A composite material mold thermal expansion error data compensation method and system
By constructing a nonlinear compensation model and combining it with the geometric and temperature characteristics of the mold, the problem of insufficient accuracy in thermal expansion error compensation of composite material molds was solved, and efficient and accurate mold manufacturing and production optimization were achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHAANXI HUANGHE XINXING EQUIP CO LTD
- Filing Date
- 2026-01-19
- Publication Date
- 2026-05-19
AI Technical Summary
In the existing technology, the thermal expansion error compensation method for composite material molds fails to effectively consider the local geometric features and temperature field distribution differences of the mold, resulting in insufficient compensation accuracy in complex shape areas, which increases manufacturing costs and cycle time.
By acquiring point cloud and temperature data of the mold, calculating geometric and thermophysical characteristics, constructing a nonlinear compensation model, including mold feature parameter set, geometric thermal sensitivity index, expansion anisotropy coefficient and thermal expansion displacement compensation amount, forming a closed-loop optimization process, generating CNC machining code and manufacturing the mold.
It achieves targeted compensation calculations, reduces product size deviations, improves production efficiency and product qualification rate, reduces subsequent repair work, and is suitable for the production of molds with various complex shapes.
Smart Images
Figure CN121525205B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to a method and system for compensating for thermal expansion error data in composite material molds. Background Technology
[0002] With the increasing demands for lightweighting in the aerospace and high-end equipment manufacturing sectors, carbon fiber reinforced composites have become widely used due to their excellent specific strength and specific modulus. Autoclave molding is the mainstream method in the manufacturing of composite components, requiring the mold to maintain shape accuracy for extended periods under high temperature and pressure. However, the coefficients of thermal expansion of the mold material and the composite component often differ significantly. During the high-temperature curing stage, the mold undergoes thermal expansion. Without precise dimensional compensation, the composite component after cooling and demolding will fail to meet assembly requirements due to dimensional deviations, severely impacting product yield.
[0003] In existing technologies, error compensation for mold thermal expansion typically employs an empirical linear scaling method. This method assumes a uniform and isotropic temperature field distribution within the mold and calculates a global scaling factor based solely on the material's nominal coefficient of thermal expansion and curing temperature to enlarge or reduce the mold surface as a whole. However, in practical engineering, the geometric features of large and complex molds vary greatly, including deep cavities and abruptly changing corners. Furthermore, the thermal convection characteristics within the autoclave inevitably lead to temperature gradients on the mold surface. The linear scaling method neglects the nonlinear influence of geometric curvature constraints on thermal deformation and fails to consider the uneven distribution of thermal stress caused by local thermal gradients. This results in errors occurring in complex areas of the mold, such as... The compensation accuracy at corners and variable cross sections is seriously insufficient, often requiring a lot of manual grinding or repair in the later stages, which increases manufacturing costs and time.
[0004] Therefore, a thermal expansion error compensation method that can integrate the local geometric features of the mold with the actual temperature field distribution data is needed. By deeply exploring the influence of geometric curvature and thermal gradient on expansion behavior, a nonlinear high-precision compensation model is constructed to overcome the limitations of the traditional linear scaling method and realize the precise design and manufacturing of composite material mold surfaces. This is of great engineering significance for improving the molding quality of high-end composite material components. Summary of the Invention
[0005] To address the technical problem that the traditional compensation method only uses a uniform scaling approach and does not consider the complex shape of the mold and the differences in surface temperature distribution, the present invention provides solutions in the following aspects.
[0006] In a first aspect, the present invention provides a method for compensating for thermal expansion error data in composite material molds, comprising:
[0007] The process involves acquiring point cloud and temperature data of a composite material mold, calculating its geometric and thermophysical characteristics to obtain a set of mold feature parameters, including mold point cloud data, mold geometric center coordinates, local Gaussian curvature, local thermal gradient modulus, global average curvature, standard deviation of curvature, and global average thermal gradient. Based on the amplifying effect of local Gaussian curvature on the mold's thermal sensitivity, a geometric thermal sensitivity index is obtained. The expansion anisotropy coefficient is calculated based on the relationship between the geometric thermal sensitivity index and overall geometric complexity influencing the mold's expansion nonlinearity. The thermal expansion displacement compensation amount is calculated based on the expansion anisotropy coefficient and the combined effect of thermal expansion displacement caused by thermal expansion factors and actual deformation correction. Based on the thermal expansion displacement compensation amount, the coordinates of each point in the mold point cloud data are compensated to obtain a mold compensation point cloud set. This set is then fitted into a surface solid to obtain the final mold compensation model. The compensated mold surface digital model is converted into CNC machining code and transmitted to a machining center for mold manufacturing. Closed-loop optimization of the mold's thermal expansion compensation is performed based on feedback from the residual data of the first mold piece.
