A method and system for optimizing the structure of a ductile cast iron casting

Abstract

The application discloses a spheroidal graphite casting structure optimization design method and system, relates to the spheroidal graphite casting design technical field, and comprises the following steps: collecting dynamic load data, combining a casting three-dimensional model to carry out finite element analysis, generating a stress distribution nephogram and marking high fatigue risk area coordinates; according to the stress distribution nephogram, combining wall thickness distribution data, dividing the casting into three functional gradient layers of a surface layer, a transition layer and a core, and defining component parameter ranges and performance target threshold values of the functional gradient layers, generating a partition coordinate mapping relationship; according to the partition coordinate mapping relationship, executing alloy presetting in a specified area of a casting mold, and releasing local inoculants at positions corresponding to the high fatigue risk area coordinates; the application realizes accurate simulation of actual stress conditions of the casting under complex working conditions by collecting dynamic load data and combining the casting three-dimensional model to carry out finite element analysis, and achieves the purpose of improving the reliability and service life of the casting.

Description

Technical Field

[0001] This invention relates to the field of ductile iron casting design technology, and in particular to a method and system for optimizing the structure of ductile iron castings. Background Technology

[0002] Since its invention in the 1940s, ductile iron has been widely used in various fields due to its excellent mechanical properties and casting processability. With the growth of industrial demands and technological advancements, the requirements for ductile iron castings have also increased, demanding not only superior strength and toughness but also stable performance under complex working environments. In recent years, the development of computer simulation technology, especially the application of multiphysics coupling simulation technology, has made it possible to more accurately predict the internal stress distribution of castings, providing new ideas and methods for the design of ductile iron castings.

[0003] Despite advancements in existing ductile iron casting design methods, several challenges remain. First, traditional methods struggle to accurately predict the actual service life of castings under complex dynamic loading conditions, as they often neglect the interaction between dynamic loads and material microstructure. Second, current design processes lack systematicity and automation, particularly in alloy pre-placement and local inoculant release, which frequently require significant manual intervention, leading to inefficiency and inconsistent results. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a structural optimization design method for ductile iron castings to solve the problems of inaccurate fatigue life prediction under dynamic load and low automation in the alloy and inoculant control process in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for optimizing the structural design of ductile iron castings, comprising: acquiring dynamic load data, performing finite element analysis in conjunction with a three-dimensional model of the casting, generating a stress distribution cloud map and marking the coordinates of high fatigue risk areas; dividing the casting into three functional gradient layers—surface layer, transition layer, and core layer—based on the stress distribution cloud map and wall thickness distribution data, defining the composition parameter range and performance target threshold for each functional gradient layer, and generating a partition coordinate mapping relationship; performing alloy pre-setting in a designated area of ​​the mold according to the partition coordinate mapping relationship, and simultaneously releasing a local inoculant at the corresponding position of the high fatigue risk area coordinates; performing a gradient solidification process based on the local inoculant and detecting the core density and microstructure morphology of the risk area of ​​the casting, and correcting the composition parameter range and performance target threshold based on the detection results.

[0008] As a preferred embodiment of the ductile iron casting structure optimization design method of the present invention, the dynamic load data includes time-domain dynamic force load, load spatial distribution coordinates and ambient temperature load coupled data.

[0009] As a preferred embodiment of the ductile iron casting structural optimization design method of the present invention, the specific steps for generating the stress distribution cloud map are as follows:

[0010] Dynamic load data is collected, and a spatiotemporally correlated dynamic load spectrum is generated through empirical mode decomposition and extrapolation of generalized Pareto distribution. This spectrum is then mapped to the surface mesh nodes of the three-dimensional model of the casting through a shape function matrix.

[0011] The mapped dynamic load spectrum is input into the finite element solver, and the dynamic stress-strain field is obtained by combining the constitutive properties of the material, generating a stress distribution cloud map.

[0012] As a preferred embodiment of the ductile iron casting structure optimization design method of the present invention, the marking of high fatigue risk area coordinates refers to calculating multiaxial fatigue damage parameters based on stress distribution cloud map using the critical plane method, and identifying high damage areas according to damage threshold to generate high fatigue risk area coordinates.

