A method and system for predicting the quality of a laser powder bed fusion formed part

Abstract

The application relates to the technical field of additive manufacturing quality control and process optimization, and specifically provides a laser powder bed fusion forming part quality prediction method and system, which comprises the following steps: acquiring three-dimensional point cloud data of a forming part fused by a laser powder bed; dividing the aligned three-dimensional point cloud data into a plurality of cross-section point sets along a central axis direction; independently performing local geometric fitting on each cross-section point set to obtain local fitting parameters of each cross section; based on the three-dimensional point cloud data and the local fitting parameters of each cross section, expanding a three-dimensional original surface of the forming part into a two-dimensional surface deviation topology graph; and based on the two-dimensional surface deviation topology graph, calculating surface roughness and statistically evaluating process-induced macro-geometric deviation of the forming part. The closed loop from process prediction to result evaluation provides strong data support for accurate decoupling and integrated characterization of two key quality characteristics of the LPBF forming part, and opens up a new way for digital preposition optimization of the process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of additive manufacturing quality control and process optimization, and particularly relates to a laser powder bed fusion forming part quality prediction method. BACKGROUND

[0002] As an advanced metal additive manufacturing technology, laser powder bed fusion (LPBF) has been widely used in the fields of aerospace, medical devices, etc. However, its layer-by-layer fusion characteristics also introduce complex quality problems, mainly manifested as the coupling of macro-geometric distortion and micro-surface morphology. Macro-distortion is caused by residual stress due to large thermal gradient, resulting in deformation such as bending and warping; micro-morphology is affected by factors such as partial melting powder adhesion and molten pool dynamic behavior, and is usually manifested as high surface roughness. The coupling of multi-scale errors makes the traditional evaluation method based on global ideal model fitting invalid, and cannot independently and accurately quantify the geometric accuracy and surface quality. At the same time, the process optimization method relying on physical experiments is costly and time-consuming. SUMMARY

[0003] In view of the above problems, the present application provides a laser powder bed fusion forming part quality prediction method, which can accurately predict the geometric and surface quality of the LPBF forming part by segmenting and locally fitting the three-dimensional point cloud data of the aligned LPBF forming part, and then expanding the three-dimensional original surface of the forming part into a two-dimensional surface deviation topology map based on the three-dimensional point cloud data and the local fitting parameters of each cross section for integrated quality evaluation.

[0004] To achieve the purpose of the present application, the present application provides the following technical solutions:

[0005] In a first aspect, the present application provides a laser powder bed fusion forming part quality prediction method, comprising:

[0006] Obtaining three-dimensional point cloud data of a laser powder bed fusion forming part;

[0007] Performing center axis alignment processing on the three-dimensional point cloud data, and segmenting the aligned three-dimensional point cloud data into a plurality of cross-sectional point sets along the center axis direction;

[0008] Performing independent local geometric fitting on each cross-sectional point set to obtain local fitting parameters of each cross section;

[0009] Based on the three-dimensional point cloud data and the local fitting parameters of each cross section, expanding the three-dimensional original surface of the forming part into a two-dimensional surface deviation topology map;

[0010] Calculate the surface roughness of the shaped part based on the two-dimensional surface deviation topology map, and statistically evaluate the process-induced macro-geometric deviation of the shaped part based on the local fitting parameters of the cross sections.

[0011] Further improvements of the present application are that the center axis alignment processing of the three-dimensional point cloud data comprises: determining a first principal component vector by using a principal component analysis algorithm, and determining the first principal component vector as an actual center main axis of the shaped part; and obtaining the aligned three-dimensional point cloud data by aligning the actual center main axis with a preset axis of a coordinate system through a rotation matrix.

[0012] Further improvements of the present application are that the three-dimensional point cloud data is segmented into a plurality of cross-sectional point sets along the direction of the center axis after alignment, comprising: defining a series of equidistant virtual slice planes along the direction of the center axis; defining a thickness tolerance zone around each virtual slice plane, and extracting three-dimensional points falling within the thickness tolerance zone for each virtual slice plane, and taking the coordinates perpendicular to the center axis to form the cross-sectional point set.