[0008] This invention achieves targeted compensation calculations by comprehensively collecting mold-related data and accurately calculating characteristic differences at different locations. The compensation amount for each location is determined based on actual conditions, avoiding the coarseness of uniform compensation. Simultaneously, it integrates scattered data into a usable processing model, ensuring that compensation is accurately implemented in actual production. The feedback optimization mechanism for the first product can promptly correct potential deviations, reducing the impact of random factors on product quality. The entire process is progressive, from data collection to compensation calculation, processing implementation, and optimization adjustments, forming a complete closed loop. This effectively reduces product dimensional deviations, minimizes subsequent repair work, improves production efficiency and product qualification rate, and provides support for the stable production of related products.
[0009] Preferably, the mold feature parameter set includes:
[0010] Obtain mold point cloud data, calculate the average position of all points in the mold point cloud data, and use it as the geometric center coordinates of the mold; obtain the curing temperature data of the mold surface, record it as temperature data, and map the temperature data to each point in the mold point cloud data according to the spatial coordinate correspondence; obtain the neighborhood point set of a single point in the mold point cloud data, fit a quadratic surface and calculate the curvature of the point based on the principle of surface differential geometry, calculate the local Gaussian curvature, calculate the temperature gradient modulus of the point, and obtain the local thermal gradient modulus; calculate the arithmetic mean and standard deviation of the absolute values of all local Gaussian curvatures to obtain the global average curvature and standard deviation of curvature, calculate the mean of all local thermal gradient moduli, and obtain the global average thermal gradient.
[0011] Preferably, the geometric thermal sensitivity index satisfies the following expression:
[0012] ;
[0013] In the formula, Indicates the midpoint of the mold point cloud data The geometric thermal sensitivity index, in degrees Celsius; Midpoint of mold point cloud data The corresponding temperature data; Provides real-time indoor reference temperature; For point Local Gaussian curvature; The global average curvature of the mold; It should be the first minimum positive number to prevent the denominator from being zero; It is an exponential function with the natural constant as its base.
[0014] This invention can reasonably assess the temperature sensitivity of different locations on a mold. Even in areas with the same temperature, it can distinguish different deformation tendencies caused by shape differences, helping workers identify areas prone to deformation problems. This differentiation method allows subsequent compensation to be more targeted, avoiding a crude approach that treats all areas the same, thus improving the rationality and accuracy of compensation and reducing product dimensional deviations.
[0015] Preferably, calculating the expansion anisotropy coefficient includes:
[0016] The maximum value among the geometric thermal sensitivity indices corresponding to all points in the mold point cloud data is denoted as the peak value of the thermal sensitivity index; the expansion anisotropy coefficient satisfies the following expression:
[0017] ;
[0018] In the formula, Point The expansion anisotropy coefficient is dimensionless; For point The local thermal gradient modulus; The global average thermal gradient is expressed in units of 1 / 2π and 1 / 2π. Maintain consistency; For point Geometric thermal sensitivity index; This represents the peak value of the heat sensitivity index. The global average curvature; The standard deviation of curvature; It is the natural logarithm function; , , These are the second smallest positive number, the third smallest positive number, and the fourth smallest positive number, respectively, used to ensure that the denominator is not zero.
[0019] This invention considers two influencing factors: uneven temperature distribution and complex shape of the mold. It generates a dynamic adjustment coefficient, which can flexibly adjust the compensation intensity according to the actual situation of different positions of the mold, breaking the limitations of traditional uniform compensation. For areas with large temperature differences or complex shapes, it can provide appropriate compensation adjustments, making the compensation more in line with actual needs and helping to improve the dimensional consistency of the product after mold forming.
[0020] Preferably, calculating the thermal expansion displacement compensation includes:
[0021] The Euclidean distance between each point in the mold point cloud data and the geometric center of the mold is denoted as the characteristic length of that point; the standard linear thermal expansion coefficient, which characterizes the basic thermal expansion properties of the material, is obtained from the database; the thermal expansion displacement compensation amount satisfies the following expression:
[0022] ;
[0023] In the formula, Point The thermal expansion displacement compensation amount, with the dimension of length; For point The characteristic length; The standard linear thermal expansion coefficient is obtained in advance; Midpoint of mold point cloud data The corresponding temperature data; Provides real-time indoor reference temperature; Point The expansion anisotropy coefficient.