[0013] As a preferred embodiment of the ductile iron casting structural optimization design method of the present invention, the specific steps for generating the partition coordinate mapping relationship are as follows:

[0014] Based on the stress distribution cloud map and combined with the wall thickness distribution data, the partition boundaries of the surface layer, transition layer and core are generated by the stress-wall thickness coupling optimization algorithm.

[0015] Based on the partition boundaries, the target thresholds for hardness of the surface layer, toughness of the transition layer, and density of the core are defined, and the ranges for antimony content of the surface layer, molybdenum-copper content of the transition layer, and carbon equivalent of the core are obtained through the materials genome database.

[0016] Based on the range of component parameters and partition boundaries, a gradient component spatial distribution function is constructed to generate partition coordinate mapping relationships.

[0017] As a preferred embodiment of the ductile iron casting structure optimization design method of the present invention, the specific steps of performing alloy presetting in a specified area of ​​the mold according to the partition coordinate mapping relationship are as follows:

[0018] Based on the partition coordinate mapping relationship, the optimal sound pressure amplitude and optimal sound wave frequency are calculated through the sound-heat coupling equation, and the piezoelectric transducer array is driven to generate a focused sound field.

[0019] Based on the focused sound field, combined with the synergistic effect of the laser heat source, differentiated alloy materials are pre-placed in a designated area of ​​the casting mold.

[0020] As a preferred embodiment of the ductile iron casting structural optimization design method of the present invention, the specific steps of simultaneously releasing a local inoculant at the coordinates corresponding to the high fatigue risk area are as follows:

[0021] Based on the coordinates of the high fatigue risk area, piezoelectric microcapsules are implanted at the corresponding positions in the mold, and local phase field values ​​are collected in real time. When the local phase field value exceeds the critical threshold, a PID control voltage signal is generated.

[0022] When the PID control voltage reaches the trigger threshold, the piezoelectric microcapsule ruptures and releases a local inoculant.

[0023] As a preferred embodiment of the ductile iron casting structural optimization design method of the present invention, the specific steps of performing a gradient solidification process based on a local inoculant and detecting the core density and microstructure morphology of the risk zone of the casting are as follows:

[0024] A gradient solidification process is performed based on the released local inoculant, while an acousto-optic coupling field is applied simultaneously and a holographic image of the solidification front is acquired.

[0025] Phase-field-light transport joint inversion was performed on the holographic image of the solidification front to detect the density of the heart and the microstructure of the risk area in real time.

[0026] As a preferred embodiment of the ductile iron casting structural optimization design method of the present invention, the specific steps for correcting the composition parameter range and performance target threshold based on the test results are as follows:

[0027] Based on the detected cardiac density and microstructure morphology data of the risk area, a parametric posterior probability distribution model was constructed.

[0028] Input the posterior probability distribution model of the parameters into the Bayesian-phase-field joint inversion algorithm to calculate the correction amount of the casting process parameters;

[0029] Based on the correction amount of casting process parameters, the range of composition parameters and performance target thresholds are dynamically adjusted.

[0030] Secondly, this invention provides a structural optimization design system for ductile iron castings, comprising a load analysis module, a gradient layering module, a material pre-setting module, and a solidification optimization module. The load analysis module collects dynamic load data, performs finite element analysis using a three-dimensional model of the casting, generates a stress distribution cloud map, and marks the coordinates of high fatigue risk areas. The gradient layering module, based on the stress distribution cloud map and wall thickness distribution data, divides the casting into three functional gradient layers: a surface layer, a transition layer, and a core layer. It defines the composition parameter range and performance target threshold for each functional gradient layer and generates a partition coordinate mapping relationship. The material pre-setting module, based on the partition coordinate mapping relationship, performs alloy pre-setting in a designated area of ​​the mold and releases a local inoculant at the corresponding position in the high fatigue risk area. The solidification optimization module, based on the local inoculant, performs a gradient solidification process and detects the core density and microstructure morphology of the risk area of ​​the casting, correcting the composition parameter range and performance target threshold based on the detection results.

[0031] The beneficial effects of this invention are as follows: by collecting dynamic load data and combining it with the three-dimensional model of the casting for finite element analysis, the actual stress condition of the casting under complex working conditions is accurately simulated, thereby improving the reliability and service life of the casting; based on the stress distribution cloud map and wall thickness distribution data, the casting is divided into functional gradient layers with different performance requirements, which specifically meets the requirements of different parts for hardness, toughness and density, greatly improving the uniformity and stability of the overall performance of the casting. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart for the structural optimization design method of ductile iron castings.