[0013] Further improvements of the present application are that the local geometric fitting is independently performed for each cross-sectional point set to obtain the local fitting parameters of each cross section, comprising: for each cross-sectional point set, a group of inliers conforming to the main contour is identified from the cross-sectional point set by using a random sample consensus algorithm to form an inlier set; and the inlier set is optimized by using a Levenberg-Marquardt algorithm to find the optimal circle parameters to determine the local fitting parameters; wherein for each inlier in the inlier set, the residual is defined as the difference between the actual radial distance of the point to the fitted circle and the fitted radius.

[0014] Further improvements of the present application are that the local fitting parameters of each cross section form a local fitting parameter sequence; and the three-dimensional original surface of the shaped part is unfolded into a two-dimensional surface deviation topology map based on the three-dimensional point cloud data and the local fitting parameters of each cross section, comprising: for any point in the three-dimensional point cloud data, a dynamic local fitting reference of the axial position of the point is established by interpolating the discrete local fitting parameter sequence; the radial deviation value of any point in the three-dimensional point cloud data relative to the dynamic local fitting reference of the point and the circumferential angle coordinate of the point in the unfolded coordinate system are calculated; the circumferential angle coordinate, axial position coordinate and radial deviation value of all points in the three-dimensional point cloud data are mapped onto the created regular two-dimensional grid based on natural neighborhood interpolation to generate the two-dimensional surface deviation topology map.

[0015] The further improvement of the present application is that the surface roughness of the shaped part is calculated based on the two-dimensional surface deviation topological graph, including: applying a two-dimensional Gaussian filter to the two-dimensional surface deviation topological graph to separate a filtered roughness matrix; the standard deviation of the two-dimensional Gaussian filter is calculated from the cut-off wavelength and the pixel density of the grid set by the user, and the relationship is as follows: ; wherein, is the standard deviation of the two-dimensional Gaussian filter; is the cut-off wavelength; is the pixel density; based on the roughness matrix, the surface roughness is calculated; the surface roughness includes the surface roughness parameter and the average axial profile roughness distributed along the circumference.

[0016] The further improvement of the present application is that the local fitting parameters of each cross section include a local radius fitting sequence and a local fitting center coordinate sequence of all cross sections; the process-induced macro-geometric deviation of the shaped part is statistically evaluated based on the local fitting parameters of each cross section, including: statistically analyzing the local fitting radius sequence to evaluate the size deviation of the shaped part; statistically analyzing the local fitting center coordinate sequence to evaluate the waviness or bending deformation of the shaped part caused by thermal stress; wherein, the statistical analysis of the local fitting center coordinate sequence includes: calculating the distance from each circle center to the actual center main axis to form a distance sequence, and then quantitatively representing the overall bending deformation caused by thermal stress based on the maximum value or the root mean square value of the distance sequence.

[0017] The further improvement of the present application is that the shaped part is a predicted shaped part simulated by a multi-physical field numerical model of a laser powder bed fusion process; the method further includes: establishing a multi-physical field numerical model of a laser powder bed fusion process, simulating the forming process based on the forming process parameters of the shaped part to be formed in the multi-physical field numerical model, and obtaining a predictive three-dimensional surface data set of the shaped part; and converting the predictive three-dimensional surface data set into three-dimensional point cloud data.

[0018] The further improvement of the present application is that the laser powder bed fusion shaped part is an actual shaped part manufactured by physical manufacturing, and for the actual shaped part, a scanning technology is used to obtain an actual three-dimensional surface data set of the surface topography of the actual shaped part; the method further includes: comparing a first evaluation result based on the predictive three-dimensional surface data set and a second evaluation result based on the actual three-dimensional surface data set to obtain a third evaluation result; and based on the third evaluation result, verifying and calibrating the multi-physical field numerical model, and / or feeding back and optimizing the laser powder bed fusion process parameters.

[0019] In a second aspect, the present application provides a laser powder bed fusion formed part quality prediction system for implementing the laser powder bed fusion formed part quality prediction method described above, comprising:

[0020] An acquisition module is configured to acquire three-dimensional point cloud data of a laser powder bed fusion formed part;

[0021] A processing module is configured to perform center axis alignment processing on the three-dimensional point cloud data, and segment the aligned three-dimensional point cloud data into a plurality of cross-sectional point sets along the center axis direction;

[0022] A fitting module is configured to perform independent local geometric fitting on each cross-sectional point set to obtain local fitting parameters of each cross section;

[0023] An unfolding module is configured to unfold the three-dimensional original surface of the formed part into a two-dimensional surface deviation topology map based on the three-dimensional point cloud data and the local fitting parameters of each cross section;

[0024] An evaluation module is configured to calculate the surface roughness of the formed part based on the two-dimensional surface deviation topology map, and statistically evaluate the process-induced macro-geometric deviation of the formed part based on the local fitting parameters of each cross section.