[0024] This invention combines the material's inherent expansion characteristics with pre-calculated adjustment coefficients to derive the specific compensation amount for each location. This calculation method respects the material's inherent properties while also taking into account the special circumstances of different mold positions, making the compensation calculation more reasonable. Furthermore, this method facilitates practical application, ensuring accurate implementation of compensation measures, reducing product dimensional issues caused by improper compensation, and improving production stability.
[0025] Preferably, obtaining the mold compensation point cloud set includes:
[0026] The local area of the mold is fitted using a data fitting method to obtain a local fitting plane. The normal vector of the local fitting plane is the normal vector of the local area, denoted as the normal vector of each point in the mold point cloud data. The direction of the normal vector of each point in the mold point cloud data is taken as the displacement direction. The original coordinates of each point in the mold point cloud data are superimposed with the product of the thermal expansion displacement compensation amount and the normal vector, and the superimposed results are integrated to obtain the mold compensation point cloud set.
[0027] This invention clarifies the direction and specific operation method for compensation at each position of the mold. By rationally determining the displacement direction and making precise adjustments, the compensation at each position can be achieved as expected. This operation method effectively avoids problems caused by deviations in the compensation direction or improper adjustments, ensuring that the shape of the compensated mold meets subsequent production requirements.
[0028] Preferably, the final mold compensation model is obtained, including:
[0029] Using a surface fitting algorithm, the mold compensation point cloud set is reconstructed into a continuous surface entity, and the local noise of the surface entity is smoothed to obtain the final mold compensation model.
[0030] Preferably, the compensated digital model of the mold surface is converted into CNC machining code, including:
[0031] The final mold compensation model is processed using CAM software, including setting toolpaths, cutting parameters, machining coordinate system and tool approach / retraction strategies. The process planning results are then converted into G-code that conforms to the control system standard of the machining center and transmitted to the machining center via industrial communication protocol.
[0032] Preferably, closed-loop optimization for mold thermal expansion compensation includes:
[0033] The system acquires 3D scanning data of the first mold and aligns it with the product design model. It calculates the deviation between the actual size and the design size of each inspection point. It statistically analyzes the distribution characteristics of the deviation values. If the maximum or average deviation value exceeds the preset accuracy threshold, the residual data is fed back to the database. Based on the residual data, the standard linear thermal expansion coefficient of the material is corrected in reverse to achieve closed-loop optimization of mold thermal expansion compensation.
[0034] Secondly, the present invention provides a composite material mold thermal expansion error data compensation system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned composite material mold thermal expansion error data compensation method is implemented.
[0035] By adopting the above technical solution, a computer program is generated from the above-mentioned method for compensating thermal expansion error data of composite material molds, and stored in a memory so that it can be loaded and executed by a processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.
[0036] The beneficial effects of this invention are as follows: It is not limited to optimizing a single step, but rather forms a complete process system from data collection, calculation, and processing to subsequent optimization, applicable to the production of molds with various complex shapes. This systematic approach helps enterprises improve production stability and reduce resource waste and cost increases caused by product defects. Simultaneously, the solution has a clear operational process, facilitating its promotion and application in actual production and providing reliable technical support to more enterprises. Its optimization mechanism allows for continuous improvement of the production process, adapting to changes in different production environments and materials, contributing to the high-quality development of related industries, and providing a reference for the manufacturing of similar products. Attached Figure Description
[0037] Figure 1 This is a flowchart illustrating a method for compensating for thermal expansion error data in a composite material mold according to the present invention;
[0038] Figure 2 This is an example chart illustrating the calculation results of thermal expansion error compensation at various points of the composite material mold in this invention;
[0039] Figure 3 This is a schematic diagram illustrating the digital model of the mold surface reconstructed based on compensated point cloud in this invention. Detailed Implementation
[0040] This invention discloses a method for compensating for thermal expansion error data in composite material molds, referring to... Figure 1 This includes steps S1-S4:
[0041] S1: Obtain point cloud and temperature data of composite material mold, calculate geometric and thermophysical features, and obtain a set of mold feature parameters including mold point cloud data, mold geometric center coordinates, local Gaussian curvature, local thermal gradient modulus, global average curvature, curvature standard deviation, and global average thermal gradient.