[0034] Figure 2 This is a flowchart for dynamic load analysis.

[0035] Figure 3 A flowchart for dividing the functional gradient layers.

[0036] Figure 4 A flowchart for controlling the release of local progestin. Detailed Implementation

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0038] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0040] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for optimizing the structure of ductile iron castings, comprising the following steps:

[0041] S1: Collect dynamic load data, combine it with the three-dimensional model of the casting to perform finite element analysis, generate stress distribution cloud map and mark the coordinates of high fatigue risk areas.

[0042] Dynamic load data is collected, and a spatiotemporally correlated dynamic load spectrum is generated through empirical mode decomposition and generalized Pareto distribution extrapolation. This spectrum is then mapped to the surface mesh nodes of the casting's 3D model via a shape function matrix.

[0043] The specific process includes: after collecting dynamic load data, using empirical mode decomposition to decompose the dynamic load data into intrinsic mode functions and residual terms, combining the generalized Pareto distribution to extrapolate the extreme loads, generating a spatiotemporally correlated dynamic load spectrum, and using the shape function matrix to map the spatial distribution characteristics of the dynamic load spectrum to the surface mesh nodes of the casting 3D model to ensure that the load application position and intensity are accurately matched on the casting 3D model.

[0044] Furthermore, the pre-training process of the casting 3D model first establishes a geometric model and meshes it based on finite element analysis software, then imports material property parameters and boundary conditions, and obtains the static stress field and modal response through the solver. Then, machine learning algorithms are used to extract features and perform correlation analysis on historical process data and simulation results, and finally generate a casting 3D model that can predict stress distribution and microstructure evolution.

[0045] The mapped dynamic load spectrum is input into the finite element solver, and the dynamic stress-strain field is obtained by combining the constitutive properties of the material, generating a stress distribution cloud map.

[0046] The specific process includes inputting the mapped dynamic load spectrum into the finite element solver, combining the elastic modulus, Poisson's ratio and yield strength parameters in the material constitutive properties, obtaining the dynamic stress-strain field through incremental iteration, generating a stress distribution cloud map that evolves over time on the three-dimensional model of the casting, and visually displaying the location and intensity changes of high stress areas through color gradients, providing a quantitative basis for subsequent coordinate marking of high fatigue risk areas.

[0047] Constitutive properties of materials refer to the stress-strain response relationship of materials (such as elastoplasticity, creep, and fatigue characteristics), which are obtained by fitting constitutive equations with data obtained from standard tensile tests, cyclic loading tests, and high-temperature creep tests.

[0048] Based on the stress distribution cloud map, multiaxial fatigue damage parameters are calculated using the critical plane method, and high-damage regions are identified according to the damage threshold, generating the coordinates of high-fatigue-risk regions. The expression is as follows:

[0049] ;

[0050] in, Indicates multiaxial fatigue damage parameters. This represents the maximum shear strain amplitude on the critical plane. Represents material constants. Indicates the normal direction of the critical plane. This represents the maximum normal stress value that the material experiences during the dynamic load cycle in the direction normal to the critical plane. Indicates the yield properties of the material. This indicates the yield strength of the material.

[0051] The specific process includes calculating multiaxial fatigue damage parameters based on stress distribution cloud maps using the critical plane method. The critical plane method determines multiaxial fatigue damage parameters by searching for the maximum damage plane. The multiaxial fatigue damage parameters are jointly determined by the maximum shear strain amplitude on the critical plane and the maximum normal stress value in the normal direction of the critical plane. The maximum shear strain amplitude reflects the degree of cyclic shear deformation, and the maximum normal stress value characterizes the tensile and compressive strength. Material constants describe the sensitivity of the material to the coupling effect of shear and tension. Yield characteristics and yield strength are used to constrain the damage calculation range. When the multiaxial fatigue damage parameters exceed the damage threshold, the corresponding area is marked as a high fatigue risk area, and the coordinates of the high fatigue risk area are generated for subsequent process optimization.

[0052] The damage threshold is preset based on material fatigue test data and service condition statistics. The threshold range is determined by fitting the SN curve and the critical damage accumulation relationship (e.g., the normalized damage threshold for ductile iron is often taken as 0.8).

