Lightweight Mechanical Structure Topology Optimization and Additive Manufacturing Integrated System

Abstract

This invention relates to the field of mechanical manufacturing technology, and particularly to an integrated system for topology optimization and additive manufacturing of lightweight mechanical structures. The system includes a topology optimization module, a finite element simulation module, a process parameter calculation module, a manufacturing module, and a printing quality monitoring module. The process parameter calculation module establishes a mapping relationship between material space and structural space based on differential geometry theory. It includes a differential geometry characterization unit, a thermal deformation prediction and compensation unit, and a process parameter mapping and optimization unit, achieving accurate conversion from the design domain to the manufacturing domain. Through a curvature-driven thermal deformation compensation method, it achieves active compensation for thermal deformation based on the geometric essence of the structure. The system employs a thermo-mechanical coupling adaptive control system for process parameters under multi-manifold cross-constraints, enabling real-time monitoring and parameter adjustment of the manufacturing process. This solves the problem that existing topology optimization results are difficult to directly apply to additive manufacturing, significantly improving the manufacturing accuracy and mechanical properties of lightweight mechanical structures.

Description

Technical Field

[0001] This invention relates to the field of mechanical manufacturing technology, and in particular to an integrated system for lightweight mechanical structure topology optimization and additive manufacturing. This system deeply integrates structural design optimization with the additive manufacturing process, realizing full-process digital control from structural design to manufacturing. Background Technology

[0002] With the increasing demand for lightweight structures in high-end manufacturing industries such as aerospace and automotive, topology optimization, as an important means to achieve structural lightweighting, has been widely applied in the design of various mechanical structures. Meanwhile, additive manufacturing (3D printing) technology, due to its advantages such as the ability to manufacture complex structures, reduce assembly steps, and shorten manufacturing cycles, is becoming an ideal means to achieve lightweight structure manufacturing.

[0003] However, there is currently a significant technological gap between topology optimization and additive manufacturing. Traditional topology optimization primarily focuses on structural mechanical properties, often neglecting manufacturing constraints such as thermal deformation and material property changes during additive manufacturing; while additive manufacturing lacks a feedback adjustment mechanism for structural characteristics. This separation of "design-manufacturing" makes it difficult to directly apply topology optimization results to additive manufacturing, compromising manufacturing accuracy and structural performance, and severely restricting the widespread application of lightweight structures.

[0004] Furthermore, thermal deformation during additive manufacturing has always been a key factor affecting manufacturing accuracy. Existing technologies mostly employ empirical compensation or post-processing correction methods, lacking an active compensation mechanism based on the inherent structural characteristics, resulting in limited compensation effectiveness and poor adaptability.

[0005] Therefore, there is an urgent need for an integrated system that can deeply integrate topology optimization and additive manufacturing to achieve intelligent control of the entire process from structural optimization to manufacturing, thereby improving the manufacturing precision and mechanical properties of lightweight structures. Summary of the Invention

[0006] The purpose of this invention is to provide an integrated system for topology optimization and additive manufacturing of lightweight mechanical structures. This system establishes a bidirectional mapping relationship between topology optimization and additive manufacturing, thereby achieving a precise conversion from the design domain to the manufacturing domain. This solves the problem that topology optimization results are difficult to directly apply to additive manufacturing in the prior art, and improves the manufacturing accuracy and performance stability of lightweight structures.

[0007] This invention proposes an integrated system for lightweight mechanical structure topology optimization and additive manufacturing, comprising:

[0008] The structural topology optimization module is used to perform topology optimization on mechanical structures and generate optimized structural solutions.

[0009] The finite element simulation module is connected to the structural topology optimization module and is used to perform mechanical performance analysis on the optimized structural scheme.

[0010] A process parameter calculation module is connected to the structural topology optimization module and the finite element simulation module. The process parameter calculation module includes:

[0011] Differential geometric representation units are used to construct material space manifolds and structural space manifolds, and to establish the mapping relationship between the two manifolds;

[0012] The thermal deformation prediction and compensation unit is used to predict the thermal deformation trend based on the structural curvature characteristics and generate a compensation strategy.

[0013] The process parameter mapping and optimization unit is used to generate optimal process parameters under multi-manifold cross constraints.

[0014] A production module, connected to the process parameter calculation module, is used to perform additive manufacturing based on the process parameters;

[0015] The print quality monitoring module is connected to the production module and the process parameter calculation module, and is used to monitor the manufacturing process and feed back the monitoring data to the process parameter calculation module.

[0016] Preferably, the structural topology optimization module includes:

[0017] The structural parameters and manufacturing constraints input submodule is used to input structural material composition, structural load distribution, structural boundary conditions, and manufacturing constraints.

[0018] The topology optimization submodule is used to perform topology optimization calculations based on the structural parameters and manufacturing constraints.

[0019] The structural improvement submodule is used to improve the manufacturability of the topology optimization results.

[0020] The material selection submodule is used to select suitable materials based on the topology optimization results.

[0021] The performance evaluation submodule is used to evaluate the mechanical properties of mechanical structures.

[0022] The optimization scheme acquisition submodule is used to output the final optimization scheme and process parameters.

[0023] Preferably, the differential geometric representation unit includes:

[0024] Material space manifold building components are used to define a material space containing parameters such as density, strength, and thermal conductivity, and to introduce Riemannian metrics into the material space;

[0025] The structure space manifold building component is used to define a structure space that includes geometry, topological connections, and performance metrics, and introduces the Riemannian metric of the structure space;

[0026] The manifold mapping component is used to construct a mapping function from material space to structure space and optimize the mapping relationship through variational functionals.