[0042] It should be noted that the curing and molding of composite material components is usually carried out in the high temperature and high pressure environment of an autoclave. The mold, as the molding reference, directly determines the dimensional accuracy of the final product due to its thermal expansion behavior. However, the mold surface is often a non-uniform and complex curved surface, leading to two core problems: first, geometric nonlinearity, where thermal deformation in deep cavities and high curvature regions is more constrained by the structure than in flat areas; and second, thermal field nonuniformity, where the heating rate and peak temperature often differ across the mold surface. Therefore, to achieve high-precision error compensation, the mold cannot be simply treated as an isotropic homogeneous body and scaled as a whole. A fundamental dataset capable of precisely describing the geometric-thermal interaction state of the mold must first be established. This invention decomposes the macroscopic surface of the mold into microscopic computational units using discretized point cloud data and extracts the Gaussian curvature characterizing its geometric complexity and the thermal gradient characterizing its temperature field distribution, providing physical parameter support for the subsequent construction of a nonlinear compensation model.
[0043] Specifically, point cloud and temperature data of the composite material mold are acquired, and geometric and thermophysical characteristics are calculated to obtain a set of mold feature parameters, including mold point cloud data, mold geometric center coordinates, local Gaussian curvature, local thermal gradient modulus, global average curvature, standard deviation of curvature, and global average thermal gradient.
[0044] The geometric information of a single mold surface is obtained by 3D scanning and discretized to obtain mold point cloud data. The average position of all points in the mold point cloud data is calculated as the geometric center coordinates of the mold. The surface temperature data of the mold during the highest temperature holding stage of the curing process is obtained and recorded as temperature data. The temperature data is mapped to each point in the mold point cloud data according to the spatial coordinate correspondence. The neighborhood point set of a single point in the mold point cloud data is obtained, a quadratic surface is fitted, and the curvature of the point is calculated based on the principle of surface differential geometry. The local Gaussian curvature is calculated. At the same time, the temperature field equation is constructed based on the local spatial coordinates and temperature values of the point and its neighborhood point set. The temperature gradient modulus of the point is calculated to obtain the local thermal gradient modulus. The arithmetic mean and standard deviation of the absolute values of all local Gaussian curvatures are statistically analyzed to obtain the global average curvature and standard deviation of curvature. The mean of all local thermal gradient moduli is calculated to obtain the global average thermal gradient.
[0045] At this point, the set of mold feature parameters for a single mold has been obtained.
[0046] S2: Obtain the geometric thermal sensitivity index based on the amplification effect of local Gaussian curvature on the thermal sensitivity of the mold; calculate the expansion anisotropy coefficient based on the relationship between the geometric thermal sensitivity index and the overall geometric complexity on the nonlinearity of mold expansion; calculate the thermal expansion displacement compensation amount based on the expansion anisotropy coefficient and the relationship between the thermal expansion displacement and the thermal expansion factor and the actual deformation correction.
[0047] It should be noted that after obtaining the mold's characteristic parameter set, this invention faces the challenge of assessing the heat sensitivity of each point on the mold. In actual working conditions, even if two regions of the mold have the same temperature, points in areas of severe bending (i.e., high curvature regions) often experience much greater thermal stress deformation due to the compression or tension of surrounding materials compared to points in flat regions. This difference in thermal deformation induced by geometry is one of the main reasons for springback or warping of composite parts after demolding. Therefore, this invention constructs a geometric thermal sensitivity index, which not only considers temperature-driven forces but also introduces local Gaussian curvature as a nonlinear amplification factor. By constructing the geometric thermal sensitivity index, the system can identify high-risk regions that, although not at high temperatures, are highly susceptible to distortion due to their complex structures, thus giving these regions higher weight in subsequent compensation calculations.
[0048] Specifically, based on the amplification effect of local Gaussian curvature on the thermal sensitivity of the mold, a geometric thermal sensitivity index is obtained, including:
[0049] The geometric thermal sensitivity index satisfies the following expression:
[0050] ;
[0051] In the formula, Indicates the midpoint of the mold point cloud data The geometric thermal sensitivity index, in degrees Celsius; Midpoint of mold point cloud data The corresponding temperature data; Provides real-time indoor reference temperature; For point Local Gaussian curvature; The global average curvature of the mold; It should be the first minimum positive number to prevent the denominator from being zero; It is an exponential function with the natural constant as its base.
[0052] In the formula, Characterized the point The fundamental thermal driving force is that the greater the temperature difference, the greater the expansion potential. This point is characterized The degree of significance of the geometric features relative to the overall morphology, i.e., the normalized curvature; This is used to amplify the effects of high curvature regions nonlinearly; that is, under the same temperature difference, the larger the geometric thermal sensitivity index, the more representative the point. The greater the tendency for thermal deformation caused by geometric constraints such as deep cavities and corners.