[0053] S2: Based on the stress distribution cloud map and combined with the wall thickness distribution data, the casting is divided into three functional gradient layers: surface layer, transition layer and core layer. The component parameter range and performance target threshold of each functional gradient layer are defined, and the partition coordinate mapping relationship is generated.

[0054] Based on stress distribution cloud maps and combined with wall thickness distribution data, the partition boundaries of the surface layer, transition layer and core are generated through stress-wall thickness coupling optimization algorithm.

[0055] The specific process includes, based on stress distribution cloud map and wall thickness distribution data, the stress-wall thickness coupling optimization algorithm first extracts the coordinates of high stress areas and the corresponding wall thickness values, and then weights and superimposes the stress amplitude and wall thickness gradient to generate a composite index. When the composite index exceeds the fatigue geometric coupling threshold, it is marked as the boundary of the transition layer. The core region is defined as the continuous space with the lowest composite index and the largest wall thickness. The surface region is automatically generated by the high stress-thin wall thickness feature region. Finally, the coordinate set of the partition boundaries of the surface, transition layer and core is output for component gradient control.

[0056] Wall thickness distribution data is a set of thickness values ​​for each region of the three-dimensional model of the casting, obtained through CAD geometric analysis or CT scanning measurement.

[0057] The fatigue-geometry coupling threshold is preset based on the material fatigue limit and wall thickness sensitivity curve, and the critical value is determined by fitting experimental data.

[0058] Based on the partition boundaries, target thresholds for hardness of the surface layer, toughness of the transition layer, and density of the core were defined. The ranges for antimony content of the surface layer, molybdenum-copper content of the transition layer, and carbon equivalent of the core were obtained from the materials genome database.

[0059] The specific process includes determining the surface region based on the partition boundaries, setting the target threshold for surface hardness to meet high wear resistance requirements, setting the target threshold for transition layer toughness to meet impact resistance requirements, setting the target threshold for core density to meet low defect requirements, obtaining the surface antimony content range from the materials genome database to meet surface strengthening requirements, the transition layer molybdenum-copper content range to meet toughness-ductility matching requirements, and the core carbon equivalent range to meet self-feeding requirements. The composition parameter ranges of each region and the performance target thresholds together constitute the process control benchmark.

[0060] The Materials Genome Database is a materials performance-composition-process correlation database constructed by integrating high-throughput experimental testing, multi-scale simulation, and existing literature data.

[0061] Based on the range of component parameters and partition boundaries, a gradient component spatial distribution function is constructed to generate partition coordinate mapping relationships.

[0062] The specific process includes, based on the composition parameter range and partition boundaries, using radial basis function interpolation to continuously transition the surface antimony content range, the transition layer molybdenum-copper content range, and the core carbon equivalent range in three-dimensional space using the gradient composition spatial distribution function. The radial basis function establishes a weight distribution with partition boundaries as nodes. The composition zone coordinate mapping relationship is generated by solving the gradient composition spatial distribution function at each mesh node of the casting three-dimensional model. Finally, the composition parameter value corresponding to each spatial coordinate point is output to guide alloy presetting.

[0063] S3: Based on the partition coordinate mapping relationship, perform alloy presetting in the specified area of ​​the mold, and at the same time release local inoculant at the corresponding position of the high fatigue risk area.

[0064] Based on the partitioned coordinate mapping relationship, the optimal sound pressure amplitude and optimal sound wave frequency are calculated through the sound-heat coupling equation, and the piezoelectric transducer array is driven to generate a focused sound field. The expression is as follows:

[0065] ;

[0066] ;

[0067] in, Represents the spatial coordinates of the casting along the thickness direction. Represents the coordinates of the casting in the thickness direction The optimal sound pressure amplitude at that location. Indicates the reference sound pressure amplitude. Represents the gradient response coefficient. Represents the coordinates of the casting in the thickness direction The target alloy composition concentration at the location, Represents the coordinates of the casting in the thickness direction The optimal sound wave frequency at that location Indicates the reference sound wave frequency. Represents the frequency modulation coefficient. Represents the error function. Represents the coordinates of the casting in the thickness direction Temperature gradient at that location, Represents the coordinates of the casting in the thickness direction The target alloy composition concentration gradient at the location, This represents the thermal-mass coupling threshold.

