulations to obtain a milling temperature regression prediction model.

[0045] The milling deformation of the grid cells corresponding to each analysis step under the milling path constitutes the workpiece milling deformation prediction result.

[0046] The workpieces processed by milling include metal materials and non-metal materials.

[0047] Metal materials include: steel materials, non-ferrous metals, high-temperature alloys and special alloys.

[0048] Steel materials include structural steel, carbon tool steel, alloy tool steel, alloy structural steel, quenched steel, stainless steel, and cast iron.

[0049] Non-ferrous metals include aluminum alloys, titanium and titanium alloys, brass, and magnesium alloys.

[0050] High temperature alloys and special alloys include: Inconel, Monel, and Cobalt-Chromium.

[0051] Non-metallic materials include: ceramics, resins and composite materials.

[0052] Ceramics include zirconia and glass ceramics.

[0053] Resins and composite materials include engineering plastics and resin-based composite materials.

[0054] Aluminum alloy, one of the most common alloy materials, is often processed into high-precision structural parts in fields such as aerospace and machinery manufacturing due to its low density, low melting point, high specific strength, and ease of processing. These structural parts typically require high machining accuracy and high material removal rates, making them susceptible to deformation during actual production and processing, which in turn affects the quality of the aluminum alloy structural parts. The present invention, through Example 1, accurately predicts the milling deformation of aluminum alloy structural parts, enabling better research on factors affecting machining deformation and saving the time and cost of machining experiments.

[0055] Example 1

[0056] The present invention provides a method for predicting deformation during milling of aluminum alloy structural parts based on finite element simulation. In Example 1, the workpiece material is aluminum alloy 6060-T6, and the tool material is YG6X. The tool is used to mill the aluminum alloy 6060-T6 to form the upper half of a camera frame. A bridge-type coordinate measuring machine (CONTURA) is used to measure and collect data on areas with significant deformation, which is then compared with simulation results. The method includes the following steps S1 to S4.

[0057] S1: A milling finite element model including a tool and a workpiece is established in ABAQUS (finite element software for engineering simulation). In the milling finite element model, a tetrahedral mesh is used for the tool and a hexahedral mesh is used for the workpiece. Preferably, mechanical properties and thermal performance parameters can also be input into the material library to determine the JC constitutive model parameters and the JC damage model parameters, and to impose constraints and boundary conditions. The JC constitutive model (Johnson-Cook constitutive model) is a mathematical model used to describe the nonlinear stress-strain relationship of metal materials under complex environments such as high temperature and high strain rate. Specifically including:

[0058] S11: Before establishing a 3D milling finite element model, it is necessary to establish a 3D model of the milling cutter tool and the workpiece to be machined, and determine the mechanical and thermal performance parameters of the tool and workpiece materials, including: density, yield strength, Poisson's ratio, specific heat capacity, thermal expansion coefficient, melting point and other parameters, and enter them into the finite element simulation software material database, as shown in Table 1.

[0059] Table 1 Some mechanical properties of aluminum alloy 6060-T6

[0060] parameter Numerical <![CDATA[Density (kg / m 3 )]]> 2700 Young's modulus (GPa) 68 Poisson's ratio 0.27 Tensile strength (MPa) 220 Specific heat capacity (J / g) 0.9 Thermal conductivity (W / m K) 210 Thermal expansion (μm / m·K) 23 Solidus (℃) 610

[0061] S12: Determine the material constitutive model and its parameters. Use the JC constitutive model to define the thermoplastic behavior during metal milling. The JC constitutive model is expressed as:

[0062]

[0063] Among them, A is the yield strength of the workpiece material, B is the strain hardening constant, C is the strain rate sensitivity coefficient, n is the strain hardening exponent, m is the thermal softening exponent; ε is the equivalent plastic strain, is the equivalent plastic strain rate, is the reference plastic strain rate; T is the workpiece temperature, T m is the melting temperature of the material, T r is the processing environment temperature, and σ is the equivalent stress. The JC constitutive model parameters of aluminum alloy 6060-T6 are shown in Table 2.