[0025] Compared with the prior art, the present application has the following beneficial effects:

[0026] Through the laser powder bed fusion formed part quality prediction method provided by the present application, the three-dimensional point cloud data of the formed part is segmented into a plurality of cross-sectional point sets along the center axis, and independent local geometric fitting is performed on each cross section. Then, based on a series of discrete local fitting parameters, a dynamic local fitting reference varying along the axis is established by interpolation, and the entire three-dimensional original surface is unfolded into a two-dimensional surface deviation topology map, thereby realizing comprehensive evaluation of the formed part quality. Through the technical path of "global alignment, local fitting, and overall unfolding", the present application separates the complex coupling error, and finally realizes the simultaneous, independent, and accurate quantitative characterization of the two key quality characteristics of the laser powder bed fusion formed part. The present application provides strong data support for the accurate decoupling and integrated characterization of the two key quality characteristics of the LPBF formed part, and opens up a new way for the digital pre-position optimization of the process. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings are included to provide a further understanding of the present application, and form a part of the specification, illustrate embodiments of the present application, and are used to explain the present application, and do not form a limitation on the present application;

[0028] Figure 1 An optional flowchart of a laser powder bed fusion formed part quality prediction method provided by an embodiment of the present application;

[0029] Figure 2 A structural schematic diagram of a three-dimensional model of a multi-physical field simulation formed columnar piece is provided for an embodiment of the present application.

[0030] Figure 3 A visualization schematic diagram of a deviation of a cylindrical piece from a nominal cylindrical piece is provided for an embodiment of the present application.

[0031] Figure 4 An optional flowchart of a laser powder bed fusion formed piece quality prediction method is provided for an embodiment of the present application.

[0032] Figure 5 An optional flowchart of a laser powder bed fusion formed piece quality prediction method is provided for an embodiment of the present application.

[0033] Figure 6 An optional flowchart of a laser powder bed fusion formed piece quality prediction method is provided for an embodiment of the present application.

[0034] Figure 7 A schematic diagram of a generated two-dimensional surface deviation topological map is provided for an embodiment of the present application.

[0035] Figure 8 A graph of a calculated average axial profile roughness Ra curve distributed along a circumference is provided for an embodiment of the present application.

[0036] Figure 9 A radius deviation distribution histogram of a macroscopic geometric deviation statistical analysis is provided for an embodiment of the present application.

[0037] Figure 10 A center offset distribution histogram of a macroscopic geometric deviation statistical analysis is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0039] The terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features; in the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0040] As one of the core technologies in the field of metal additive manufacturing, LPBF technology has been widely used in the high-end fields such as aerospace and medical devices, which have strict requirements on the performance and precision of components, due to its unique advantages in the forming of complex and precise components. This technology realizes three-dimensional entity manufacturing through the way of layer-by-layer laying of metal powder and selective laser melting and solidification, which can break through the process limitations of traditional subtractive manufacturing, efficiently form key components with lightweight topology structure and function-integrated characteristics, and provide important technical support for the structural innovation and performance upgrade of high-end equipment.

[0041] During the layer-by-layer melting process, the local high-energy heating of the laser beam on the powder layer causes the temperature of the molten pool area to rise sharply, while the rapid heat dissipation of the surrounding non-melted area and the substrate forms a significant temperature difference. This temperature difference causes thermal stress to accumulate inside the component, and after the forming is completed and cooled, the release of residual thermal stress will cause the component to appear macroscopic deformations such as bending, warping, and size contraction. Especially for large-size and thin-wall components, such deformations are more significant, directly affecting the precision stability and application reliability of the component. At the same time, under the action of the laser, the metal powder is not completely melted, and part of the powder with smaller particle size or far away from the laser focus area only undergoes partial melting, which is easily adhered to the surface of the formed part after cooling to form a "powder sticking" defect, destroying the surface flatness. At the same time, the molten pool has complex behaviors such as dynamic flow, spatter, solidification shrinkage, etc. during the laser scanning process, and the irregular solidification of the molten pool boundary forms uneven melt channel textures on the surface of the formed part, and the unevenness of the interlayer fusion interface further aggravates the surface roughness. For components such as medical implants and precision transmission parts that have very high requirements on surface quality, excessive surface roughness not only affects the appearance, but also may cause subsequent problems such as decreased biocompatibility and increased stress concentration.