[0068] The specific process includes, based on the partitioned coordinate mapping relationship, the acoustic-thermal coupling equation converts the target alloy composition concentration into the optimal sound pressure amplitude through the gradient response coefficient. The optimal sound pressure amplitude is jointly determined by the reference sound pressure amplitude and the target alloy composition concentration gradient. The optimal sound wave frequency is generated by correlating the temperature gradient with the target alloy composition concentration gradient through the frequency modulation coefficient. The error function is used to correct the sound field distortion under the thermal-mass coupling threshold. The piezoelectric transducer array generates a focused sound field according to the optimal sound pressure amplitude and the optimal sound wave frequency. The focused sound field forms a spatially distributed energy focusing in the thickness direction of the casting for directional control of the solidification process.

[0069] Based on the focused sound field, combined with the synergistic effect of the laser heat source, differentiated alloy materials are pre-placed in a designated area of ​​the casting mold.

[0070] The specific process includes the following steps: based on the synergistic effect of the focused sound field and the laser heat source, the focused sound field drives the alloy powder to migrate directionally in a designated area of ​​the mold, the laser heat source provides local melting energy to make the alloy powder bond with the matrix, the partition coordinate mapping relationship guides the pre-placement of high antimony alloy in the surface area, the pre-placement of molybdenum-copper alloy in the transition layer area, and the pre-placement of high carbon equivalent material in the core area, the intensity of the focused sound field is adjusted according to the optimal sound pressure amplitude, and the laser power density is matched with the temperature gradient distribution, ultimately achieving gradient control of composition and structure in the three-dimensional space of the casting.

[0071] A laser heat source is a controllable heat source formed by focusing a high-energy laser beam through an optical fiber (such as a YAG laser or a fiber laser). The power density and spot size of the laser heat source are determined by calibration using optical equipment.

[0072] Based on the coordinates of the high fatigue risk area, piezoelectric microcapsules are implanted at the corresponding positions in the mold, and the local phase field value is collected in real time. When the local phase field value exceeds the critical threshold, a PID control voltage signal is generated.

[0073] The specific process includes implanting piezoelectric microcapsules at corresponding positions in the mold based on the coordinates of the high fatigue risk area. The built-in sensors in the piezoelectric microcapsules collect local phase field values ​​in real time. When the local phase field value exceeds a critical threshold, a PID control algorithm is triggered. The PID control algorithm generates a voltage signal based on the deviation between the local phase field value and the critical threshold. The voltage signal drives the piezoelectric microcapsules to release the inoculant. The release rate is proportional to the amplitude of the voltage signal, thereby achieving targeted strengthening of the high fatigue risk area.

[0074] The critical threshold is preset based on experimental data of material phase transformation kinetics and the evolution law of microstructure, and is calibrated by metallographic observation and thermodynamic simulation.

[0075] When the PID control voltage reaches the trigger threshold, the piezoelectric microcapsule ruptures and releases a local inoculant.

[0076] The specific process includes the following steps: when the PID control voltage reaches the trigger threshold, the lead zirconate titanate piezoelectric ceramic shell of the piezoelectric microcapsule generates mechanical strain under the inverse piezoelectric effect. The mechanical strain exceeds the shell fracture strength, causing the piezoelectric microcapsule to rupture. The cerium-yttrium composite inoculant stored inside the piezoelectric microcapsule is released into the melt in the form of atomization. The release position is precisely located by the coordinates of the high fatigue risk area, and the release timing is controlled by the real-time feedback of the local phase field value, thereby realizing the point-to-point microalloying of the risk area.

[0077] The trigger threshold is preset based on experimental data of the fracture strength of piezoelectric ceramics and the release kinetics of inoculant, and the critical value is determined by voltage-strain calibration curve.

[0078] S4: Based on local inoculants, perform gradient solidification process and detect the core density and microstructure morphology of the casting. Adjust the composition parameter range and performance target threshold according to the test results.

[0079] A gradient solidification process is performed based on the released local inoculant, with an acousto-optic coupling field applied simultaneously and a holographic image of the solidification front acquired.