[0064] Table 2 JC constitutive model parameters of aluminum alloy 6060-T6

[0065]

[0066] The chip separation criterion for finite element calculation is determined. The JC damage model is used as the criterion for judging the separation of chips from the workpiece matrix. Based on this model, damage calculation is performed on each element:

[0067]

[0068] Where Δε is the equivalent plastic strain increment, ε f is the fracture strain of the element. During the simulation, Δε is updated at each analysis step. f It is calculated by the following formula:

[0069]

[0070] Among them, σ m is the mean stress, σ eq is the equivalent stress; D1 to D5 are JC failure parameters determined by the properties of the material itself, D1 is the reference fracture strain, D2 is the fracture strain amplitude coefficient, D3 is the stress state sensitivity coefficient, D4 is the strain rate sensitivity coefficient, and D5 is the temperature sensitivity coefficient. is the current strain rate, is the reference strain rate. The JC failure parameters for aluminum alloy 6060-T6 are shown in Table 3. When the calculated result of D is 1, the corresponding element fails, and the stress component of the corresponding failed element is set to zero and remains zero throughout the subsequent simulation, being treated as a chip separated from the workpiece.

[0071] Table 3 Failure parameters of aluminum alloy 6060-T6 JC

[0072] <![CDATA[D1]]> <![CDATA[D2]]> <![CDATA[D3]]> <![CDATA[D4]]> <![CDATA[D5]]> -0.197 1.102 -0.971 0.05 5.696

[0073] S13: Mesh the tool and workpiece models and determine the analysis step type. The mesh size is selected based on the hardware requirements. A C3D4T tetrahedral mesh is used for the tool, while a C3D8RT hexahedral mesh is used for the workpiece to ensure simulation accuracy. The global mesh size is set to 1 mm, while the mesh size near the milling area is set to 0.3 mm. Explicit temperature-displacement coupled analysis is used for both models to ensure accurate simulation data for milling forces and temperatures.

[0074] S14: Apply constraints and boundary conditions to the tool and workpiece. Because the tool hardness during milling is typically much greater than the workpiece material hardness, the tool is set as a rigid body and the surface region node is coupled to a reference point on the tool axis. A horizontal velocity and an angular velocity about the tool axis are applied to this point to simulate the tool feed motion and spindle speed during milling. A fully fixed constraint is applied to the bottom of the test piece to simulate the condition of the test piece being fixed to the machine tool. A temperature field is applied to the model as the ambient temperature, set to 20°C.

[0075] S15: Set up the interaction. This interaction uses general contact for surface-to-surface contact, including contact between the tool and the workpiece, and between the workpiece and itself. The hardness of the YG6X tungsten steel tool is much greater than that of aluminum alloy, so the contact surface on the tool is the primary surface, and the workpiece mesh surface is the secondary surface. All mesh surfaces of the workpiece itself are set as contact surfaces. Based on the material properties of 6060-T6, the contact properties are set as follows: mechanical tangential behavior is defined as "penalty contact," the friction coefficient is 0.3, and normal behavior is defined as "hard contact."

[0076] S2: Determine the number of factors that affect machining deformation that need to be studied, and design the number of levels of the factors based on the central composite design method, determine the experimental plan and conduct trial cutting simulation experiments, and collect milling force and milling temperature data in the simulation experiments. Specifically including:

[0077] S21: Based on the actual machining parameters, determine the number of influencing factors that influence the machining deformation to be studied. Based on the principle of central composite design, set the number of levels for each factor based on the number of influencing factors, and determine the experimental plan and number of experiments. Based on the actual machining conditions, select the tool feed per tooth, milling speed, radial depth of cut, and axial depth of cut as the milling parameters related to workpiece milling deformation. Based on the principle of central composite design, design a four-factor, five-level experiment, and conduct a total of 30 simulation experiments.

[0078] Table 4 Experimental factors and levels

[0079]

[0080] S22: Conduct simulation experiments based on the established 3D milling finite element model and collect milling force and temperature data. Adjust the boundary conditions within the simulation model according to the designed experimental plan to conduct experiments with different milling parameter combinations. Because dynamic simulation of the entire milling process requires high hardware requirements and is time-consuming, the dynamic simulation analysis should be performed only until the collected milling force and temperature data curves stabilize.

[0081] S23: Collect and process each set of simulation experiment data. Extract the highest temperature of each frame on the finite element model during the simulation experiment as the milling temperature during the milling process, and the three-directional support reaction forces on the reference point as the three components of the milling force. After noise reduction processing, the peak average values ​​in the stable phase are output as the experimental results. Figure 2 Shown are the milling temperature data extracted from one set of experiments.