[0042] Currently, traditional quality evaluation methods are mostly based on global ideal model fitting comparison, that is, the actual three-dimensional data of the formed part is matched with the designed ideal model as a whole. This method cannot effectively distinguish the independent influence of macroscopic geometric distortion and microscopic surface defects, and is easy to misjudge the local deviation caused by surface roughness as macroscopic geometric error, or to cover up the real surface quality problem due to macroscopic deformation, resulting in distorted quantitative evaluation results of geometric precision and surface quality, which is difficult to guide the targeted adjustment of process parameters. In the process of process optimization, the existing method of designing multiple sets of different laser power, scanning speed, powder layer thickness, etc. to prepare a large number of entity samples and conduct quality detection, and then selecting the optimal parameters is costly and time-consuming, and it is difficult to quickly respond to the process optimization needs of different materials and different structural components.

[0043] To solve the above technical problems, the present application proposes the following technical solutions and corresponding embodiments.

[0044] The following will be combined with Figures 1 to 10The embodiments shown describe the technical solutions of the present application:

[0045] Embodiment 1

[0046] A laser powder bed fusion formed part quality prediction method according to an embodiment of the present application is used for comprehensive quality control of metal formed parts manufactured using LPBF technology, and refers to Figure 1 As shown, the laser powder bed fusion formed part quality prediction method according to the present embodiment includes the following steps S101 to S105:

[0047] Step S101, acquiring three-dimensional point cloud data of the laser powder bed fusion formed part.

[0048] In the present embodiment, the surface topography of the laser powder bed fusion formed part is reconstructed into three-dimensional point cloud data , , wherein Here, the laser powder bed fusion formed part is a predicted formed part simulated formed or an actual formed part manufactured physically; for the predicted formed part simulated formed, a multi-physics field numerical model is used to simulate the forming process to obtain a predictive three-dimensional surface data set of the predicted formed part; for the actual formed part, a scanning technique is used to obtain an actual three-dimensional surface data set of the surface topography of the actual formed part.

[0049] In the present embodiment, a multi-physics field numerical model of the laser powder bed fusion process is established, and based on the forming process parameters of the part to be formed, the forming process is simulated by the multi-physics field numerical model to obtain a predictive three-dimensional surface data set of the formed part, and the predictive three-dimensional surface data set is converted into three-dimensional point cloud data. In the present embodiment, reference is made to Figure 2 , Figure 3 As shown, the object to be measured is a cylindrical sample prepared using IN718 high-temperature alloy powder, with a nominal radius of 0.25 mm and a nominal height of 4 mm.

[0050] In the embodiments of the present application, the predictive three-dimensional surface data set refers to the surface topography and defect feature distribution information of the entire formed part obtained by simulating the forming process through a multi-physical field numerical model. In the embodiments of the present application, the numerical model adopted is a melt pool scale multi-physical field model coupled based on the lattice Boltzmann method and the height function method, combined with the slice CLI (Common Layer Interface) file (laser scanning path) of the model of the part to be formed and the adaptive mesh refinement technology, and then the scale of the multi-physical field numerical model is extended to the component scale, so as to realize the simulation of the state of each laser scanning powder bed process in the forming process under any process parameter condition, including the melting and spattering of the powder, the dynamic solidification behavior of the melt pool and the temperature accumulation effect, etc. In the simulation process, the system records the surface morphology data after the deposition of each layer layer by layer, and finally integrates all the layer data after the simulation ends to generate a complete predictive three-dimensional surface data set. Based on the data set, three-dimensional point cloud data can be directly or indirectly generated; the indirect method includes: first converting the predictive three-dimensional surface data set into a visual model in STL (StereoLithography) format, and then further extracting the corresponding three-dimensional point cloud data from the STL file.

[0051] In the embodiments of the present application, the obtained three-dimensional point cloud data is cleaned to remove invalid points containing non-numerical NaN (Not a Number) values, and valid point cloud data after cleaning is obtained .

[0052] Step S102, the center axis alignment processing is performed on the three-dimensional point cloud data, and the aligned three-dimensional point cloud data is divided into a plurality of cross-sectional point sets along the center axis direction.