[0053] For example, global mean curvature For a point A in a flat region, , , For a certain corner region point B, , ,but The calculation results show that, although the temperature difference is the same, the geometric thermal sensitivity index of the high curvature region is much higher than that of the flat region, which is consistent with the law of thermal stress concentration. , All values are rounded to one decimal place.
[0054] Thus, the geometric thermal sensitivity index of any region of the mold was obtained.
[0055] It should be noted that traditional thermal expansion compensation often employs simple linear scaling, which ignores the thermal gradient effect and geometric constraint effect of the mold. In large composite material molds, heat flow is often non-uniform, leading to significant differences in thermal gradients across the mold. Simultaneously, the complexity of the geometry also contributes to anisotropic deformation. This invention introduces a dimensionless expansion anisotropy coefficient. This coefficient cleverly separates the non-uniformity caused by the thermal gradient from the constraint caused by the geometric structure into two independent driving sources. Dimensionless processing eliminates unit interference, aiming to generate a dynamic correction coefficient. When this coefficient is greater than 1, it indicates an additional expansion tendency in that region, requiring the system to break away from traditional linear laws and apply stronger nonlinear compensation to counteract the actual physical deformation under complex conditions.
[0056] Preferably, the anisotropy coefficient of expansion is calculated based on the relationship between the geometric thermal sensitivity index and the overall geometric complexity influencing the degree of nonlinearity of mold expansion, including:
[0057] The maximum value among the geometric thermal sensitivity indices corresponding to all points in the mold point cloud data is recorded as the peak value of the thermal sensitivity index.
[0058] The expansion anisotropy coefficient satisfies the following expression:
[0059] ;
[0060] In the formula, Point The expansion anisotropy coefficient is dimensionless; For point The local thermal gradient modulus; The global average thermal gradient is expressed in units of 1 / 2π and 1 / 2π. Maintain consistency; For point Geometric thermal sensitivity index; This represents the peak value of the heat sensitivity index. The global average curvature; The standard deviation of curvature; It is the natural logarithm function; , , These are the second smallest positive number, the third smallest positive number, and the fourth smallest positive number, respectively, used to ensure that the denominator is not zero.
[0061] In the formula, This constitutes a dimensionless thermal gradient ratio, eliminating the interference of length units on the calculation results; The term represents the additional deformation weight caused by uneven heat flow, and it automatically resets to zero when the thermal gradient is 0. The exponential term represents the weight of deformation caused by the geometric structure. The geometric complexity factor, in its physical sense, represents the superposition of the nonlinearity caused by the thermal gradient and the nonlinearity caused by geometric constraints as two independent physical driving sources, corrected based on fundamental linear expansion. Even in extremely uniform temperature fields, complex geometric structures can still cause... This triggers nonlinear compensation, which is consistent with physical facts.
[0062] For example, , ,but 2. Assuming a global average thermal gradient A certain point is located in a region of high thermal gradient. , , If the geometric thermal sensitivity index is high, Then the geometric term is Finally, at this point This means that, under the combined effects of a strong thermal gradient and complex geometry, the actual expansion effect at this point is 2.25 times the theoretical linear value. Retain to three decimal places.
[0063] Thus, the expansion anisotropy coefficient of any region of the mold was obtained.
[0064] It should be noted that in order to convert the anisotropic coefficient of thermal expansion into instructions that can be executed by CNC machining, it must be mapped back to the displacement in physical space. The core of this invention lies in returning to the physical essence, that is, using the inherent standard linear thermal expansion coefficient of the material as a benchmark, combined with the characteristic length and temperature difference of each point relative to the centroid of the mold, to calculate a theoretical linear expansion amount. Then, the anisotropic coefficient dynamically corrects this theoretical value. The advantage of this calculation method is that it retains the basic role of the material's physical properties while incorporating the nonlinear effects of complex fields, thereby calculating the precise thermal expansion displacement compensation amount for each point, ensuring that the mold surface can accurately offset deformation at high temperatures and achieve the design-required dimensions.
[0065] Preferably, the thermal expansion displacement compensation is calculated based on the expansion anisotropy coefficient and the combined effect of the thermal expansion displacement factor and the actual deformation correction, including:
[0066] The Euclidean distance between each point in the mold point cloud data and the geometric center of the mold is recorded as the characteristic length of that point; the standard linear thermal expansion coefficient, which characterizes the basic thermal expansion properties of the material, is obtained from the database;
[0067] The thermal expansion displacement compensation amount satisfies the following expression:
[0068] ;
[0069] In the formula, Point The thermal expansion displacement compensation amount, with the dimension of length; For point The characteristic length; The standard linear thermal expansion coefficient is obtained in advance; Midpoint of mold point cloud data The corresponding temperature data; Provides real-time indoor reference temperature; Point The expansion anisotropy coefficient.