[0082] S3: Perform regression analysis on the experimental data through Design-Expert to obtain a regression prediction model of milling parameters, milling force, and milling temperature. The milling parameters required to predict machining deformation are input into the model to obtain the milling force and milling thermal load required to be applied to the simulation model. Specifically, it includes:

[0083] S31: Input the influencing factor level of each set of experiments and the corresponding experimental results into Design-Expert for regression analysis to obtain the regression model between the influencing factor to be studied and the milling force and milling temperature. The regression prediction model in the implementation example is:

[0084]

[0085] F y =191.97-954.58f z +0.84542v c +85.892a e -193.98a p -5.8625f z v c +181.25f z a e +339.25f z a p -0.63125v c a e +0.48375v c a p +12.025a e a p +1377.9f z 2<h2 style=";text-align:left;direction:ltr">+0.012573v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -24.771a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +28.129a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0086] <h2 style=";text-align:left;direction:ltr"> F<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> =337.50-2192.3f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> -3.6263v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> +38.275a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> +5.375a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +5.8125f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> +253.25f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> +359.75f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +0.77375v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> +0.08625v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +20.525a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +2922.9f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +0.018448v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -31.271a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -31.121a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0087] <h2 style=";text-align:left;direction:ltr"> T=-310.98+1752.7f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> +11.268v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> +10.333a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> +71.683a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> -0.25f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> +130.50f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> -219.50f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> -0.9375v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> +0.1925v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> -7.3a<h2 style=";text-align:left;direction:ltr"> e <h2 style=";text-align:left;direction:ltr"> a<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> -3434.2f<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> -0.10335v<h2 style=";text-align:left;direction:ltr"> c <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +7.2083a<h2 style=";text-align:left;direction:ltr"> e2 -3.3417a p 2

[0088] Among them, f z is the feed rate, v c is the milling speed, a e is the radial cutting depth, a p is the axial cutting depth, F x 、F y 、F z is the three-dimensional milling force, and T is the milling temperature.

[0089] S32: Use variance analysis to verify the regression prediction model. Use variance analysis to verify the reliability of the model, verify whether it meets the test principles of regression analysis, whether the accuracy of the regression prediction model meets the requirements, distinguish between significant and insignificant items, and identify which influencing factors have the greatest impact on milling force and milling temperature. If the variance analysis results do not meet the test principles, it is necessary to check whether there are any problems in the various preparation links of the simulation experiment and make corresponding adjustments and improvements (for example, adjust the milling parameters or the milling finite element model).

[0090] S33: The feed per tooth, milling speed, radial cutting depth and axial cutting depth used in actual processing are input into the regression prediction model to obtain the thermal load data for converting the tool action, and the data are recorded in an Excel document according to the unit number of the complete milling area for future use. The grid unit number is recorded in the first column, the three-dimensional component data of the milling force are recorded in the second, third and fourth columns, and the milling temperature data are recorded in the fifth column.

[0091] S4: Perform meshing, confirm the milling path, apply loads, delete elements, and other settings in the milling simulation model of the workpiece. Conduct static implicit simulation analysis to obtain machining deformation prediction results, including:

[0092] S41: Using the aluminum alloy material properties, constitutive model, and chip separation criteria from S2, a 3D model is created based on the actual dimensions of the workpiece. Boundary conditions are applied to the workpiece based on the actual clamping conditions during machining. The initial ambient temperature is set to 20°C based on the machining environment. The clamping conditions are set to fully constrained boundary conditions.

[0093] S42: The mesh unit size is divided according to the volume of material that needs to be removed in each analysis step. The area of ​​the tool action is determined by the radial cutting depth and axial cutting depth of the tool.

[0094] S43: Extract the milling path. The milling process path is usually different from the arrangement of mesh elements. Therefore, it is necessary to extract the mesh unit position information on the milling path. The node coordinate information and unit node information in the finite element inp file are extracted and numbered with the thermal load recorded in S3 to apply the load.