[0053] In the embodiments of the present application, referring to FIG. 1, the content of the center axis alignment processing of the three-dimensional point cloud data by using the following steps S1021 to S1022 is described, including: Figure 4

[0054] Step S10211, a first principal component vector is determined by using a principal component analysis algorithm, and the first principal component vector is determined as the actual center main axis of the formed part.

[0055] In the embodiments of the present application, the valid point cloud data after cleaning is applied to principal component analysis (PCA) to calculate the covariance matrix and solve the eigenvalues and eigenvectors, and the eigenvector corresponding to the maximum eigenvalue is determined as the actual center main axis of the deformation sample.

[0056] ​Step S10212: Calculate the rotation matrix to make the actual central axis coincide with the preset axis of the coordinate system, and obtain the aligned three-dimensional point cloud data.

[0057] In this embodiment, to ensure that the actual center principal axis direction (principal axis direction) is consistent with the original design forming direction of the formed part (usually the +Z direction), the correlation coefficient between the projected coordinates in the principal axis direction and the original Z coordinates is calculated. If the correlation coefficient is positive, it indicates that the principal axis direction is consistent with the original design forming direction, and no adjustment is needed; if the correlation coefficient is negative, the principal axis vector should be reversed, i.e. .

[0058] In this embodiment, the corrected main axis is calculated. Rotate to the Z-axis of the coordinate system Rotation matrix required for coincidence The rotation matrix This can be obtained based on Rodrigues' rotation formula. Therefore, by performing a rotation transformation on all point cloud coordinates, we obtain the rotated and aligned point cloud. : ,in, For rotation matrix, Let be the transpose matrix of the point cloud coordinates; for the rotated point cloud, translate its Z-coordinate as a whole so that the minimum Z-coordinate is 0, that is, make the Z-coordinate of the lowest point 0, and recalculate the Z-coordinates of the remaining points according to their relative height to complete the final attitude correction: .

[0059] In this way, the three-dimensional point cloud data of the formed part (including the point cloud data that may have been affected by the attitude deviation due to macroscopic deformation) is unified into a standard coordinate system with the actual central axis as the Z-axis, ensuring that subsequent geometric analysis is based on a consistent spatial standard.

[0060] In the embodiments of this application, reference is made to Figure 5 As shown, the process of segmenting the aligned 3D point cloud data into multiple cross-sectional point sets along the central axis using steps S10221 to SS10222 is explained, including:

[0061] Step S10221: Define a series of equally spaced virtual slice planes along the central axis direction.

[0062] In this embodiment of the application, along the Z-axis direction, in Define within range There are three equally spaced virtual slice planes, each with a Z-coordinate of [missing value]. For example, It is 1500.

[0063] Step S10222: Define a thickness tolerance zone around each of the virtual slice planes. For each virtual slice plane, extract three-dimensional points that fall within the thickness tolerance zone and take the coordinates perpendicular to the central axis to form the cross-sectional point set.

[0064] In the embodiments of this application, around each A plane is defined as a thickness tolerance. Extract all that satisfy the condition. The three-dimensional point, and take its Coordinates, forming the first A set of two-dimensional cross-sectional points For example, thickness tolerance It can be 3 times the slice spacing.

[0065] Step S103: Perform independent local geometric fitting for each set of cross-sectional points to obtain the local fitting parameters for each cross-section.

[0066] In this embodiment, a two-stage robust fitting strategy is adopted: first, outliers are removed using the RANSAC (Random Sample Consensus) algorithm, and then the Levenberg-Marquardt nonlinear optimization algorithm is used to accurately fit the interior points.

[0067] Specifically, for each cross-sectional point set, a random sampling consensus algorithm is used to identify a set of interior points that conform to the main body contour from the cross-sectional point set, forming an interior point set; and the Levenberg-Mannelt algorithm is used to optimize the interior point set using nonlinear least squares method to find the optimal circle parameters and determine the local fitting parameters.

[0068] In this embodiment of the application, for each two-dimensional cross-sectional point set The random sampling consensus algorithm is applied iteratively to perform the following operations: randomly select 3 points to fit a candidate circle; calculate the point set. The radial distance from all points to the candidate circle is calculated; an inlier threshold is dynamically set based on the distribution of distances (such as Median Absolute Deviation, MAD), and points with distances less than the threshold are marked as inliers; finally, the inlier set corresponding to the model that obtains the maximum number of inliers is selected. .