[0027] Preferably, the thermal deformation prediction and compensation unit includes:

[0028] The thermal deformation-curvature relationship modeling component is used to analyze the Gaussian curvature and average curvature distribution of structural surfaces and establish the mapping relationship between thermal deformation and curvature.

[0029] Curvature-driven structure optimization components are used to design strategies for the evolution of structural surface morphology based on curvature distribution.

[0030] The thermal deformation prediction and compensation component is used to predict the temperature field distribution based on thermal analysis results, calculate the curvature change caused by the temperature field, and generate an active compensation scheme.

[0031] Preferably, the process parameter mapping and optimization unit includes:

[0032] Multi-manifold space construction components are used to define thermal manifolds, force manifolds, and process parameter manifolds;

[0033] The cross-constraint measure space component is used to define cross measures between manifolds and quantify the mutual influence between different parameters;

[0034] The geodesic parameter optimization component is used to construct energy functionals in parameter space and derive the optimal parameter trajectory.

[0035] An adaptive control component is used to construct an error manifold based on real-time monitoring data and calculate parameter adjustment strategies.

[0036] Preferably, the finite element simulation module includes:

[0037] The data input submodule is used to generate finite element mesh models and boundary conditions based on the optimized structural scheme.

[0038] The finite element simulation submodule is used to calculate the stress distribution parameters and deformation distribution parameters of the structure.

[0039] The performance feedback submodule is used to generate mechanical structure performance parameters based on the finite element analysis results, and to feed the performance parameters back to the structure topology optimization module and the process parameter calculation module.

[0040] Preferably, the manufacturing module includes:

[0041] The optimization scheme input submodule is used to receive mechanical structure parameters from the structure topology optimization module and process parameters from the process parameter calculation module;

[0042] The manufacturing method selection submodule is used to select a manufacturing scheme based on the mechanical structure parameters and the process parameters;

[0043] An additive manufacturing submodule is used to perform additive manufacturing when the manufacturing scheme is additive manufacturing;

[0044] The subtractive manufacturing submodule is used to perform subtractive manufacturing when the manufacturing scheme is subtractive manufacturing.

[0045] The additive and subtractive manufacturing submodule is used to perform additive and subtractive manufacturing when the manufacturing scheme is integrated manufacturing.

[0046] Preferably, the print quality monitoring module includes:

[0047] The material property calculation submodule is used to calculate the material change law during the manufacturing process and the material parameters after thermal deformation compensation based on the process parameters.

[0048] The printing process monitoring submodule is used to monitor the manufacturing process and acquire monitoring data, calculate the deviation from the expected state based on the monitoring data, and feed the deviation back to the process parameter calculation module for parameter adjustment.

[0049] Preferably, the parameter adjustment strategy adopted by the adaptive control component includes:

[0050] An energy input control strategy is used to dynamically adjust the energy density based on the material state tensor.

[0051] A scan path planning strategy is used to generate an adaptive scan path based on the structural curvature distribution.

[0052] Printing speed control strategy, used to dynamically adjust printing speed based on thermal gradient;

[0053] Material supply management strategy, used to dynamically adjust the material supply rate based on cross-sectional characteristics.

[0054] Preferably, the data transfer mechanism established between the differential geometry characterization unit, the thermal deformation prediction and compensation unit, and the process parameter mapping and optimization unit includes:

[0055] The differential geometry characterization unit transmits the material-structure mapping relationship and curvature distribution data to the thermal deformation prediction and compensation unit;

[0056] The thermal deformation prediction and compensation unit transmits the predicted deformation field and compensation strategy to the process parameter mapping and optimization unit.

[0057] The process parameter mapping and optimization unit transmits parameter optimization feedback information to the differential geometry characterization unit and the thermal deformation prediction and compensation unit.

[0058] The beneficial effects of this invention include:

[0059] 1. By using the material-structure mapping theory based on differential geometry, a multi-scale mapping mechanism from the microscopic properties of materials to the macroscopic performance of structures was established. This mechanism breaks through the limitations of traditional linear mapping, accurately describes the nonlinear material behavior in the additive manufacturing process, and improves the accuracy of structural performance prediction.

[0060] 2. By using a curvature-driven thermal deformation compensation method, active compensation for thermal deformation is achieved based on the geometric essence of the structure. Compared with traditional empirical compensation methods, the manufacturing accuracy is improved by 45%-60%, especially in complex curved surface structures.

[0061] 3. Through the thermo-mechanical coupling process parameter adaptive control system under multi-manifold cross constraints, real-time monitoring and parameter adjustment of the manufacturing process were realized, which improved the stability of the manufacturing process by more than 50% and significantly reduced the scrap rate;

[0062] 4. Through the collaborative work of various functional modules, closed-loop control from design to manufacturing is achieved, which improves the structural mechanical performance by 30%-40% while maintaining lightweight design, especially in anisotropic material application scenarios. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the overall architecture of the integrated system for lightweight mechanical structure topology optimization and additive manufacturing of the present invention;

[0064] Figure 2 This is a schematic diagram of the functional architecture of the topology optimization module of the present invention;

[0065] Figure 3 This is a schematic diagram of the functional architecture of the process parameter calculation module of the present invention;

[0066] Figure 4 This is a schematic diagram of the workflow of the differential geometry characterization unit of the present invention;

[0067] Figure 5 This is a schematic diagram of the working process of the thermal deformation prediction and compensation unit of the present invention;

[0068] Figure 6 This is a schematic diagram of the workflow of the process parameter mapping and optimization unit of the present invention;

[0069] Figure 7 This is a schematic diagram of the data flow process of the system of the present invention. Detailed Implementation

[0070] Please refer to Figures 1-7 The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0071] Reference Figure 1 The lightweight mechanical structure topology optimization and additive manufacturing integrated system provided by the present invention includes a structural topology optimization module 1, a finite element simulation module 2, a process parameter calculation module 3, a production and manufacturing module 4, and a printing quality monitoring module 5.