[0095] S44: Load application. At the beginning of each analysis step, the tool will apply the milling force and milling temperature loads on the milling surface, and remove this part of the mesh units as chips at the end of the analysis step. The finite element simulation software ABAQUS has the birth and death unit technology, which is suitable for simulating the continuous removal of chips during the milling process. It belongs to the static implicit analysis, which reduces the calculation cost and improves the calculation efficiency. The birth and death unit technology uses mathematical calculation methods to multiply the unit mass by a value close to zero, that is, to return the unit mass to zero, which is equivalent to "killing" the unit, so as to achieve the effect of unit removal. Therefore, in order to convert the tool action into a static thermal-mechanical coupling action, it is necessary to first apply a thermal-mechanical load to the corresponding mesh unit in an analysis step, and then invalidate the mesh as a chip, and then repeat the operation on the next part of the mesh until the machining process is completed, so as to achieve the transformation of the tool action.

[0096] According to the milling process path, a thermal-mechanical load is applied to the corresponding unit in each analysis step. The force load is applied to the vertices of the mesh unit in the form of three components, and the thermal load is applied to the contact surface in the form of heat flow. The calculation formula of the heat flow is:

[0097] Q=k(Tt B )

[0098] Where k is the heat transfer coefficient, T is the milling temperature, t B is the tool temperature.

[0099] After all the analysis step settings are completed, submit them and start running the simulation program.

[0100] S45: Check the simulation results. After the simulation analysis is completed, the workpiece displacement data is extracted and output to obtain the simulated deformation of the workpiece after the entire processing process. Compare it with the actual workpiece measurement data after the processing is completed. The measurement data is as follows: Figure 3 shown.

[0101] The present invention simulates the metal milling process by constructing a three-dimensional milling finite element model synchronized with the actual processing process, comprehensively considering the combined effects of milling force and milling heat on the processing process, predicting the milling force distribution under different milling parameters, analyzing the influence of milling heat on the workpiece, realizing multi-physical field coupling analysis, simulating complex interaction effects, and predicting the elastic-plastic deformation of the workpiece caused by thermal-mechanical coupling during the milling process. At the same time, since there is no need to carry out a large number of experiments, the consumption of cutting tools and workpieces is reduced. The JC constitutive model describes the plastic flow stress of the material under dynamic load, taking into account strain hardening, strain rate efficiency and temperature softening. The JC damage model predicts the failure behavior of the material, determines when to delete the unit through the damage accumulation criterion, and simulates the separation of chips. Combining the JC constitutive model and the JC damage model, the simulation authenticity of the material failure behavior of aluminum alloys in the dynamic cutting process is improved, making the simulation data more reliable. Explicit dynamic analysis is used to carry out simulation experiments to collect data for constructing the regression model, making the constructed regression prediction model more reliable. The established regression model of milling force and milling heat can convert the tool action into thermal-mechanical load, and convert the milling simulation process that requires explicit dynamic analysis into a static implicit analysis process with multiple analysis steps, which greatly reduces the simulation calculation amount and makes it possible to simulate the complete milling process.

[0102] The regression prediction model of the present invention can link the milling parameters in different simulation stages with the simulated deformation results, realize the inversion of process parameters, adjust the applied thermal-mechanical load according to the changes in milling parameters, and thus affect the simulated deformation output results. Therefore, the optimal milling parameter combination can be reversely deduced according to the changes in simulation results, saving a lot of time and economic costs in conducting real processing experiments to find the optimal milling parameter combination.

[0103] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting workpiece milling deformation based on finite element simulation, characterized in that: include: The milling parameters related to the deformation of the workpiece during milling are input into the milling force regression prediction model to obtain the milling force, and the milling parameters related to the deformation of the workpiece during milling are input into the milling temperature regression prediction model to obtain the milling temperature; The workpiece is divided into multiple grid cells arranged in the order of the milling process path. The size of the grid cell corresponding to each analysis step is the same as the volume of the workpiece that needs to be removed by the milling process path in that analysis step. Finite element analysis is performed on the workpiece according to the arrangement order of the grid cells. At the beginning of each analysis step, the milling force and milling temperature are applied to the grid cells corresponding to the analysis step. At the end of the analysis step, the grid cells corresponding to the analysis step are removed to obtain the milling deformation prediction results of the workpiece under the milling process path. The milling force regression prediction model and the milling temperature regression prediction model are obtained by the following method: The milling parameters related to the workpiece milling deformation are input into the milling finite element model for dynamic simulation to obtain the milling force and milling temperature; A linear regression is performed on the milling forces obtained from multiple sets of milling parameters and their dynamic simulations to obtain a milling force regression prediction model. A linear regression is performed on the milling temperatures obtained from multiple sets of milling parameters and their dynamic simulations to obtain a milling temperature regression prediction model.