[0069] Inner point set The Levenberg-Marquardt nonlinear least squares method is applied for iterative optimization, with the goal of finding the optimal circle parameters. To minimize the sum of squared residuals For each point in the interior set... Its residual Defined as the difference between the actual radial distance from the point to the fitted circle and the fitted radius: .

[0070] Repeat the above steps to obtain the local fitting parameter sequence for all effective cross sections. and the local fitting center coordinate sequence .

[0071] Step S104: Based on the three-dimensional point cloud data and the local fitting parameters of each cross section, unfold the three-dimensional original surface of the formed part into a two-dimensional surface deviation topology map.

[0072] In the embodiments of this application, reference is made to Figure 6 As shown, the content of step S104 can be achieved through the following steps S1041 to S1043:

[0073] Step S1041: For any point in the three-dimensional point cloud data, a dynamic local fitting reference for the axial position of the point is established by interpolating and discretizing the local fitting parameter sequence.

[0074] In this embodiment of the application, for the original aligned point cloud any point in By analyzing the parameter sequence and In its One-dimensional linear interpolation is performed at the coordinates to calculate the dynamic local fitting reference at the height of that point, i.e., the center of the interpolation circle. and interpolation radius .

[0075] Step S1042: Calculate the radial deviation value of any point in the three-dimensional point cloud data relative to the dynamic local fitting reference of that point and the circumferential angular coordinate of that point in the unfolded coordinate system.

[0076] In this embodiment, the radial deviation value dev of any point relative to its dynamic local fitting datum is calculated, and its circumferential angular coordinate theta in the unfolded coordinate system is also calculated. That is, based on the dynamic datum, the point is calculated... radial deviation and circumferential angle : , ;in, For point x-coordinate For point The ordinate, The interpolation radius; The x-coordinate of the interpolation circle center. is the longitudinal coordinate of the interpolated center of the circle; is a function that calculates the square root of a non-negative number.

[0077] Step S1043, mapping the circumferential angle coordinate, axial position coordinate and radial deviation value of all points in the three-dimensional point cloud data to the created regular two-dimensional grid based on natural neighborhood interpolation, to generate the two-dimensional surface deviation topology map.

[0078] In the embodiments of the present application, referring to FIG. 2, the circumferential angle coordinate, axial position coordinate and radial deviation value of all points are generated into the two-dimensional surface deviation topology map through interpolation; that is, the unwrapped coordinates and deviation values of all points are generated into a two-dimensional topology map through interpolation. Figure 7

[0079] In the embodiments of the present application, a regular two-dimensional grid is created, and the coordinate axes thereof are circumferential angle and axial position . The scatter point data of all points are mapped to the regular grid by applying a scatter point interpolation method to generate a two-dimensional surface deviation topology map matrix . In the embodiments, the scatter point interpolation method is natural neighborhood interpolation.

[0080] Step S105, calculating the surface roughness of the formed part based on the two-dimensional surface deviation topology map, and statistically evaluating the process-induced macro-geometric deviation of the formed part based on the local fitting parameters of each cross section.

[0081] In the embodiments of the present application, integrated quality evaluation of a laser powder bed fusion formed part is performed. After applying Gaussian filtering on the topology map, the surface roughness parameters such as Sa and Ra are calculated, and the macro-geometric deviation is statistically evaluated based on the local fitting parameter sequence.

[0082] For surface roughness evaluation, the following contents of steps S10511 to S10513 are used for illustration:

[0083] Step S10511, applying a two-dimensional Gaussian filter to the two-dimensional surface deviation topology map matrix to separate out the surface roughness, and the standard deviation of the filter is calculated according to the cutoff wavelength and the pixel density (i.e. the number of pixels corresponding to each millimeter) of the grid , and the formula is: , thereby obtaining the filtered roughness matrix . As a feasible implementation manner, .

[0084] ​​Step S10512, in the roughness matrix Above, calculate surface roughness parameters (Arithmetic mean height) and (Root mean square height): , .

[0085] In this embodiment, the surface roughness parameter Sa is calculated based on the filtered data according to the ISO 25178 standard.