[0070] In the formula, It is a theoretical linear expansion amount based on the physical properties of materials; As a nonlinear correction coefficient derived from the thermo-geometric interaction field, the theoretical value is dynamically adjusted. When When the thermal expansion displacement compensation is equal to that of traditional linear scaling; when When this occurs, it indicates that there is an additional expansion trend in the region, and the system will automatically increase the reverse thermal expansion displacement compensation to offset this nonlinear error.
[0071] For example, , Temperature difference 100℃; Traditional linear calculation value = ,like Therefore, the thermal expansion displacement compensation amount calculated by this invention is: The displacement direction is along the normal vector direction of that point in the mold point cloud data.
[0072] It should be noted that, Figure 2This is an example chart showing the calculation results of thermal expansion error compensation at various points in a composite material mold. The table systematically lists the temperature, local Gaussian curvature, local thermal gradient modulus, geometric thermal sensitivity index, expansion anisotropy coefficient, and thermal expansion displacement compensation at different points. Typical locations such as points A, B, C, and D are clearly marked in the chart. Point A is a flat area. , Point B is a region of high curvature. , = Point C is a region with a high thermal gradient. , = Point D is a low-temperature region. , = By comparing the numerical differences of various parameters and compensation amounts, the significant differences in thermal expansion compensation requirements in different regions under the influence of geometric characteristics and thermal field distribution are clearly demonstrated.
[0073] Thus, the thermal expansion displacement compensation amount for any region of the mold was obtained.
[0074] S3: Based on the thermal expansion displacement compensation, the coordinates of each point in the mold point cloud data are compensated to obtain the mold compensation point cloud set; the mold compensation point cloud set is fitted into a curved surface entity to obtain the final mold compensation model.
[0075] It should be noted that since the thermal expansion of the mold mainly extends outward along the normal direction of the material surface, the compensation operation must be a reverse pre-deformation to counteract this expansion. This means that during the mold manufacturing stage at room temperature, the mold profile is intentionally contracted inward by a specific amount, i.e., the thermal expansion displacement compensation. This invention precisely adjusts the spatial coordinates of each point in the mold point cloud data through normal vector calculations. This is not only an update to the mold point cloud data but also a crucial step in transitioning from theoretical calculation to geometric reconstruction, ensuring that after undergoing high-temperature expansion, the mold profile grows to the precise dimensions required by the design.
[0076] Specifically, based on the thermal expansion displacement compensation, the coordinates of each point in the mold point cloud data are compensated to obtain a mold compensated point cloud set, including:
[0077] The local area of the mold is fitted using a data fitting method to obtain a local fitting plane. The normal vector of the local fitting plane is the normal vector of the local area, denoted as the normal vector of each point in the mold point cloud data. The direction of the normal vector of each point in the mold point cloud data is taken as the displacement direction. The original coordinates of each point in the mold point cloud data are superimposed with the product of the thermal expansion displacement compensation amount and the normal vector, and the superimposed results are integrated to obtain the mold compensation point cloud set.
[0078] At this point, the set of mold compensation points for the mold has been obtained.
[0079] It should be noted that although the mold compensation point cloud set after coordinate adjustment contains compensation information numerically, it is still a discrete set of points and cannot be directly used for CNC machine tool programming. Industrial manufacturing requires continuous, smooth surface models with topological structures. Furthermore, discrete point clouds may introduce minute numerical noise during calculation and adjustment, which, if left untreated, can lead to a rough mold surface. Therefore, this invention transforms the discrete compensation point cloud into a high-quality NURBS surface through surface reconstruction and smoothing. This not only corrects potential computational noise but also provides the necessary digital twin model for subsequent high-precision machining.
[0080] Preferably, the mold compensation point cloud set is fitted into a curved surface solid to obtain the final mold compensation model, including:
[0081] Using a surface fitting algorithm, the mold compensation point cloud set is reconstructed into a continuous surface entity, and the local noise of the surface entity is smoothed to obtain the final mold compensation model.
[0082] At this point, the final mold compensation model of the mold was obtained.
