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CN-122021170-A - Die-casting die adaptation degree detection method based on three-dimensional model

CN122021170ACN 122021170 ACN122021170 ACN 122021170ACN-122021170-A

Abstract

The invention relates to the field of three-dimensional models, and discloses a die-casting die adaptation degree detection method based on a three-dimensional model. The method comprises the steps of obtaining high-precision geometric three-dimensional model and industrial CT voxel data, fusing and constructing an enhanced digital twin body with 7-dimensional attributes, establishing a multi-physical field coupling finite element model based on the enhanced digital twin body, combining and correcting Johnson-Cook native and Paris crack extension formulas, simulating and predicting the structural health state of a key area, quantifying the adaptation degree through a weighted comprehensive scoring function, judging whether a die is qualified or not according to a safety threshold, and supporting the dynamic updating of the model driven by embedded sensor data and the marking of a problem sensitive area. The invention realizes jump from static geometric matching to dynamic function reliability evaluation, and improves detection accuracy and mold service safety.

Inventors

  • Shao Canzhuo
  • SHAO YUNYUE

Assignees

  • 余姚市久源液压技术有限公司

Dates

Publication Date
20260512
Application Date
20260203

Claims (10)

  1. 1. The die casting die adaptation degree detection method based on the three-dimensional model is characterized by comprising the following steps of: Obtaining a high-precision geometric three-dimensional model of a die-casting die to be tested; Carrying out full-area nondestructive testing on the die-casting die to be tested by adopting industrial computer tomography equipment to obtain space voxel data of the density distribution, the porosity, the crack trend and the impurity distribution of the internal material, wherein the gray value of the space voxel data and the local density of the material are in a linear mapping relation; Aligning the high-precision geometric three-dimensional model with the space voxel data by a space coordinate system, and constructing an enhanced three-dimensional digital model fusing the geometric shape and the internal material state, wherein each space unit of the enhanced three-dimensional digital model simultaneously comprises 7-dimensional attributes of a surface normal vector, a curvature radius, a local thickness, material density, a pore volume fraction and a microcrack existence mark; based on the enhanced three-dimensional digital model, establishing a multi-physical field coupling finite element analysis model, wherein the enhanced three-dimensional digital model comprises a heat conduction equation, an elastoplastic mechanical equation and a fatigue damage evolution equation; In the finite element solving process, a Johnson-Cook model taking the porosity correction into consideration is adopted for the constitutive relation of the material, a Paris formula is adopted for describing the microcrack expansion behavior, and the crack expansion rate is corrected according to the local pore distribution and the main stress direction; extracting accumulated plastic strain, maximum main stress amplitude, fatigue damage accumulated value and thermal fatigue crack initiation position of a key region of a die as structural health state indexes, comparing the structural health state indexes with a preset safety threshold, and judging that the functional adaptation degree of the die does not reach the standard if any index is larger than the corresponding threshold; And constructing an adaptation degree comprehensive scoring function, wherein the adaptation degree comprehensive scoring function is a weighted sum of health state indexes of all structures, the weight coefficient is extracted from the historical service data through a principal component analysis method, and when the adaptation degree comprehensive scoring value is greater than 1, the mold adaptation degree is judged to be unqualified.
  2. 2. The three-dimensional model-based die casting mold fitness detection method according to claim 1, wherein aligning the high-precision geometric three-dimensional model with the spatial voxel data in a spatial coordinate system comprises: performing initial registration on the high-precision geometric three-dimensional model and the space voxel data by adopting an iterative nearest point algorithm; Optimizing the rotation matrix and the translation vector to minimize the sum of squares of distances between the geometric model surface points and corresponding boundary voxels in the voxel data; and resampling the voxel data to the surface grid nodes of the geometric model and the adjacent internal space thereof, and constructing the enhanced three-dimensional digital model with 7-dimensional attributes in each space unit.
  3. 3. The method for detecting the adaptation degree of the die-casting die based on the three-dimensional model according to claim 2, wherein the local thickness in the 7-dimensional attribute is obtained by intersecting a normal reverse ray from a surface node to the inside of the die, the pore volume fraction is defined as the ratio of pore voxels in a space unit, the microcrack existence mark is a Boolean value, and if a crack segment with the length of more than 50 microns exists in the unit, the microcrack existence mark is set to be true.
  4. 4. The method for detecting the adaptation degree of the die-casting die based on the three-dimensional model according to claim 3, wherein the meshing of the multi-physical field coupling finite element analysis model adopts self-adaptive tetrahedral units, and the key areas comprise a die cavity corner, a position near a pouring gate, a cooling water channel outer wall and a wall thickness abrupt change.
  5. 5. The three-dimensional model-based die casting mold fitness detection method according to claim 4, wherein the yield strength of the Johnson-Cook model The expression is: ; in the form of a static yield strength, In order to achieve a strain hardening coefficient, In order to obtain a strain hardening index, the composition, In order to be a factor of the sensitivity to strain rate, In order to provide a heat softening index, In order to be an equivalent plastic strain, In order for the strain rate to be a function of, For the purpose of the reference strain rate, Is at room temperature, and is at room temperature, Is the melting point of the material, and is the melting point of the material, Is the current thermodynamic temperature of the material.
  6. 6. The three-dimensional model-based die casting mold fitness detection method according to claim 5, wherein the Paris formula crack propagation rate ; Representing the length of the crack, Representing the number of cyclic loads, And (3) with As a matter of material fatigue parameter, Is the stress intensity factor amplitude; Is corrected according to whether a pore or an impurity exists in front of the crack tip, the effective crack length is increased if the pore exists, and the introduced resistance factor is reduced if the impurity exists 。
  7. 7. The three-dimensional model-based die casting die adaptation degree detection method according to claim 6, wherein the multi-physical field coupling finite element analysis model adopts an explicit-implicit hybrid solving strategy, the thermal coupling part adopts implicit solving, and the fatigue damage evolution adopts explicit integration.
  8. 8. The three-dimensional model-based die casting mold fitness detection method according to claim 7, wherein the fitness comprehensive scoring function is: ; For the comprehensive scoring value of the fitness, 、 、 , 、 、 Respectively obtaining the accumulated plastic strain, the maximum principal stress amplitude and the fatigue damage accumulated value by simulation, 、 、 Is the corresponding safety threshold.
  9. 9. The method for detecting the adaptation degree of the die-casting die based on the three-dimensional model according to claim 8 is characterized by further comprising the step of dynamically updating an enhanced three-dimensional digital model, wherein in the actual service process of the die, temperature and strain data of a key position are monitored in real time through an embedded fiber bragg grating sensor, deviation analysis is carried out on the measured data and a simulation predicted value, and a model parameter inversion mechanism is triggered if the temperature deviation is greater than +/-10 ℃ or the strain deviation is greater than +/-0.2%.
  10. 10. The method for detecting the adaptation degree of the die-casting die based on the three-dimensional model according to claim 9, wherein the model parameter inversion mechanism adopts an extended Kalman filtering algorithm, takes measured temperature and strain data as observation input, carries out on-line estimation and correction on the intrinsic parameters and the internal problem state of the material, and updates an enhanced three-dimensional digital model for subsequent adaptation degree re-estimation.

