A milling and boring machine ram optimization method and device based on a functionally graded material

Abstract

This invention relates to the field of machine tool design and manufacturing technology, and provides a method and apparatus for optimizing the slide of a milling and boring machine based on functionally graded materials (FJTs). The method includes: establishing a dynamic finite element model of the slide component; obtaining the deformation field and stress distribution of the slide component by simulating its working state under machining load to determine the optimization objective; establishing a mapping relationship between the material composition distribution and equivalent elastic parameters of the FJT; optimizing the gradient distribution function of the FJT based on the mapping relationship, with the end deformation in the optimization objective as the optimization direction, to obtain the optimal material gradient distribution function; and controlling the manufacturing process to form the slide component according to the optimal material gradient distribution function. This invention solves the problem in the prior art of actively controlling the stiffness distribution of the slide at the structural level to suppress its pitch deformation, and realizes the optimization of the mechanical properties of the slide through the axial gradient distribution of material stiffness.

Description

Technical Field

[0001] This invention relates to the field of machine tool design and manufacturing technology, and in particular to a method and apparatus for optimizing the slide of a milling and boring machine based on functionally graded materials. Background Technology

[0002] In CNC floor-type milling and boring machines, the ram, as a key load-bearing and guiding component of the spindle system, directly affects the overall machining performance of the machine tool. During actual machining, as the ram extends outward along the guide rail, it forms a cantilever beam structure due to its own weight. The dynamic change in its center of gravity causes a pitch deviation at the distal end, the so-called "head-down" phenomenon. Simultaneously, the force exerted by the workpiece during machining is transmitted to the front end of the ram through the spindle, further exacerbating bending and torsional deformation. These deformations increase with the extension length of the ram, causing the actual rotation axis of the spindle to gradually deviate from its theoretical position, thus introducing machining errors and affecting the workpiece forming accuracy.

[0003] To address the aforementioned issues, various compensation schemes have been proposed in the prior art. For example, a pre-deformation device can be used to apply reverse pre-deformation to the slide to counteract the effects of gravity; or a closed-loop feedback system can be employed to use sensors to measure position errors in real time and perform dynamic correction; or the software compensation function of the CNC system can be used to write the offline calibrated compensation amount into the machining program.

[0004] However, the aforementioned pre-deformation compensation schemes rely on static preset parameters, making it difficult to adapt to dynamic changes in cutting forces and temperatures during actual machining. Closed-loop feedback compensation systems suffer from complex structures, high hardware costs, and sluggish control response. CNC system software compensation is limited by offline calibrated compensation models and cannot effectively correct real-time errors generated during machining. Therefore, how to actively control the stiffness distribution of the slide at the structural level to suppress its pitch deformation during extension has become a crucial issue that urgently needs to be addressed in the industry. Summary of the Invention

[0005] This invention provides a method and apparatus for optimizing the slide of a milling and boring machine based on functionally graded materials. It solves the problem in the prior art that it is difficult to actively control the stiffness distribution of the slide at the structural level to suppress its pitch deformation, and realizes the optimization of the mechanical properties of the slide by the axial gradient distribution of material stiffness.

[0006] This invention provides a method for optimizing the slide of a milling machine based on functionally graded materials, comprising the following steps: A dynamic finite element model of the ram component is established, and the deformation field and stress distribution of the ram component are obtained by simulating the working state of the ram component under processing load, so as to determine the optimization target. A mapping relationship is established between the material composition distribution and equivalent elastic parameters of functionally graded materials, wherein the functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus; Based on the mapping relationship, with the end deformation in the optimization objective as the optimization direction, the gradient distribution function of the functionally graded material is optimized to obtain the optimal material gradient distribution function; The manufacturing process is controlled to form the slide component based on the optimal material gradient distribution function.

[0007] According to the present invention, a method for optimizing the slide of a milling and boring machine based on functionally graded materials is provided. The method involves establishing a dynamic finite element model of the slide component and simulating its working state under machining loads. Specifically, this includes: simplifying non-critical features in the three-dimensional solid model of the slide component to establish a finite element analysis model; selecting cutting parameters according to preset cutting conditions, calculating the cutting load, and determining the upper limit of the cutting load; applying the cutting load to the finite element analysis model and obtaining the spatial deformation field and stress distribution cloud map of the slide component under typical machining loads through simulation calculations; and determining the location of the maximum deflection and the high-stress region of the slide component based on the spatial deformation field and stress distribution cloud map.