[0080] The specific process includes: when performing gradient solidification based on the released local inoculant, the acousto-optic coupling field modulates the refractive index distribution of the melt by emitting high-frequency acoustic waves through the piezoelectric transducer array; synchronous pulsed laser penetrates the casting to generate diffraction fringes carrying phase field information; a high-speed CMOS camera acquires a holographic image of the solidification front; the spatial frequency distribution of the holographic image reflects the phase field gradient change under the action of the local inoculant, providing input data for subsequent phase field inversion.

[0081] Phase-field-light transport joint inversion was performed on the holographic image of the solidification front to detect the density of the heart and the microstructure of the risk area in real time.

[0082] The specific process includes the following steps: When performing phase-field-optical transport joint inversion on the solidification front holographic image, the amplitude and phase distribution in the holographic image are first extracted. The amplitude and phase distribution is then used to reconstruct the wavefront information through Fresnel-Kirchhoff diffraction integrals. The reconstructed wavefront information is coupled with the phase-field dynamics equations to solve for the local phase-field values. The local phase-field values ​​are mapped to the core compactness and the graphite spheroid diameter distribution in the risk zone. The core compactness is obtained by integrating the phase-field values. The microstructure morphology of the risk zone is generated by correlating the phase-field gradient with the spheroid diameter distribution. The detection results are used to provide feedback and correct the range of component parameters and the performance target threshold.

[0083] Based on the detected cardiac density and microscopic tissue morphology data of the risk area, a parametric posterior probability distribution model is constructed.

[0084] The specific process includes: based on the detected core compactness and microstructure morphology data of the risk area, a parameter posterior probability distribution model is constructed using the Bayesian inference method. The Bayesian inference method uses the core compactness as the input of the likelihood function and the microstructure morphology data of the risk area as the prior distribution constraint. Markov chain Monte Carlo sampling is used to solve the parameter joint posterior probability distribution. The parameter joint posterior probability distribution outputs the carbon equivalent correction interval and the inoculant release threshold optimization range, and finally generates the process parameter confidence interval for closed-loop control.

[0085] Furthermore, the pre-training process of the parameter posterior probability distribution model is first based on historical casting process datasets. The joint distribution of material composition parameters and performance indicators is sampled using the Markov chain Monte Carlo method. The sampling results are input into a variational autoencoder to extract the latent feature space. The latent feature space and phase field simulation data are aligned and optimized using an adversarial generative network. Finally, a probabilistic mapping relationship is constructed that can predict the parameter correction amount based on the core density and the tissue morphology of the risk area.

[0086] The posterior probability distribution model of the parameters is input into the Bayesian-phase-field joint inversion algorithm to calculate the correction amount of the casting process parameters. The expression is as follows:

[0087] ;

[0088] in, This indicates the amount of correction to the casting process parameters. The weighting coefficients represent the posterior expectation terms. Indicates casting process parameters The variational posterior probability distribution function, Describes the variational posterior probability distribution function Below are the casting process parameters The expected value of the sequence. This represents the normalized weighting coefficients of the phase-field gradient coupling term. Indicates the length of the core feature of the casting. Represents the three-dimensional spatial integral domain of the casting. Represents the spatial gradient of casting process parameters. Represents phase field variables Spatial gradient, This represents the real-time value vector of the current casting process parameters.

[0089] The specific process includes: inputting the parameter posterior probability distribution model into the Bayesian-phase-field joint inversion algorithm; adjusting the weight coefficient of the posterior expectation term to improve the contribution of the variational posterior probability distribution function to the correction of casting process parameters; calculating the mathematical expectation value of casting process parameters through Markov chain Monte Carlo sampling using the variational posterior probability distribution function; balancing the synergistic effect of the spatial gradient of casting process parameters and the spatial gradient of phase field variables using the normalized weight coefficient of the phase field gradient coupling term; using the feature length of the casting core to calibrate the spatial scale of gradient coupling; limiting the calculation range using the three-dimensional spatial integral domain of the casting; reflecting the non-uniformity of component distribution in the spatial gradient of the phase field variables; characterizing the direction of microstructure evolution using the spatial gradient of the phase field variables; providing a correction benchmark using the real-time value vector of the current casting process parameters; and finally outputting the correction amount of casting process parameters that satisfies multi-objective optimization.