[0072] The structural topology optimization module 1 is used to optimize the topology of the mechanical structure and generate optimized structural schemes. The finite element simulation module 2 is connected to the structural topology optimization module 1 and is used to analyze the mechanical properties of the optimized structural schemes. The process parameter calculation module 3 is connected to both the structural topology optimization module 1 and the finite element simulation module 2 and is used to generate optimal process parameters based on differential geometry theory. The production and manufacturing module 4 is connected to the process parameter calculation module 3 and is used to implement additive manufacturing based on the process parameters. The printing quality monitoring module 5 is connected to both the production and manufacturing module 4 and the process parameter calculation module 3 and is used to monitor the manufacturing process and feed the monitoring data back to the process parameter calculation module 3.

[0073] The various modules of the present invention will be described in detail below:

[0074] Reference Figure 2 The structural topology optimization module 1 includes a structural parameter and manufacturing constraint input submodule 11, a topology optimization submodule 12, a structural improvement submodule 13, a material selection submodule 14, a performance evaluation submodule 15, and an optimization scheme acquisition submodule 16.

[0075] The structural parameter and manufacturing constraint input submodule 11 is used to input the structural material composition, structural load distribution, structural boundary conditions, and manufacturing constraints. In a preferred embodiment of the present invention, the input structural parameters include material property parameters such as material density, elastic modulus, and Poisson's ratio, as well as information such as the geometric boundaries, load distribution, and support locations of the structure. Manufacturing constraints include manufacturability-related restrictions such as minimum wall thickness constraints (typically set to 0.8mm-1.5mm, depending on the accuracy of the additive manufacturing equipment used), maximum overhang angle constraints (typically 45°), and minimum feature size constraints.

[0076] The topology optimization submodule 12 is used to perform topology optimization calculations based on structural parameters and manufacturing constraints. In this invention, a variable density method combined with a moving asymptote algorithm is preferably used for topology optimization, with the optimization objective being to minimize the structural mass while satisfying given constraints. Specifically, the mathematical model for topology optimization can be expressed as:

[0077]

[0078] ,

[0079] in: For design domain points The relative density at (range of values) up to 1); For design domain; The maximum permissible volume ratio is typically set to 30%-50% of the original volume; and These are the maximum stress and the allowable stress, respectively. and These are the maximum displacement and the allowable displacement, respectively. The value is usually set to 0.001 to avoid singularity problems in numerical calculations.

[0080] The structural improvement submodule 13 is used to improve the manufacturability of the topology optimization results. This submodule mainly addresses common issues in the direct results of topology optimization, such as the "checkerboard" phenomenon, discontinuous structures, and non-compliance with manufacturing constraints. Preferably, density filtering and projection methods combined with sensitivity analysis are used for structural improvement to ensure that the final structure meets the process requirements of additive manufacturing.

[0081] The material selection submodule 14 is used to select suitable materials based on the topology optimization results. In a preferred embodiment of the invention, this submodule selects the most suitable material from a preset material library based on the mechanical requirements and thermal deformation characteristics of the structure. The material library includes common metallic materials (such as aluminum alloys, titanium alloys, stainless steel, etc.), polymer materials, and composite materials, each of which has detailed physical and mechanical property parameters recorded.

[0082] The performance evaluation submodule 15 is used to evaluate the mechanical properties of the mechanical structure. This submodule combines the optimized structure with the properties of the selected materials and uses the finite element method to evaluate the structure's performance indicators in terms of static load, dynamic response, and thermal deformation. The performance evaluation results will serve as the basis for determining whether the structure needs further optimization.

[0083] The optimization scheme acquisition submodule 16 is used to output the final optimization scheme and process parameters. Once the structural performance meets the design requirements, this submodule will generate a structural model file in a standard format (such as STL, STEP, etc.) and preliminary process parameter suggestions, providing basic data for the subsequent manufacturing process.

[0084] Reference Figure 1 The finite element simulation module 2 includes a data input submodule 21, a finite element simulation submodule 22, and a performance feedback submodule 23.

[0085] The data input submodule 21 is used to generate a finite element mesh model and boundary conditions based on the optimized structural scheme. In a preferred embodiment of the invention, an adaptive mesh generation technique is employed to automatically refine the mesh in areas with complex structural geometry (such as thin walls, sharp corners, and transition regions), thereby improving analysis accuracy. For typical thin-walled regions of lightweight structures, the mesh size is typically set to 1 / 3 to 1 / 5 of the wall thickness to ensure computational accuracy.

[0086] The finite element simulation submodule 22 is used to calculate the stress distribution parameters and deformation distribution parameters of the structure. This submodule supports various analysis types, including static analysis, modal analysis, thermal analysis, and thermo-mechanical coupling analysis. Preferably, for additive manufacturing processes, the focus is on thermo-mechanical coupling analysis to predict the temperature field distribution, thermal stress distribution, and the resulting deformation field during the manufacturing process.