[0008] This invention provides a method for optimizing the slide of a milling machine based on functionally graded materials (FJCTs). The method establishes a mapping relationship between the material composition distribution of FJCTs and their equivalent elastic parameters. Specifically, it includes: defining the axial direction of the slide component as the gradient direction, and selecting a gradient distribution function to describe the spatial variation of the volume fraction of the hard phase material along the gradient direction; the gradient distribution function is a power function; based on inclusion theory and introducing a modified mean field model, a quantitative mapping relationship is established between the local volume fraction of the hard phase material and the macroscopic equivalent elastic parameters; the gradient distribution function is parameterized to quantify the influence weight of the gradient coefficient on the equivalent elastic parameters; and, combined with the geometric parameters of the slide component, an integral expression between the local modulus gradient and bending stiffness is established based on beam theory to determine the mechanism by which local modulus control optimizes overall stiffness.

[0009] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided. The method optimizes the gradient distribution function of the functionally graded materials, specifically including: establishing a material-structure multi-constraint collaborative optimization model, integrating the parameterized material property field into the dynamic finite element model; taking the minimization of the end deformation of the slide component as the optimization objective, and taking the maximum working stress of the slide component not exceeding the allowable stress of the material and the gradient distribution function meeting the manufacturing process requirements as constraints, the method performs collaborative optimization to solve the gradient distribution function to obtain the optimal gradient distribution model.

[0010] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided. The method for controlling the manufacturing process to form the slide component specifically includes: using a casting process, injecting molten materials with different compositions for forming the hard phase material and the soft phase material into a multi-channel gating system through timing control; and controlling the injection sequence and proportion of the molten materials so that the material composition of the formed slide component changes continuously or quasi-continuously along the axial direction.

[0011] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided. The method for controlling the manufacturing process to form the slide component further includes: performing rough machining, heat treatment and finish machining on the cast slide blank in sequence to complete the preparation of the slide component.

[0012] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided, wherein the elastic modulus of the material at the fixed end of the slide component is higher than that at the distal end.

[0013] The present invention also provides a milling and boring machine ram optimization device based on functionally graded materials, comprising the following modules: The finite element model building module is used to build a dynamic finite element model of the ram component, and obtain the deformation field and stress distribution of the ram component by simulating the working state of the ram component under processing load, so as to determine the optimization target; The mapping relationship establishment module is used to establish the mapping relationship between the material composition distribution and the equivalent elastic parameters of functionally graded materials, wherein the functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus; The function optimization module is used to optimize the gradient distribution function of the functionally graded material based on the mapping relationship and with the end deformation in the optimization objective as the optimization direction, so as to obtain the optimal material gradient distribution function. The ram preparation module is used to control the manufacturing process to form the ram component according to the optimal material gradient distribution function.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the milling machine ram optimization method based on functionally graded materials as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the milling machine ram optimization method based on functionally graded materials as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the milling machine ram optimization method based on functionally graded materials as described above.

[0017] This invention provides a method and apparatus for optimizing the slide of a milling and boring machine based on functionally graded materials (FJTs), which offers the following advantages: By establishing a dynamic finite element model of the slide component and simulating its working state under machining load, the deformation field and stress distribution of the slide component are obtained, thereby determining the optimization objective with end deformation as the core. This provides a clear mechanical guidance for subsequent optimization. By establishing a mapping relationship between the component distribution of hard and soft phases in the FJT and the equivalent elastic parameters, a theoretical basis for quantitative control of material properties is provided. The gradient distribution function is optimized with end deformation as the optimization direction. The obtained optimal material gradient distribution function allows the axial configuration of material stiffness to directly serve the goal of suppressing end deformation. The manufacturing process is controlled according to this optimal distribution function to form the slide component, realizing the transformation from material design to physical entity. Therefore, this solution can actively control the stiffness distribution of the slide at the structural level, matching the material stiffness with the distribution law of decreasing bending moment from the fixed end to the far end in the slide cantilever structure, thereby effectively suppressing end pitch deformation of the slide during the extension process and improving the machining accuracy of the machine tool. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the milling machine slide optimization method based on functionally graded materials provided by the present invention.