[0083] It should be noted that, Figure 3 This is a schematic diagram of the digital model of the mold surface reconstructed based on compensated point cloud. The points are marked in the diagram. The value of thermal expansion displacement compensation corresponding to the location, i.e. Furthermore, the color gradient visually distinguishes the compensation amount distribution in different areas of the mold surface. Areas with a darker color gradient correspond to areas with high compensation amount, while areas with a lighter gradient correspond to areas with low compensation amount. At the same time, the difference in arrow length is used to further present the magnitude of the compensation displacement at each point, with the arrow at point A being shorter and the arrow at high compensation area being longer. Through the dual comparison of color gradient and arrow, the gradient difference in thermal expansion compensation displacement in different areas under the influence of complex geometry and thermal field of the mold surface is shown.
[0084] S4: Convert the compensated mold surface digital model into CNC machining code and transmit it to the machining center for mold manufacturing; perform closed-loop optimization of mold thermal expansion compensation based on the feedback of the residual data of the first mold.
[0085] It should be noted that the final mold compensation model presents a non-designed shape at room temperature. This shape is a compensation structure specifically designed for the high-temperature thermal expansion characteristics of molds. By using CAM software to compile CNC machining code, the mold blank is precisely cut according to the surface parameters of the model. After the mold is heated and expanded in the high-temperature curing environment of the autoclave, the shape of the mold exactly matches the product design dimensions, thus realizing the transformation from theoretical compensation calculation to the actual precise manufacturing of the mold.
[0086] Specifically, the compensated digital model of the mold surface is converted into CNC machining code and transmitted to the machining center for mold manufacturing, including:
[0087] The final mold compensation model is processed using CAM software, including setting toolpaths, cutting parameters, machining coordinate system and tool approach / retraction strategies. The process planning results are then converted into G-code that conforms to the control system standard of the machining center and transmitted to the machining center via industrial communication protocol.
[0088] At this point, the CNC machining code for the mold was obtained.
[0089] It should be noted that relying solely on the open-loop compensation of the theoretical model cannot completely eliminate errors. The actual thermal expansion of composite materials is affected by random factors such as resin content, fiber batch, and curing heating rate. Therefore, this invention captures the deviation between theoretical prediction and actual results through the measured data of the first product, and then feeds the deviation back to the algorithm core to correct basic parameters such as the standard linear thermal expansion coefficient. As the number of processing times increases, the system's prediction accuracy continues to approach the real thermal expansion law, ultimately achieving intelligent closed-loop manufacturing.
[0090] Preferably, based on the feedback from the residual data of the first mold piece, closed-loop optimization of mold thermal expansion compensation is performed, including:
[0091] The system acquires 3D scanning data of the first mold and aligns it with the product design model. It calculates the deviation between the actual size and the design size of each inspection point. It statistically analyzes the distribution characteristics of the deviation values. If the maximum or average deviation value exceeds the preset accuracy threshold, the residual data is fed back to the database. Based on the residual data, the standard linear thermal expansion coefficient of the material is corrected in reverse to achieve closed-loop optimization of mold thermal expansion compensation.
[0092] This completes the compensation for thermal expansion error data of a composite material mold.
[0093] This invention also discloses a composite material mold thermal expansion error data compensation system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a composite material mold thermal expansion error data compensation method according to the present invention is implemented.
[0094] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.
[0095] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.