[0087] The performance feedback submodule 23 is used to generate mechanical structure performance parameters based on the finite element analysis results and feed these parameters back to the structural topology optimization module 1 and the process parameter calculation module 3. Performance parameters include, but are not limited to: maximum stress (accurate to MPa), stress distribution uniformity (expressed in standard deviation), maximum deformation (accurate to μm), deformation distribution, and natural frequency (accurate to Hz). These parameters will serve as the basis for optimization iterations and process parameter adjustments.

[0088] The process parameter calculation module 3 is the core innovation of this invention. Through a theoretical framework based on differential geometry, it establishes a precise mapping between topology optimization results and additive manufacturing process parameters, achieving seamless conversion from the design domain to the manufacturing domain. For example... Figure 3 As shown, the module includes a differential geometry characterization unit 31, a thermal deformation prediction and compensation unit 32, and a process parameter mapping and optimization unit 33.

[0089] The differential geometry representation unit 31 is a fundamental component of the process parameter calculation module 3, used to construct the material space manifold and the structural space manifold, and to establish the mapping relationship between the two manifolds. (Refer to...) Figure 4 The unit includes a material space manifold construction component 311, a structural space manifold construction component 312, and a manifold mapping establishment component 313.

[0090] The material space manifold building component 311 is used to define a material space containing parameters such as density, strength, and thermal conductivity, and to introduce Riemannian metrics into the material space. In a preferred embodiment of the invention, the material space is defined as a multidimensional parameter space M, whose coordinates represent various physical and mechanical properties of the material. For metal additive manufacturing, typical material parameters include density ρ (typically 2.7-8.0 g / cm³), elastic modulus E (typically 70-210 GPa), thermal conductivity k (typically 15-400 W / (m·K)), and coefficient of thermal expansion α (typically 10-25 × 10⁻⁻⁻⁴).6 (e.g., / K)

[0091] To describe the interaction relationships between material parameters, a Riemannian metric is introduced in the material space M. This metric defines the concepts of "distance" and "angle" in material space. Metric matrix The construction is based on the correlation analysis between material parameters, and can be expressed as:

[0092] ,

[0093] in: To measure the components of the tensor, the relationship between the i-th parameter and the j-th parameter in the material space is represented; These are structural performance indicators (such as strength, stiffness, etc.), representing the mechanical response of the structure. , These are material parameters, such as density and elastic modulus. The weighting coefficients reflect the importance of different performance indicators and are usually determined according to the specific application scenario. For example, in load-bearing structures, the weight of the strength indicator can be set to 0.6, the weight of the stiffness indicator can be set to 0.3, and the weight of the thermal deformation indicator can be set to 0.1. This indicates the performance metrics considered. Perform summation.

[0094] The structural space manifold building component 312 is used to define a structural space containing geometry, topological connections, and performance indicators, and introduces a Riemannian metric for the structural space. The structural space S is a multidimensional space describing the geometry and performance characteristics of the structure, and its coordinates represent the morphological features and performance indicators of the structure. In this invention, particular attention is paid to the curvature characteristics of the structural surface, and Gaussian curvature is introduced. and mean curvature As a key parameter characterizing structural morphology.

[0095] In structural space Introducing Riemannian metrics This metric matrix, used to describe the coupling relationship between structural parameters, can be represented as:

[0096] .

[0097] in: The metric tensor components in the structure space represent the structure parameters. and parameters The relationship between them; These are structural response indicators (such as displacement, stress, etc.), representing the behavior of a structure under external loads. , These are structural parameters, such as geometric dimensions and shape characteristics; The weighting coefficients reflect the importance of different structural response indicators. For example, for structures requiring high precision, the displacement indicator weight can be set to 0.7, and the stress indicator weight can be set to 0.3. Indicates the structural response index for all considered Perform summation.

[0098] The manifold mapping component 313 is used to construct a mapping function from material space to structural space and optimize this mapping relationship through variational functionals. This component establishes a mapping function between material properties and structural performance. This mapping maps points in material space to corresponding points in structural space.

[0099] To optimize the mapping relationship, a variational functional is constructed. , representing the "energy" of the mapping, and an ideal mapping should minimize this energy:

[0100] ,

[0101] in: For mapping The energy functional is used to evaluate the quality of a mapping; The tensile energy represents the spatial tensile deformation of the material caused by the mapping, and its unit is J; The bending energy represents the spatial bending deformation of the material caused by the mapping, and its unit is J; is the distortion energy, representing the spatial distortion of the material caused by the mapping, measured in J; , , The weighting coefficients are usually set to , , It can be adjusted according to specific application requirements; Represents the entire material space Integrate on top; It is a volume element in the material space.

[0102] Solving for the optimal mapping function using numerical optimization methods (such as gradient descent, conjugate gradient, etc.) To establish a precise correspondence between material properties and structural performance.

[0103] The thermal deformation prediction and compensation unit 32 is a key component for solving the thermal deformation problem in additive manufacturing. It achieves accurate prediction and active compensation of thermal deformation by analyzing the intrinsic relationship between structural curvature characteristics and thermal deformation. (Refer to...) Figure 5 The unit includes a thermal deformation-curvature relationship modeling component 321, a curvature-driven structure optimization component 322, and a thermal deformation prediction and compensation component 323.

[0104] The thermal deformation-curvature relationship modeling component 321 is used to analyze the Gaussian curvature and average curvature distribution of the structural surface and establish a mapping relationship between thermal deformation and curvature. In this invention, it was found that the thermal deformation mode of a structure is closely related to its curvature distribution, especially for thin-walled structures, where this relationship is more pronounced.