[0020] Figure 2 This is a schematic diagram of the slide of the CNC floor-type milling and boring machine provided by the present invention.

[0021] Figure 3 This is a schematic diagram of the slide structure provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the flexural deformation of a conventional slide block provided by the present invention.

[0023] Figure 5 This is a schematic diagram of the flexural deformation of a slide block with a stiffness gradient provided by the present invention.

[0024] Figure 6This is a schematic diagram of the structure of the milling machine slide optimization device based on functionally graded materials provided by the present invention.

[0025] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] The following is combined with Figures 1-7 The embodiments of the present invention are described in detail.

[0028] The milling machine slide optimization method based on functionally graded materials provided in this embodiment of the invention is executed by a milling machine slide optimization device based on functionally graded materials. This device can be configured in a computer, which can be a local computer or a cloud computer. The local computer can be a computer, tablet, etc., and no specific limitation is made here.

[0029] Figure 1 This is a flowchart illustrating the milling machine ram optimization method based on functionally graded materials provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: S110. Establish a dynamic finite element model of the ram component, and obtain the deformation field and stress distribution of the ram component by simulating the working state of the ram component under processing load, so as to determine the optimization target.

[0030] According to the present invention, a method for optimizing the slide of a milling and boring machine based on functionally graded materials is provided. This method establishes a dynamic finite element model of the slide component and simulates its working state under machining loads. Specifically, the method includes: simplifying non-critical features in the three-dimensional solid model of the slide component to establish a finite element analysis model; selecting cutting parameters according to preset cutting conditions, calculating the cutting load, and determining the upper limit of the cutting load; applying the cutting load to the finite element analysis model and obtaining the spatial deformation field and stress distribution cloud map of the slide component under typical machining loads through simulation calculations; and determining the location of the maximum deflection and the high-stress region of the slide component based on the spatial deformation field and stress distribution cloud map.

[0031] Specifically, a dynamic finite element model of the ram component is established. Non-critical features in the 3D solid model of the ram, such as chamfers, small holes, and non-load-bearing surfaces, are simplified to establish a finite element analysis model of the ram component. Reasonable cutting parameters are selected based on actual cutting conditions, and the cutting load is accurately calculated and its upper limit is determined. The calculated cutting load is applied to the finite element analysis model, and simulation calculations are used to obtain the spatial deformation field and stress distribution cloud map of the ram component under typical machining loads. Based on the spatial deformation field and stress distribution cloud map, the location of the maximum deflection along the entire length of the ram and the distribution range of high-stress regions are quantitatively determined, thus providing clear mechanical objectives and benchmark references for subsequent stiffness gradient optimization.

[0032] This embodiment establishes a dynamic finite element model of the ram component and simplifies the three-dimensional solid model by applying cutting loads for simulation calculations. This accurately obtains the spatial deformation field and stress distribution cloud map of the ram component under typical machining loads, thereby quantitatively determining the location of maximum deflection and high-stress regions. This enables subsequent gradient optimization design to specifically adjust stiffness in deformation-sensitive and high-stress regions, providing a precise mechanical basis for the gradient distribution design of functionally graded materials and improving the targeting and effectiveness of ram structure optimization.

[0033] S120. Establish the mapping relationship between the material composition distribution and equivalent elastic parameters of functionally graded materials, wherein functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus.

[0034] This invention provides a method for optimizing the slide of a milling machine based on functionally graded materials (FJCTs). The method establishes a mapping relationship between the material composition distribution and equivalent elastic parameters of FJCTs. Specifically, it includes: defining the axial direction of the slide component as the gradient direction and selecting a gradient distribution function to describe the spatial variation of the volume fraction of the hard phase material along the gradient direction; the gradient distribution function is a power function; based on inclusion theory and introducing a modified mean field model, a quantitative mapping relationship is established between the local volume fraction of the hard phase material and the macroscopic equivalent elastic parameters; the gradient distribution function is parameterized to quantify the influence weight of the gradient coefficient on the equivalent elastic parameters; and, combined with the geometric parameters of the slide component, an integral expression between the local modulus gradient and bending stiffness is established based on beam theory to determine the mechanism by which local modulus control optimizes overall stiffness.