2. The method for predicting workpiece milling deformation based on finite element simulation according to claim 1, characterized in that: The dynamics simulation includes: A milling finite element model including the tool and the workpiece is established. In the milling finite element model, the tool is divided into tetrahedral meshes, and the workpiece is divided into hexahedral meshes. The milling parameters related to the workpiece milling deformation are input into the milling finite element model for dynamic simulation to obtain the milling force and milling temperature.

3. The method for predicting workpiece milling deformation based on finite element simulation according to claim 2, characterized in that: During the dynamic simulation process, the damage of each unit after the workpiece hexahedron mesh is divided is calculated, and when the damage of the unit is a preset value, the stress of the unit is set to zero.

4. A method for predicting workpiece milling deformation based on finite element simulation according to claim 2 or 3, characterized in that: After the linear regression obtains the milling force regression prediction model, the milling force error between the predicted value of the milling parameter by the milling force regression prediction model and the result of dynamic simulation using the milling parameter is calculated; when the milling force error is greater than the mechanical threshold, the milling parameter or the milling processing finite element model is adjusted; when the milling force error is less than or equal to the mechanical threshold, the milling force regression prediction model is used for prediction; After the milling temperature regression prediction model is obtained by linear regression, the milling temperature error between the predicted value of the milling parameters by the milling temperature regression prediction model and the result of dynamic simulation using the milling parameters is calculated. When the milling temperature error is greater than the temperature threshold, the milling parameters or the milling processing finite element model are adjusted. When the milling temperature error is less than or equal to the temperature threshold, the milling temperature regression prediction model is used for prediction.

5. A method for predicting workpiece milling deformation based on finite element simulation according to any one of claims 1 to 3, characterized in that: The finite element analysis of the workpiece is performed, and at the beginning of each analysis step, the milling force is applied to the vertices of the grid unit corresponding to the analysis step, and the milling temperature is applied to the contact surface between the tool and the workpiece in the grid unit corresponding to the analysis step.

6. The method for predicting workpiece milling deformation based on finite element simulation according to claim 5, characterized in that: The milling temperature is applied in the form of heat flux on the contact surface between the tool and the workpiece in the grid cell corresponding to the analysis step. The heat flux Q is calculated as: Q=k(T-t B ) Where k is the heat transfer coefficient, T is the milling temperature, t B is the tool temperature.

7. A method for predicting workpiece milling deformation based on finite element simulation according to any one of claims 1 to 3, characterized in that: The milling parameters related to the deformation of the workpiece during milling include the feed rate, milling speed, radial cutting depth and axial cutting depth of the tool.

8. A system for predicting deformation of workpiece milling based on finite element simulation, characterized in that: include: The model building module is used to input milling parameters related to workpiece milling deformation into the milling finite element model for dynamic simulation to obtain milling force and milling temperature; Linear regression is performed on multiple sets of milling parameters and the milling forces obtained by dynamic simulation to obtain a milling force regression prediction model. Linear regression is performed on multiple sets of milling parameters and the milling temperatures obtained by dynamic simulation to obtain a milling temperature regression prediction model. A regression prediction module is used to input milling parameters related to workpiece milling deformation into a milling force regression prediction model to obtain milling force, and input milling parameters related to workpiece milling deformation into a milling temperature regression prediction model to obtain milling temperature; The deformation prediction module is used to divide the workpiece into multiple grid units arranged in the order of the milling processing path. The size of the grid unit corresponding to each analysis step is the same as the volume of the workpiece that needs to be removed in the milling processing path in this analysis step; finite element analysis is performed on the workpiece according to the arrangement order of the grid units. At the beginning of each analysis step, the milling force and milling temperature are applied to the grid unit corresponding to the analysis step. At the end of the analysis step, the grid unit corresponding to the analysis step is removed to obtain the milling deformation prediction result of the workpiece under the milling processing path.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute a method for predicting workpiece milling deformation based on finite element simulation as described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for predicting workpiece milling deformation based on finite element simulation according to any one of claims 1 to 7 is implemented.

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