[0105] Gaussian curvature of structural surface and mean curvature They are defined as follows:

[0106] , ,

[0107] in: Gaussian curvature, characterizing the intrinsic geometric properties of a surface, is expressed in units of 1. ; The mean curvature characterizes the extrinsic geometric properties of a surface, with units of 1. ; and Principal curvature represents the degree of curvature of a surface at a point along two orthogonal principal directions, and is measured in units of 1 / 2 ppm. .

[0108] The mapping relationship between thermal deformation and curvature can be expressed as:

[0109] ,

[0110] in: For point The deformation vector at point represents the displacement caused by thermal deformation, in units of . ; and Points Gaussian curvature and mean curvature at the location; For the temperature field, representing a point Temperature at that location, in units of ; The temperature gradient represents the spatial rate of change of the temperature field, with units of . ; Let be a mapping function, representing the relationship between curvature, temperature, and deformation.

[0111] In practical applications, this mapping relationship is obtained through finite element analysis and fitting of experimental data. For a typical metal additive manufacturing process, the relationship between thermal deformation and curvature can be approximated as:

[0112] ,

[0113] in: For point Deformation at point, in units of ; to The fitting coefficients are obtained by fitting experimental data and finite element analysis results. The mean curvature is expressed in units of 1. ; Gaussian curvature, in units of ; The magnitude of the temperature gradient, in units of For typical aluminum alloy structures, the reference values ​​for these coefficients are: , , , , , , (Units and corresponding items).

[0114] The curvature-driven structure optimization component 322 is used to design a structural surface morphology evolution strategy based on curvature distribution. This component, based on the mapping relationship between thermal deformation and curvature, achieves active control of thermal deformation by adjusting the curvature distribution of the structure.

[0115] To achieve this goal, a curvature flow equation is designed to describe the morphological evolution of the structural surface:

[0116] ,

[0117] in: Represents structural surface With evolution parameters The rate of change describes the evolution of the surface morphology; For surface gradient operators, it represents the gradient calculated on a surface; It is an energy functional based on curvature, which characterizes the quality of surface morphology; Gaussian curvature; The mean curvature.

[0118] Energy functional Defined as:

[0119] ,

[0120] in: It is an energy functional used to evaluate the quality of surface morphology; and These are the target average curvature and Gaussian curvature, respectively, which are calculated inversely from the thermal deformation prediction results. and The weighting coefficient is usually set to... , This reflects the relative importance of mean curvature and Gaussian curvature in thermal deformation control; Represents the entire surface of the structure Integrate on top; It represents the area element on the curved surface.

[0121] The thermal deformation prediction and compensation component 323 is used to predict the temperature field distribution based on thermal analysis results, calculate the curvature change caused by the temperature field, and generate an active compensation scheme. This component first predicts the temperature field distribution during the additive manufacturing process based on the thermal analysis results:

[0122] ,

[0123] in: For point At the moment Temperature, in units C; The ambient temperature (usually 20-25 degrees Celsius) C), unit is C; Temperature rise due to the manufacturing process, in units of C is obtained through thermal analysis simulation.

[0124] Then, based on the temperature field prediction results, the curvature change caused by temperature is calculated:

[0125] .

[0126] .

[0127] in: and These are the changes in Gaussian curvature and mean curvature, respectively, with units of... and ; Temperature change, unit: ; The magnitude of the temperature gradient, in units of ; to This is a material-related coefficient; for aluminum alloys, a typical value is... , , , (Units and corresponding items).

[0128] Finally, a reverse curvature adjustment scheme is generated to achieve pre-compensation for thermal deformation:

[0129] ,

[0130] ,

[0131] in: and The compensated curvature distribution is shown in units of [missing information]. and ; and The original curvature distribution is given by the units of... and ; and The change in curvature is expressed in units of 1 / 2 and 2 / 3 respectively. and ; and The compensation coefficient is usually set to... , Slightly larger To take into account the nonlinear behavior of materials.

[0132] The process parameter mapping and optimization unit 33 is a key component that transforms design requirements into specific process parameters. It achieves coordinated optimization of the thermal field, force field, and process parameters by establishing a multi-manifold cross-constraint framework. (Refer to...) Figure 6 The unit includes a multi-manifold space construction component 331, a cross-constraint measure space component 332, a geodesic parameter optimization component 333, and an adaptive control component 334.

[0133] The multi-manifold space construction component 331 is used to define the thermal manifold, force manifold, and process parameter manifold. This component treats the thermal field, force field, and process parameters in the additive manufacturing process as different manifold spaces and establishes their mathematical descriptions.

[0134] The thermal manifold T describes the temperature distribution and heat flow dynamics, with points representing different temperature field configurations. The force manifold F characterizes the stress distribution and deformation state, with points representing different force field configurations. The process parameter manifold P includes parameters such as energy input, scanning speed, and path, with points representing different parameter configurations.

[0135] By introducing appropriate metrics onto these manifolds, for example, on process parameter manifolds, the metric can be defined as:

[0136] ,

[0137] in: For process parameters on the manifold point and points A measure between them, representing their "distance"; and These two points on the manifold of the process parameters represent two different parameter configurations; and Points and points The Each coordinate component represents a specific process parameter (such as laser power, scanning speed, etc.). This is a weight matrix, reflecting the relative importance of different parameters; This indicates that the summation is performed over all combinations of parameters.

[0138] The cross-constraint measure space component 332 is used to define cross measures between manifolds, quantifying the mutual influence between different parameters. This component constructs the cross measures between manifolds. This is a probability measure used to describe the correspondence between points on different manifolds.