[0035] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided, wherein the elastic modulus of the material at the fixed end of the slide component is higher than that at the distal end.

[0036] Specifically, the axial direction of the slide block is defined as the gradient direction, and a power-law gradient distribution is selected to describe the spatial variation of the volume fraction of the hard phase. Based on Eshelby inclusion theory and introducing a modified Mori-Tanaka model, a mapping relationship between material composition distribution and equivalent elastic modulus is constructed. The gradient distribution function is parameterized, and the influence weight of the gradient coefficient on the equivalent elastic modulus is quantified. Combining the geometric parameters such as the shape and size of the target structure, and based on the governing equations of functionally graded materials and Timoshenko beam theory, an integral expression between the local modulus gradient and bending stiffness is established, clarifying the mechanism by which local modulus control optimizes overall stiffness.

[0037] Introducing normalized coordinates ( For the fixed side of the ram, (For the extended side), among which, The axial distance between a point on the ram and the fixed side of the ram; This is the axial length of the slide.

[0038] The expression for the power function distribution is: in, .in This represents the volume fraction of the hard phase. This represents the volume fraction of the soft phase.

[0039] Elastic modulus of functionally graded composite materials It can be represented as: in: , and These represent the average stresses of the hard and soft phases, respectively. and These represent the average strain of the hard phase and the soft phase, respectively.

[0040] According to the theory of mechanics of materials, the deformation of the front end face of the ram... It can be represented as: in: For uniformly distributed loads, The change in elastic modulus represents the functionally graded composite material. Represents the area moment of inertia. This indicates the distribution of bending stiffness.

[0041] This embodiment defines the ram axis as the gradient direction and selects a power function as the gradient distribution function. It establishes a quantitative mapping relationship between the volume fraction of the hard phase material and the macroscopic equivalent elastic parameters by combining inclusion theory and a modified mean field model. Then, it parameterizes the gradient distribution function and establishes an integral expression between the modulus gradient and bending stiffness based on beam theory. This achieves a stiffness gradient distribution where the elastic modulus at the fixed end of the ram component is higher than that at the distal end. This allows the material stiffness to accurately match the bending moment distribution under ram working conditions along the axial direction, providing an accurate material performance control model for subsequent collaborative optimization aimed at minimizing end deformation.

[0042] S130. Based on the mapping relationship, with the end deformation in the optimization objective as the optimization direction, the gradient distribution function of the functionally graded material is optimized to obtain the optimal material gradient distribution function.

[0043] According to the present invention, a milling machine ram optimization method based on functionally graded materials is provided. The method optimizes the gradient distribution function of the functionally graded materials, specifically including: establishing a material-structure multi-constraint collaborative optimization model, integrating the parameterized material property field into the dynamic finite element model; taking the minimization of the end deformation of the ram component as the optimization objective, and taking the maximum working stress of the ram component not exceeding the allowable stress of the material and the gradient distribution function meeting the manufacturing process requirements as constraints, the gradient distribution function is collaboratively optimized and solved to obtain the optimal gradient distribution model.

[0044] Specifically, a multi-constraint co-optimization model of materials and structures is established, integrating parameterized material property fields into the dynamic finite element model. The optimization objective is to minimize the end deformation of the ram component, with constraints including that the maximum working stress of the ram component does not exceed the allowable stress of the material and that the gradient distribution function meets manufacturing process requirements. The gradient distribution function is then co-optimized and solved. Design variables include the power exponent in the power function and the range of values ​​for the hard phase volume fraction. The constraints ensure that the optimized material gradient distribution satisfies both structural strength requirements and can be achieved through subsequent time-controlled casting processes. By solving the optimization model, the gradient distribution model that optimizes the overall mechanical performance of the ram is obtained. This model outputs the optimal volume fraction ratio of hard and soft phase materials at various positions along the ram's axial direction.

[0045] This embodiment establishes a material-structure multi-constraint collaborative optimization model, integrating parameterized material property fields into a dynamic finite element model. With the goal of minimizing end deformation and constraints of stress not exceeding allowable stress and gradient distribution conforming to manufacturing processes, the gradient distribution function is collaboratively optimized to obtain an optimal material gradient distribution model that balances structural strength and manufacturing feasibility. This allows the ram's stiffness distribution to accurately match the mechanical requirements under working conditions, effectively suppressing end deformation while meeting strength constraints.