[0090] It should be noted that the casting process parameter correction amount is used to achieve structural optimization through the Bayesian-phase-field joint inversion algorithm. The specific process is as follows: Based on the real-time detected core density and risk zone microstructure morphology data, a parameter posterior probability distribution model is constructed. After being input into the Bayesian-phase-field joint inversion algorithm, the algorithm integrates the detection data and phase-field evolution law to generate the casting process parameter correction amount. The casting process parameter correction amount dynamically adjusts the antimony content range of the surface layer to enhance surface wear resistance, optimizes the molybdenum-copper content range of the transition layer to improve toughness matching, and calibrates the core carbon equivalent range to ensure self-feeding effect. Thus, the target thresholds for hardness, toughness, and density are corrected simultaneously, core shrinkage defects are eliminated, the graphite spheroid diameter distribution in the risk zone is homogenized, and the overall fatigue life and structural reliability of the casting are ultimately improved.

[0091] Based on the correction amount of casting process parameters, the range of composition parameters and performance target thresholds are dynamically adjusted.

[0092] The specific process includes: based on the correction amount of casting process parameters, updating the composition parameter range by querying the material genome database, adjusting the surface antimony content range to enhance wear resistance, adapting the transition layer molybdenum-copper content range to meet the toughness and plasticity matching requirements, ensuring the core carbon equivalent range meets the self-feeding requirements, updating the performance target thresholds simultaneously, raising the surface hardness target threshold to a high wear resistance level, maintaining the transition layer toughness target threshold to an impact resistance level, and ensuring the core density target threshold to meet low defect requirements. The corrected parameters serve as the input benchmark for the next round of process iteration.

[0093] This embodiment also provides a structural optimization design system for ductile iron castings, including: a load analysis module, a gradient layering module, a material pre-setting module, and a solidification optimization module. The load analysis module is used to collect dynamic load data, perform finite element analysis in conjunction with the three-dimensional model of the casting, generate a stress distribution cloud map, and mark the coordinates of high fatigue risk areas. The gradient layering module is used to divide the casting into three functional gradient layers—surface, transition layer, and core—based on the stress distribution cloud map and wall thickness distribution data, define the composition parameter range and performance target threshold for each functional gradient layer, and generate a partition coordinate mapping relationship. The material pre-setting module is used to perform alloy pre-setting in a specified area of ​​the mold according to the partition coordinate mapping relationship, and simultaneously release a local inoculant at the corresponding position of the high fatigue risk area. The solidification optimization module is used to perform a gradient solidification process based on the local inoculant and detect the core density and microstructure morphology of the risk area of ​​the casting, and correct the composition parameter range and performance target threshold based on the detection results.

[0094] This embodiment also provides a computer device applicable to the ductile iron casting structure optimization design method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the ductile iron casting structure optimization design method proposed in the above embodiment.

[0095] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0096] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for optimizing the structure of ductile iron castings as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0097] In summary, this invention achieves accurate simulation of the actual stress conditions of castings under complex working conditions by collecting dynamic load data and performing finite element analysis based on a three-dimensional model of the casting, thereby improving the reliability and service life of the castings. Based on stress distribution cloud maps and wall thickness distribution data, the castings are divided into functional gradient layers with different performance requirements, specifically meeting the requirements of different parts for hardness, toughness, and density, and greatly improving the uniformity and stability of the overall performance of the castings.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the design of a nodular cast structure, characterized in that: The method comprises the following steps of: Collecting dynamic load data, combining a three-dimensional model of the casting to perform finite element analysis, generating a stress distribution cloud map and marking high fatigue risk area coordinates; According to the stress distribution cloud map, combining wall thickness distribution data, the casting is divided into three functional gradient layers of surface layer, transition layer and core, and the composition parameter range and performance target threshold of each functional gradient layer are defined, and the partition coordinate mapping relationship is generated, the specific steps are as follows, Based on the stress distribution cloud map, combining the wall thickness distribution data, the partition boundaries of the surface layer, the transition layer and the core are generated by the stress-wall thickness coupling optimization algorithm; According to the partition boundary, the hardness target threshold of the surface layer, the toughness target threshold of the transition layer and the density target threshold of the core are defined, and the antimony content range of the surface layer, the molybdenum copper content range of the transition layer and the carbon equivalent range of the core are obtained through the material genome database; According to the composition parameter range and the partition boundary, the gradient composition space distribution function is constructed, and the partition coordinate mapping relationship is generated; According to the partition coordinate mapping relationship, the alloy is preloaded in the specified area of the mold, and the local inoculant is released at the corresponding position of the high fatigue risk area coordinates, and the specific steps are as follows, Based on the partition coordinate mapping relationship, the optimal sound pressure amplitude and the optimal sound wave frequency are calculated through the acoustic-thermal coupling equation, and the piezoelectric transducer array is driven to generate a focused acoustic field; Based on the focused acoustic field, the laser heat source is combined to perform synergistic action, and the differential alloy material is preloaded in the specified area of the mold; According to the high fatigue risk area coordinates, the piezoelectric microcapsule is implanted at the corresponding position of the mold, and the local phase field value is collected in real time, and when the local phase field value exceeds the critical threshold, the PID control voltage signal is generated; When the PID control voltage reaches the trigger threshold, the piezoelectric microcapsule breaks to release the local inoculant; Based on the local inoculant, the gradient solidification process is performed, and the core density and the microstructure morphology of the risk area of the casting are detected, and the composition parameter range and the performance target threshold are corrected according to the detection results.