Description

Die-casting die adaptation degree detection method based on three-dimensional model Technical Field The invention belongs to the field of three-dimensional models, and particularly relates to a die-casting die adaptation degree detection method based on a three-dimensional model. Background With the wide application of the die-casting molding process in the high-end manufacturing fields of automobiles, aerospace, precision manufacturing and the like, higher requirements are put on the reliability and service life of the die-casting mold. The structural integrity of the die casting die directly determines the forming precision and the production stability under the conditions of high temperature, high pressure and thermal shock cyclic load. The traditional mold detection method mainly relies on optical scanning, three-coordinate measurement or geometric adaption matching based on a CAD model to evaluate the space matching degree between a mold cavity and a casting. Although the method can identify the problem of macroscopic dimensional deviation or surface morphology, only the external geometric form is concerned, and the microstructure state inside the material cannot be reflected. The adaptation degree detection technology based on the three-dimensional model becomes a research hot spot in recent years, and the high-precision digital twin model is generated for virtual assembly verification by reconstructing the point cloud data on the surface of the mold through laser scanning or structured light. The method has the effect on the static geometric matching layer, and can rapidly locate the dislocation, abrasion or deformation area. However, the failure of the mold during actual service often does not originate from geometrical mismatch, but rather is caused by the gradual evolution of internal recessive problems, such as shrinkage porosity, microcracks, inclusions or weakening of grain boundaries, under the action of the thermo-mechanical coupling cycle. The problems have little influence on the whole appearance under the normal temperature static state, are difficult to capture by the conventional three-dimensional modeling means, but induce local stress concentration in an alternating stress field, accelerate crack initiation and expansion, and finally lead to sudden fracture or loss of functions. In the prior art, although the industrial CT can realize nondestructive internal structure imaging, the output of the industrial CT is mostly used for qualitative observation or isolated problem labeling, and is not yet deeply coupled with a mechanical property prediction model, while the traditional finite element analysis is generally based on an ideal homogeneous material assumption, and the actual disturbance of microscopic problems to local mechanical response is ignored. Therefore, an adaptation degree evaluation mechanism capable of fusing three-dimensional distribution information of internal problems with microscale mechanical behavior simulation is lacking at present, and potential failure risks of the die under cyclic load cannot be quantitatively early-warned before the die is put into use. The technical fault makes the high-value die still face the risk of 'geometric qualification but sudden death in service', and a hidden problem adaptive failure early warning method for fusing the real characterization of an internal structure and the evolution simulation of multiple physical fields needs to be established. Disclosure of Invention The invention provides a die-casting die adaptation degree detection method based on a three-dimensional model, and aims to solve the problems of misjudgment of adaptation degree and service failure risk caused by the fact that the hidden problem inside a die cannot be effectively identified through a conventional geometric model in the prior art. According to the method, the multi-physical-field simulation data and the high-resolution nondestructive testing information are fused, an enhanced digital twin body containing material internal state characteristics is constructed on the basis of a traditional geometric three-dimensional model, and an adaptation evaluation mechanism of dynamic load response driving is established on the basis, so that the accurate prediction of the structural integrity and functional reliability of the die under the actual working condition is realized. The invention provides a die-casting die adaptation degree detection method based on a three-dimensional model, which comprises the following steps: Obtaining a high-precision geometric three-dimensional model of a die-casting die to be tested; Carrying out full-area nondestructive testing on the die-casting die to be tested by adopting industrial computer tomography equipment to obtain space voxel data of the density distribution, the porosity, the crack trend and the impurity distribution of the internal material, wherein the gray value of the space voxel data and the local density of the ma