[0046] S140. Based on the optimal material gradient distribution function, control the manufacturing process to form the slide ram component.

[0047] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided. The method controls the manufacturing process to form the slide component. Specifically, it includes: using a casting process, injecting molten materials with different compositions for forming hard phase materials and soft phase materials into a multi-channel gating system through timing control; and controlling the injection sequence and proportion of the molten materials so that the material composition of the formed slide component changes continuously or quasi-continuously along the axial direction.

[0048] According to the present invention, a method for optimizing the slide of a milling machine based on functionally graded materials is provided, which controls the manufacturing process to form the slide component, and further includes: performing rough machining, heat treatment and finish machining on the cast slide blank in sequence to complete the preparation of the slide component.

[0049] Specifically, a sand casting process is employed, with a multi-segment independent ingate system designed within the additively manufactured sand mold. One ingate connects to alloyed molten iron for forming the hard phase material, while the other connects to the base component molten iron for forming the soft phase material. Using a program-controlled tilting platform or a timed solenoid valve assembly, the two molten irons with different compositions are injected in stages according to a preset sequence and proportion. First, the high-elastic-modulus hard phase material melt is injected to form the fixed-end region. Subsequently, the proportion of the soft phase material melt is gradually increased, causing the material composition to exhibit a continuous or quasi-continuous gradient change along the slide ram axis from the fixed end to the distal end. The resulting functionally graded material slide ram blank undergoes rough machining to remove the gating system and excess material, followed by stress-relieving aging heat treatment to eliminate casting internal stress. Finally, a finishing process is performed to achieve the designed dimensions and surface accuracy requirements, ultimately completing the fabrication of a slide ram component with a stiffness gradient distribution.

[0050] This embodiment employs a casting process and utilizes a time-controlled multi-channel gating system to inject molten materials of different compositions in stages according to a preset sequence and proportion. This allows the material composition to change continuously or quasi-continuously along the ram's axial direction from the fixed end to the distal end in a gradient. Combined with post-processing steps such as rough machining, heat treatment, and finish machining, a gradient distribution of the ram component's material stiffness along the axial direction from the fixed end to the distal end is achieved. This manufacturing method can accurately reproduce the stiffness distribution law determined by the optimal material gradient distribution function, ensuring that the ram component, while meeting structural strength requirements, possesses stiffness characteristics that match the bending moment distribution under working conditions.

[0051] Figure 2 This is a schematic diagram of the ram of a CNC floor-type milling and boring machine. The diagram shows the ram (…). Figure 3The diagram shows a ram structure, which is a long, narrow box-like structure. Its rear end connects to the spindle box, and it can extend and retract axially relative to the spindle box under the drive mechanism. The front end of the ram is used to mount the spindle system. The spindle motor is located at the corresponding position on the ram or spindle box, driving the spindle rotation through a transmission mechanism. As can be seen from the diagram, the ram extends outward from the spindle box in a cantilever manner. Its fixed end is located at the connection with the spindle box, and the distal end is the extended end for mounting the spindle. This structural feature reveals the mechanical root cause of the ram's pitch deformation under its own weight and cutting forces during operation.

[0052] Figure 4 This diagram illustrates the flexural deformation of a conventional ram. It shows the deformation of a conventional ram made of homogeneous material in its extended state. As can be seen, as the ram extends from the fixed end to the distal end, the deviation of the distal end from the theoretical axis gradually increases, exhibiting a clear downward bending trend, the so-called "head-down" phenomenon. This deformation indicates that the conventional ram, due to its uniform axial stiffness distribution, cannot adapt to the decreasing moment distribution from the fixed end to the distal end in a ram cantilever structure, resulting in relatively insufficient stiffness at the distal end and significant flexural deformation.

[0053] Figure 5 This diagram illustrates the flexural deformation of a ram with a stiffness gradient constructed according to an embodiment of the present invention. The diagram shows the deformation morphology of the ram of the present invention, made of a functionally graded material, under the same working conditions. Figure 4 In contrast, the ram with a stiffness gradient exhibits almost no deviation from the theoretical axis at its distal end, resulting in a smooth deformation curve and effective suppression of overall deflection. This deformation pattern demonstrates that by making the ram material stiffness gradient along the axial direction from the fixed end to the distal end, the stiffness distribution matches the bending moment distribution under ram working conditions, thereby actively suppressing pitch deformation at the structural level and improving the ram's extension accuracy.