[0139] Specifically, cross measure This can be represented as a joint probability distribution:

[0140] ,

[0141] in: For cross-measures, it indicates that the thermal field state is The force field state is The process parameters are as follows: The probability density; For thermal manifold A point on the top indicates a specific temperature field configuration; Force field manifold A point on the top indicates a specific force field configuration; For process parameter manifold The dot above indicates a specific parameter configuration; Represents probability; , , These represent tiny regions on the corresponding manifolds.

[0142] Based on cross-measure, define the entropy function. Characterizing the uncertainty of the system:

[0143] ,

[0144] in: Entropy is the function that quantifies the uncertainty of a system, and its unit is bit. For cross-measure; Represents the natural logarithm; This indicates that the integration is performed on the Cartesian product of the three manifolds.

[0145] The geodesic parameter optimization component 333 is used to construct an energy functional in the parameter space and derive the optimal parameter trajectory. This component constructs the energy functional... Characterizing the quality of parameter trajectories: ,in: For energy functionals, evaluate parameter trajectories The degree of superiority or inferiority, expressed in J; For curves in parameter space, These are the parameters of the curve; The kinetic energy term of the curve is a measure on the manifold using process parameters. Calculation, representing the "rate" of parameter change; This is the potential energy term, representing the quality of the parameter configuration; the smaller the value, the better the configuration. Indicates in the parameter Integrate within the range of 0 to 1.

[0146] The Euler-Lagrange equation is derived using the variational method to describe the optimal parameter trajectory.

[0147] ,

[0148] in: For parameters Regarding curve parameters The second derivative of represents the acceleration of the parameter; The Christopher notation represents the connection coefficient of the process parameter manifold, describing the local geometric properties of the surface; and For parameters and about The first derivative of represents the rate of change of the parameter; For measuring tensors The reverse; Potential energy Regarding parameters The partial derivative of represents the gradient of the potential field.

[0149] The adaptive control component 334 is used to construct an error manifold based on real-time monitoring data and calculate parameter adjustment strategies. This component first constructs the error manifold based on the real-time monitoring data. , indicating the deviation between the actual state and the expected state:

[0150] ,

[0151] in: For a moment The error vector represents the deviation between the actual state and the expected state; This refers to the actual state, including monitoring parameters such as temperature and deformation; The expected state is the ideal parameter value predicted by the model.

[0152] Then, geodesics are calculated on the error manifold to determine the optimal correction direction:

[0153] ,

[0154] in: For error components Regarding time The second derivative; Christopher's notation on the error manifold; and For error components and Regarding time The first derivative.

[0155] Finally, based on the geodesic calculation results, a parameter adjustment strategy is generated:

[0156] ,

[0157] in The parameter adjustment amount indicates the correction that needs to be made to the process parameters; This is the step size factor, usually set to 0.1-0.5, and can be dynamically adjusted according to the magnitude of the error; Let be the gradient of the error with respect to the parameters, representing the sensitivity of the error to changes in the parameters.

[0158] Manufacturing module 4 is the execution unit for implementing additive manufacturing, as shown in the reference. Figure 1 This module includes an optimization scheme input submodule 41, a manufacturing method selection submodule 42, an additive manufacturing submodule 43, a subtractive manufacturing submodule 44, and an additive-subtractive integrated manufacturing submodule 45.

[0159] The optimization scheme input submodule 41 is used to receive mechanical structure parameters from the structural topology optimization module 1 and process parameters from the process parameter calculation module 3. This submodule parses and preprocesses the structural geometry data (usually in STL format) and process parameters (including energy input, scanning speed, scanning path, etc.) to prepare for the subsequent manufacturing process.

[0160] The manufacturing method selection submodule 42 is used to select a manufacturing scheme based on mechanical structure parameters and process parameters. Depending on the complexity of the structure, precision requirements, and material properties, this submodule intelligently selects the most suitable manufacturing method. Typically, for highly complex lightweight structures, additive manufacturing is preferred; for functional surfaces requiring high surface quality, a combination of additive and subtractive manufacturing may be chosen.

[0161] The additive manufacturing submodule 43 is used to perform additive manufacturing when the manufacturing scheme is additive manufacturing. This submodule supports various additive manufacturing technologies, including selective laser melting (SLM), electron beam melting (EBM), and laser stereoforming (LSF). Based on the parameters provided by the process parameter calculation module 3, it controls the power, scanning speed, and path of the energy source (such as laser or electron beam), as well as the powder / filament supply rate, to achieve high-precision additive manufacturing.

[0162] Subtractive manufacturing submodule 44 is used to perform subtractive manufacturing when the manufacturing scheme is subtractive manufacturing. This submodule is mainly used for manufacturing parts with high precision requirements and relatively simple structures, or as a finishing step after additive manufacturing.

[0163] The additive-subtractive hybrid manufacturing submodule 45 is used to perform additive-subtractive hybrid manufacturing when the manufacturing scheme is hybrid manufacturing. This hybrid manufacturing method combines the complex structure forming capability of additive manufacturing with the high precision characteristics of subtractive manufacturing, and is suitable for parts that require both complex internal structures and high surface quality.

[0164] Print quality monitoring module 5 is a key component for ensuring manufacturing quality, referencing Figure 1 This module includes a material property calculation submodule 51 and a printing process monitoring submodule 52.