2. The method of optimizing the structure of a ductile castings as set forth in claim 1, wherein: The dynamic load data includes time domain dynamic force load, load space distribution coordinates and environmental temperature load coupling data.

3. The method of optimizing the structure of a ductile castings as set forth in claim 2, wherein: The stress distribution cloud map is generated, and the specific steps are as follows, Collecting dynamic load data, generating time and space associated dynamic load spectrum through empirical mode decomposition and generalized Pareto distribution extrapolation, and mapping to the surface grid nodes of the three-dimensional model of the casting through shape function matrix; The mapped dynamic load spectrum is input into the finite element solver, and the dynamic stress-strain field is obtained by combining the material constitutive property, and the stress distribution cloud map is generated.

4. The method of optimizing the structure of a ductile castings as set forth in claim 3, wherein: The high fatigue risk area coordinates are marked, that is, based on the stress distribution cloud map, the multi-axial fatigue damage parameter is calculated through the critical plane method, and the high damage area is identified according to the damage threshold, and the high fatigue risk area coordinates are generated.

5. The method of optimizing the structure of a ductile cast part according to claim 4, wherein: Based on the local inoculant, the gradient solidification process is performed, and the core density and the microstructure morphology of the risk area of the casting are detected, and the composition parameter range and the performance target threshold are corrected according to the detection results. Based on the released local inoculant, the gradient solidification process is performed, the acoustic-optical coupling field is applied synchronously, and the solidification front holographic image is collected; The phase field-light transmission joint inversion is performed on the solidification front holographic image, and the core density and the microstructure morphology of the risk area are detected in real time.

6. The method of optimizing a design of a ductile cast component as set forth in claim 5, wherein: The composition parameter range and the performance target threshold are corrected according to the detection results, and the specific steps are as follows, Based on the detected heart density and risk area microstructure morphology data, a parameter posterior probability distribution model is constructed; Input the parameter posterior probability distribution model into the Bayesian-phase field joint inversion algorithm to calculate the casting process parameter correction; According to the casting process parameter correction, the composition parameter range and performance target threshold are dynamically corrected.

7. A system for optimizing the structure of a ductile casting based on the method for optimizing the structure of a ductile casting according to any one of claims 1 to 6, characterized in that: It includes load analysis module, gradient layering module, material presetting module and solidification optimization module, The load analysis module is used to collect dynamic load data, conduct finite element analysis combined with the three-dimensional model of the casting, generate stress distribution chart and mark the high fatigue risk area coordinates; The gradient layering module is used to divide the casting into three functional gradient layers of surface layer, transition layer and heart according to the stress distribution chart and combined with the wall thickness distribution data, define the composition parameter range and performance target threshold of each functional gradient layer, and generate the partition coordinate mapping relationship; The material presetting module is used to execute alloy presetting in the specified area of the mold according to the partition coordinate mapping relationship, and release local inoculant at the corresponding position of the high fatigue risk area coordinates; The solidification optimization module is used to execute gradient solidification process based on the local inoculant and detect the casting heart density and risk area microstructure morphology, and correct the composition parameter range and performance target threshold according to the detection results.

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