[0054] The following describes the milling machine slide optimization device based on functionally graded materials provided by the present invention. The milling machine slide optimization device based on functionally graded materials described below can be referred to in correspondence with the milling machine slide optimization method based on functionally graded materials described above.

[0055] like Figure 6 The image shows a milling machine ram optimization device based on functionally graded materials provided by the present invention, comprising: The finite element model establishment module 610 is used to establish the dynamic finite element model of the ram component, and obtain the deformation field and stress distribution of the ram component by simulating the working state of the ram component under processing load, so as to determine the optimization target. The mapping relationship establishment module 620 is used to establish the mapping relationship between the material composition distribution and the equivalent elastic parameters of functionally graded materials, wherein functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus. The function optimization module 630 is used to optimize the gradient distribution function of the functionally graded material based on the mapping relationship, with the end deformation in the optimization target as the optimization direction, so as to obtain the optimal material gradient distribution function. The slide preparation module 640 is used to control the manufacturing process to form the slide component according to the optimal material gradient distribution function.

[0056] Specifically, the functions of each module in the milling machine slide optimization device based on functionally graded materials provided in this embodiment of the invention correspond one-to-one with the operation flow of each step in the above-mentioned method-like embodiments, and the achieved effects are also the same. For details, please refer to the above embodiments, and this will not be repeated in this embodiment of the invention.

[0057] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communications bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other through the communications bus 740. The processor 710 can call logic instructions in the memory 730 to execute a milling machine ram optimization method based on functionally graded materials. This method includes: establishing a dynamic finite element model of the ram component and obtaining the deformation field and stress distribution of the ram component by simulating its working state under machining load to determine the optimization objective; establishing a mapping relationship between the material composition distribution and equivalent elastic parameters of the functionally graded materials, wherein the functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus; based on the mapping relationship, optimizing the gradient distribution function of the functionally graded materials with the end deformation in the optimization objective as the optimization direction to obtain the optimal material gradient distribution function; and controlling the manufacturing process to form the ram component according to the optimal material gradient distribution function, so that the material stiffness of the ram component changes in a gradient along the axial direction from the fixed end to the far end.

[0058] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the milling machine ram optimization method based on functionally graded materials provided by the above methods. The method includes: establishing a dynamic finite element model of the ram component, and obtaining the deformation field and stress distribution of the ram component by simulating the working state of the ram component under machining load to determine the optimization target; establishing a mapping relationship between the material composition distribution and equivalent elastic parameters of the functionally graded material, wherein the functionally graded material includes a hard phase material with a high elastic modulus and a soft phase material with a low elastic modulus; optimizing the gradient distribution function of the functionally graded material based on the mapping relationship, with the end deformation in the optimization target as the optimization direction, to obtain the optimal material gradient distribution function; and controlling the manufacturing process to form the ram component according to the optimal material gradient distribution function, so that the material stiffness of the ram component changes in a gradient along the axial direction from the fixed end to the far end.

[0060] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the above-described milling machine ram optimization method based on functionally graded materials, which includes: establishing a dynamic finite element model of the ram component and obtaining the deformation field and stress distribution of the ram component by simulating its working state under machining load to determine the optimization objective; establishing a mapping relationship between the material composition distribution and equivalent elastic parameters of the functionally graded material, wherein the functionally graded material includes a hard phase material with a high elastic modulus and a soft phase material with a low elastic modulus; optimizing the gradient distribution function of the functionally graded material based on the mapping relationship, with the end deformation in the optimization objective as the optimization direction, to obtain the optimal material gradient distribution function; and controlling the manufacturing process to form the ram component according to the optimal material gradient distribution function, such that the material stiffness of the ram component changes in a gradient along the axial direction from the fixed end to the far end.

[0061] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0062] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the slide of a milling and boring machine based on functionally graded materials, characterized in that, include: A dynamic finite element model of the ram component is established, and the deformation field and stress distribution of the ram component are obtained by simulating the working state of the ram component under processing load, so as to determine the optimization target. A mapping relationship is established between the material composition distribution and equivalent elastic parameters of functionally graded materials, wherein the functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus; Based on the mapping relationship, with the end deformation in the optimization objective as the optimization direction, the gradient distribution function of the functionally graded material is optimized to obtain the optimal material gradient distribution function; The manufacturing process is controlled to form the slide component based on the optimal material gradient distribution function.