[0165] The material property calculation submodule 51 is used to calculate the material change patterns during the manufacturing process and the material parameters after thermal deformation compensation based on process parameters. This submodule, based on materials science and thermodynamic principles and combined with real-time process parameters, predicts the microstructure, mechanical properties, and geometric deformation of materials during additive manufacturing. For example, for metal additive manufacturing, this submodule can predict microscopic characteristics such as grain size, phase composition, and residual stress, as well as the resulting changes in macroscopic mechanical properties.

[0166] The printing process monitoring submodule 52 monitors the manufacturing process and acquires monitoring data. Based on this data, it calculates the deviation from the expected state and feeds this deviation back to the process parameter calculation module 3 for parameter adjustment. This submodule uses various sensors (such as infrared cameras, high-speed cameras, force sensors, etc.) to monitor key parameters in the printing process in real time, including melt pool size (typically 0.1-0.5 mm), temperature distribution (typically 1400-1600°C for metallic materials), and cooling rate (typically 10³-10⁻⁶). 5 (°C / s), etc. After the monitoring data is processed, the deviation from the expected state is calculated, forming a closed-loop control system.

[0167] Reference Figure 7 The data flow and collaborative working process between the modules in this invention system is as follows:

[0168] First, the structural topology optimization module 1 receives the structural parameters and manufacturing constraints input by the user, performs topology optimization calculations, and generates a preliminary optimized structural scheme.

[0169] Then, the finite element simulation module 2 receives the optimized structural scheme, establishes a finite element model, performs mechanical performance analysis, and feeds back the analysis results to the structural topology optimization module 1 and the process parameter calculation module 3.

[0170] Next, the process parameter calculation module 3, based on the optimized structural scheme and finite element analysis results, applies differential geometry theory to construct the material-structure mapping relationship, predict the thermal deformation trend, generate compensation strategies, and optimize the process parameter configuration. Specifically, the differential geometry characterization unit 31 transmits the material-structure mapping relationship and curvature distribution data to the thermal deformation prediction and compensation unit 32; the thermal deformation prediction and compensation unit 32 transmits the predicted deformation field and compensation strategy to the process parameter mapping and optimization unit 33; and the process parameter mapping and optimization unit 33 transmits parameter optimization feedback information to the differential geometry characterization unit 31 and the thermal deformation prediction and compensation unit 32.

[0171] Subsequently, the production module 4 receives the optimized structural scheme and process parameters, selects the appropriate manufacturing method, and executes the additive manufacturing or hybrid manufacturing process.

[0172] Finally, the printing quality monitoring module 5 monitors the manufacturing process in real time, acquires monitoring data, calculates the deviation from the expected state, and feeds back the deviation to the process parameter calculation module 3, forming a closed-loop control system.

[0173] The application effect of the system of the present invention will be illustrated by a specific embodiment below.

[0174] A certain aircraft engine bracket requires lightweight design and manufacturing. This bracket is a typical load-bearing structure, originally designed to weigh 2.5 kg, and made of TC4 titanium alloy. The process of lightweight design and manufacturing using the system of this invention is as follows:

[0175] First, in the structural topology optimization module 1, the structural parameters of the support (including geometric boundaries, load conditions, support positions, etc.) and manufacturing constraints (minimum wall thickness of 1.2mm, maximum overhang angle of 45°) are input. Through topology optimization calculations, an optimized structural scheme with a 42% reduction in volume is obtained.

[0176] Then, in the finite element simulation module 2, static and modal analyses were performed on the optimized structural scheme, verifying that it met the strength requirements (maximum stress of 685 MPa, less than the material yield strength of 950 MPa) and stiffness requirements (maximum deformation of 0.28 mm, less than the allowable deformation of 0.5 mm).

[0177] Next, in the process parameter calculation module 3, the differential geometry characterization unit 31 established a mapping relationship between the material properties of TC4 titanium alloy and the performance of the support structure. Through the thermal deformation prediction and compensation unit 32, the curvature distribution of the support surface was analyzed, the thermal deformation trend was predicted (the maximum deformation is expected to be 0.35 mm), and a compensation strategy was generated. The process parameter mapping and optimization unit 33 optimized the process parameter configuration, including laser power (180 W), scanning speed (900 mm / s), and scanning path (curvature-based adaptive path), within a multi-manifold cross-constraint framework.

[0178] Subsequently, in manufacturing module 4, selective laser melting (SLM) technology was selected for manufacturing. During the manufacturing process, the print quality monitoring module 5 monitored the temperature and size of the molten pool in real time and found that the temperature in some areas was too high (above 1650°C). Through a feedback mechanism, the process parameter calculation module 3 dynamically adjusted the laser power and scanning speed in these areas to ensure manufacturing quality.

[0179] The final manufactured support weighed 1.45 kg, a 42% reduction compared to the original design. Mechanical performance tests showed that the optimized support met the design requirements in terms of both strength and stiffness, and achieved high manufacturing precision (maximum dimensional deviation of ±0.15 mm). Compared to traditional methods, this system significantly improved manufacturing precision (approximately 55% improvement) and structural performance (approximately 35% increase in strength for the same weight).

[0180] As can be seen from the above embodiments, the lightweight mechanical structure topology optimization and additive manufacturing integrated system provided by the present invention achieves deep integration of topology optimization and additive manufacturing through the material-structure mapping theory based on differential geometry, the curvature-driven thermal deformation compensation method, and the thermo-mechanical coupling process parameter adaptive control system under multi-manifold cross constraints. This significantly improves the manufacturing accuracy and mechanical properties of lightweight structures.

[0181] The above description is merely a preferred embodiment of the present invention and does not limit the scope of patent protection of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of the present invention.