Claims
1. A method for compensating for thermal expansion error data in composite material molds, characterized in that, include: The point cloud and temperature data of the composite material mold are acquired, and the geometric and thermophysical characteristics are calculated to obtain a set of mold feature parameters including mold point cloud data, mold geometric center coordinates, local Gaussian curvature, local thermal gradient modulus, global average curvature, curvature standard deviation, and global average thermal gradient. Based on the amplification effect of local Gaussian curvature on the thermal sensitivity of the mold, the geometric thermal sensitivity index is obtained. In the formula, Indicates the midpoint of the mold point cloud data The geometric thermal sensitivity index, in degrees Celsius; Midpoint of mold point cloud data The corresponding temperature data; Provides real-time indoor reference temperature; For point Local Gaussian curvature; The global average curvature of the mold; It is the first minimum positive number to prevent the denominator from being zero; It is an exponential function with the natural constant as its base; Based on the relationship between the geometric thermal sensitivity index and the overall geometric complexity influencing the nonlinearity of mold expansion, the expansion anisotropy coefficient is calculated, including: the maximum value of the geometric thermal sensitivity index corresponding to all points in the mold point cloud data is recorded as the peak value of the thermal sensitivity index; the expansion anisotropy coefficient satisfies the following expression: In the formula, Point The expansion anisotropy coefficient is dimensionless; For point The local thermal gradient modulus; The global average thermal gradient is expressed in units of 1 / 2π and 1 / 2π. Maintain consistency; This represents the peak value of the heat sensitivity index. The standard deviation of curvature; It is the natural logarithm function; , , These are the second, third, and fourth minimum positive numbers, used to ensure that the denominator is not zero; the thermal expansion displacement compensation is calculated based on the expansion anisotropy coefficient and the combined effect of thermal expansion displacement, thermal expansion factor, and actual deformation correction. Based on the thermal expansion displacement compensation, the coordinates of each point in the mold point cloud data are compensated to obtain a mold compensation point cloud set. This includes: fitting a local area of the mold using a data fitting method to obtain a local fitting plane, where the normal vector of the local fitting plane is the normal vector of the local area, denoted as the normal vector of each point in the mold point cloud data; taking the direction of the normal vector of each point in the mold point cloud data as the displacement direction; superimposing the original coordinates of each point in the mold point cloud data with the product of the thermal expansion displacement compensation and the normal vector, and integrating the superimposed results to obtain the mold compensation point cloud set; fitting the mold compensation point cloud set into a curved surface entity to obtain the final mold compensation model. The compensated mold surface digital model is converted into CNC machining code and transmitted to the machining center for mold manufacturing; based on the feedback of the residual data of the first mold, closed-loop optimization of mold thermal expansion compensation is performed.
2. The method for compensating for thermal expansion error data in composite material molds according to claim 1, characterized in that, The mold feature parameter set includes: Obtain mold point cloud data, calculate the average position of all points in the mold point cloud data, and use it as the geometric center coordinates of the mold; obtain the curing temperature data of the mold surface, record it as temperature data, and map the temperature data to each point in the mold point cloud data according to the spatial coordinate correspondence; obtain the neighborhood point set of a single point in the mold point cloud data, fit a quadratic surface and calculate the curvature of the point based on the principle of surface differential geometry, calculate the local Gaussian curvature, calculate the temperature gradient modulus of the point, and obtain the local thermal gradient modulus; calculate the arithmetic mean and standard deviation of the absolute values of all local Gaussian curvatures to obtain the global average curvature and standard deviation of curvature, calculate the mean of all local thermal gradient moduli, and obtain the global average thermal gradient.
3. The method for compensating for thermal expansion error data in composite material molds according to claim 1, characterized in that, The calculation of thermal expansion displacement compensation includes: The Euclidean distance between each point in the mold point cloud data and the geometric center of the mold is denoted as the characteristic length of that point; the standard linear thermal expansion coefficient, which characterizes the basic thermal expansion properties of the material, is obtained from the database; the thermal expansion displacement compensation amount satisfies the following expression: ； In the formula, Point The thermal expansion displacement compensation amount, with the dimension of length; For point The characteristic length; The standard linear thermal expansion coefficient is obtained in advance; Midpoint of mold point cloud data The corresponding temperature data; Provides real-time indoor reference temperature; Point The expansion anisotropy coefficient.
4. The method for compensating for thermal expansion error data in composite material molds according to claim 1, characterized in that, The final mold compensation model obtained includes: Using a surface fitting algorithm, the mold compensation point cloud set is reconstructed into a continuous surface entity, and the local noise of the surface entity is smoothed to obtain the final mold compensation model.
5. The method for compensating for thermal expansion error data in composite material molds according to claim 1, characterized in that, The process of converting the compensated digital model of the mold surface into CNC machining code includes: The final mold compensation model is processed using CAM software, including setting toolpaths, cutting parameters, machining coordinate system and tool approach / retract strategy. The process planning results are then converted into G-code that conforms to the control system standard of the machining center and transmitted to the machining center via industrial communication protocol.
6. The method for compensating for thermal expansion error data in composite material molds according to claim 1, characterized in that, The closed-loop optimization for mold thermal expansion compensation includes: The system acquires 3D scanning data of the first mold and aligns it with the product design model. It calculates the deviation between the actual size and the design size of each inspection point. It statistically analyzes the distribution characteristics of the deviation values. If the maximum or average deviation value exceeds the preset accuracy threshold, the residual data is fed back to the database. Based on the residual data, the standard linear thermal expansion coefficient of the material is corrected in reverse to achieve closed-loop optimization of mold thermal expansion compensation.
7. A composite material mold thermal expansion error data compensation system, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a method for compensating for thermal expansion error data of a composite material mold according to any one of claims 1-6.