2. The milling machine ram optimization method based on functionally graded materials according to claim 1, characterized in that, The establishment of a dynamic finite element model of the ram component, and the simulation of the working state of the ram component under machining load, specifically includes: Simplify the non-critical features in the three-dimensional solid model of the slide ram component and establish a finite element analysis model of the slide ram component; Select cutting parameters based on preset cutting conditions, calculate cutting load, and determine the upper limit of cutting load; The cutting load is applied to the finite element analysis model, and the spatial deformation field and stress distribution cloud map of the slide block component under typical machining load are obtained through simulation calculation. Based on the spatial deformation field and stress distribution cloud map, the location of the maximum deflection and the high-stress region of the ram component are determined.

3. The milling machine ram optimization method based on functionally graded materials according to claim 1, characterized in that, Establishing a mapping relationship between the material composition distribution and equivalent elastic parameters of functionally graded materials, specifically including: The axial direction of the slide component is defined as the gradient direction, and a gradient distribution function is selected to describe the spatial variation of the volume fraction of the hard phase material along the gradient direction; the gradient distribution function is a power function. Based on inclusion theory and by introducing a modified mean field model, a quantitative mapping relationship between the local volume fraction of the hard phase material and the macroscopic equivalent elastic parameters is established. The gradient distribution function is parameterized to quantify the influence weight of the gradient coefficients on the equivalent elasticity parameter; Based on the geometric parameters of the ram component, an integral expression between the local modulus gradient and bending stiffness is established according to beam theory to determine the mechanism by which local modulus control optimizes overall stiffness.

4. The milling machine ram optimization method based on functionally graded materials according to claim 1, characterized in that, Optimizing the gradient distribution function of the functionally graded material specifically includes: A material-structure multi-constraint collaborative optimization model is established, integrating the parameterized material property field into the dynamic finite element model; With minimizing the end deformation of the ram component as the optimization objective, and with the constraints that the maximum working stress of the ram component does not exceed the allowable stress of the material and that the gradient distribution function meets the manufacturing process requirements, the gradient distribution function is solved through collaborative optimization to obtain the optimal gradient distribution model.

5. The milling machine ram optimization method based on functionally graded materials according to claim 1, characterized in that, The controlled manufacturing process forms the slide ram component, specifically including: A casting process is employed, in which a multi-channel gating system with time-controlled injection is used to separately inject molten materials with different compositions for forming the hard phase material and the soft phase material. The injection sequence and ratio of the molten material are controlled so that the material composition of the ram component after molding changes continuously or quasi-continuously along the axial direction.

6. The milling machine ram optimization method based on functionally graded materials according to claim 5, characterized in that, The controlled manufacturing process for forming the slide component also includes: The cast ram blank is subjected to rough machining, heat treatment and finish machining in sequence to complete the preparation of the ram component.

7. The method for optimizing the slide of a milling and boring machine based on functionally graded materials according to claim 1, characterized in that, The elastic modulus of the material at the fixed end of the ram component is higher than that at the distal end.

8. A milling and boring machine ram optimization device based on functionally graded materials, characterized in that, include: The finite element model building module is used to build a dynamic finite element model of the ram component, and obtain the deformation field and stress distribution of the ram component by simulating the working state of the ram component under processing load, so as to determine the optimization target; The mapping relationship establishment module is used to establish the mapping relationship between the material composition distribution and the equivalent elastic parameters of functionally graded materials, wherein the functionally graded materials include hard phase materials with high elastic modulus and soft phase materials with low elastic modulus; The function optimization module is used to optimize the gradient distribution function of the functionally graded material based on the mapping relationship and with the end deformation in the optimization objective as the optimization direction, so as to obtain the optimal material gradient distribution function. The ram preparation module is used to control the manufacturing process to form the ram component according to the optimal material gradient distribution function.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the milling machine slide optimization method based on functionally graded materials as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the milling machine slide optimization method based on functionally graded materials as described in any one of claims 1 to 7.

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