Claims

1. A lightweight mechanical structure topology optimization and additive manufacturing integrated system, characterized in that, Comprise: A structure topology optimization module for topology optimization of mechanical structures and generating optimized structure schemes; A finite element simulation module connected with the structure topology optimization module for mechanical performance analysis of the optimized structure schemes; A process parameter calculation module connected with the structure topology optimization module and the finite element simulation module, the process parameter calculation module comprising: A differential geometry characterization unit for constructing material space manifold and structure space manifold, and establishing a mapping relationship between the two manifolds; A thermal deformation prediction and compensation unit for predicting thermal deformation trends based on structure curvature characteristics and generating compensation strategies; A process parameter mapping and optimization unit for generating optimal process parameters under multi-manifold intersection constraints; A production and manufacturing module connected with the process parameter calculation module for implementing additive manufacturing according to the process parameters; A printing quality monitoring module connected with the production and manufacturing module and the process parameter calculation module for monitoring the manufacturing process and feeding back monitoring data to the process parameter calculation module. 2.The lightweight mechanical structure topology optimization and integrated additive manufacturing system according to claim 1, characterized in that, The structure topology optimization module comprises: A structure parameter and manufacturing constraint input sub-module for inputting structure material composition, structure load distribution, structure boundary conditions and manufacturing constraints; A topology optimization sub-module for topology optimization calculation according to the structure parameters and manufacturing constraints; A structure improvement sub-module for manufacturability improvement of topology optimization results; A material selection sub-module for selecting suitable materials according to topology optimization results; A performance evaluation sub-module for evaluating the mechanical performance of the mechanical structure; An optimized scheme acquisition sub-module for outputting the final optimized scheme and process parameters. 3.The lightweight mechanical structure topology optimization and integrated additive manufacturing system of claim 1, wherein, The differential geometry characterization unit comprises: A material space manifold construction component for defining a material space containing density, strength and thermal conductivity parameters, and introducing a Riemann metric on the material space; A structure space manifold construction component for defining a structure space containing geometric shape, topological connection and performance index, and introducing a Riemann metric of the structure space; A manifold mapping establishment component for constructing a mapping function from the material space to the structure space, and optimizing the mapping relationship through a variational functional. 4.The lightweight mechanical structure topology optimization and integrated additive manufacturing system of claim 1, wherein, The thermal deformation prediction and compensation unit comprises: A thermal deformation-curvature relationship modeling component for analyzing the Gaussian curvature and mean curvature distribution of the structure surface, and establishing a mapping relationship between thermal deformation and curvature; A curvature-driven structure optimization component for designing structure surface morphology evolution strategies based on curvature distribution; A thermal deformation prediction and compensation component for predicting temperature field distribution based on thermal analysis results, calculating curvature changes caused by temperature field, and generating active compensation schemes. 5.The lightweight mechanical structure topology optimization and integrated additive manufacturing system of claim 1, wherein, The process parameter mapping and optimization unit comprises: A multi-manifold space construction component for defining thermal field manifold, force field manifold and process parameter manifold; An intersection constraint measure space component for defining intersection measures between manifolds, quantifying the mutual influence between different parameters; A geodesic parameter optimization component for constructing an energy functional in the parameter space and deriving an optimal parameter trajectory; An adaptive control component for constructing an error manifold based on real-time monitoring data, and calculating parameter adjustment strategies. 6.The lightweight mechanical structure topology optimization and integrated additive manufacturing system of claim 1, wherein, The finite element simulation module comprises: The data input sub-module is configured to generate a finite element grid model and boundary conditions according to an optimization structure scheme; The finite element simulation sub-module is configured to calculate stress distribution parameters and deformation distribution parameters of the structure; The performance feedback sub-module is configured to generate mechanical structure performance parameters according to the finite element analysis results, and feed back the performance parameters to the structure topology optimization module and the process parameter calculation module. 7.The lightweight mechanical structure topology optimization and additive manufacturing integrated system according to claim 1, characterized in that, The production manufacturing module comprises: An optimization scheme input sub-module configured to receive mechanical structure parameters from the structure topology optimization module and process parameters from the process parameter calculation module; A manufacturing method selection sub-module configured to select a manufacturing scheme according to the mechanical structure parameters and the process parameters; An additive manufacturing sub-module configured to perform additive manufacturing when the manufacturing scheme is additive manufacturing; A subtractive manufacturing sub-module configured to perform subtractive manufacturing when the manufacturing scheme is subtractive manufacturing; An additive-subtractive hybrid manufacturing sub-module configured to perform additive-subtractive hybrid manufacturing when the manufacturing scheme is hybrid manufacturing. 8.The lightweight mechanical structure topology optimization and integrated additive manufacturing system of claim 1, wherein, The printing quality monitoring module comprises: A material performance calculation sub-module configured to calculate material variation rules and material parameters after thermal deformation compensation during the manufacturing process according to the process parameters; A printing process monitoring sub-module configured to monitor the manufacturing process and obtain monitoring data, calculate deviations from expected states according to the monitoring data, and feed back the deviations to the process parameter calculation module for parameter adjustment. 9.The lightweight mechanical structure topology optimization and additive manufacturing integrated system of claim 5, wherein, The parameter adjustment strategy adopted by the adaptive control assembly comprises: An energy input control strategy configured to dynamically adjust the energy density based on the material state tensor; A scanning path planning strategy configured to generate an adaptive scanning path based on the structure curvature distribution; A printing speed regulation strategy configured to dynamically adjust the printing speed based on the thermal gradient; A material supply management strategy configured to dynamically adjust the material supply rate based on the cross-section characteristics.

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