[0086] Step S10513: For each circumferential angle in the topology graph Each row of the matrix represents a profile line along the Z-axis; the roughness of this profile line is calculated according to ISO 25178 standard. ,Right now And thus all angles The values ​​are plotted as a curve, for reference. Figure 8 As shown, it can be used to analyze the surface quality in different directions. For each pillar of the sample with different forming directions, at least 2000 intersecting cross sections are randomly selected for convergence analysis to verify the sufficiency and representativeness of the number of intersecting cross sections in capturing the geometric features and surface morphology distribution of the pillar.

[0087] In the embodiments of this application, reference is made to Figure 9 , Figure 10 As shown, for the evaluation of macroscopic geometric deviation, 1) analyze the radius sequence. The difference between its mean and nominal radius reflects the overall dimensional deviation, and its variation with... The changing trend can be used to quantify taper or bulge; 2) Analyze the center coordinate sequence Calculate the distance from the center of each circle to the Z-axis. The maximum or root mean square value of this distance sequence can be used to quantitatively characterize the overall bending deformation caused by thermal stress.

[0088] In the embodiments of this application, the multiphysics numerical model can be verified and calibrated by comparing the first evaluation result based on the predictive three-dimensional surface dataset and the second evaluation result based on the actual three-dimensional surface dataset, and / or the laser powder bed melting process parameters can be optimized by feedback.

[0089] The laser powder bed fusion forming part quality prediction method provided by the embodiment has the technical effect that through a series of feature combinations, the macroscopic geometric deviation and microscopic surface quality of the forming part are accurately decoupled and integrally evaluated. First, the three-dimensional point cloud data of the forming part is obtained, and the central principal axis thereof is determined and aligned by using a principal component analysis algorithm, thereby establishing a unified and standard coordinate reference for subsequent analysis and eliminating errors introduced by posture deviation of the workpiece in the measurement process. Then, the method divides the point cloud into a plurality of cross-sectional point sets along the central axis, and performs independent local geometric fitting on each cross section. The fitting uses a random sample consensus algorithm to identify and remove outlier points caused by defects such as 'powder sticking', and then performs nonlinear least squares optimization on the inliers conforming to the main contour by using a Levenberg-Marquardt algorithm, so as to accurately obtain the dynamic fitting parameters of each cross section. Based on the series of discrete local fitting parameters, a dynamic local fitting reference varying along the axis is established by interpolation, and then the entire three-dimensional original surface is unfolded into a two-dimensional surface deviation topology map. The coordinate axes of the topology map are the axial position and the circumferential angle of the part, and the value is the radial deviation of each point relative to the dynamic reference. Finally, the comprehensive evaluation of the quality is realized based on the topology map. On the one hand, the high-frequency components are separated by applying two-dimensional Gaussian filtering and the like, so as to independently and accurately calculate the surface roughness. On the other hand, the macroscopic geometric deviation caused by process factors, such as size deviation, waviness or overall bending deformation, can be quantitatively evaluated by statistically analyzing the sequence of local fitting parameters.

[0090] In summary, the method separates the complex coupled errors through the technical path of 'global alignment, local fitting, and overall unfolding', and finally realizes the simultaneous, independent and accurate quantitative characterization of the two key quality characteristics of the laser powder bed fusion forming part. Through the closed loop from process prediction to result evaluation, problems in the part manufacturing process can be predicted, thereby solving potential manufacturing risks, accelerating the iterative optimization of the design and manufacturing of actual parts, providing effective protection for actual high-quality and performance forming parts, and greatly reducing the cost and unnecessary energy consumption of experiments.

[0091] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method in any of the embodiments of the present application. Specifically, a system or device equipped with a storage medium can be provided, and the storage medium stores software program code for implementing the functions of any of the above embodiments, and the computer of the system or device reads and executes the program code stored in the storage medium.

[0092] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program in accordance with embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable storage medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication section, and / or installed from a removable medium. When the computer program is executed by a central processing unit, the above-described functions defined in the system of the present application are performed.

[0093] It should be noted that the computer readable storage medium shown in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (flash memory), an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a carrier wave in a propagated data signal, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium can also be any computer readable storage medium that can send, propagate or transfer a program for use by or in connection with an instruction execution system, device or apparatus. The program code contained on the computer readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the above.

[0094] The computer program product of the present application can be a computer program implemented on one or more computers. The program itself can be stored on a computer readable medium, such as a hard disk, a CD-ROM, a USB flash drive, a RAM, or an SD card. The program can be implemented in a high level programming language, and can be executed directly by an electronic digital computer, or after having been compiled.

[0095] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can be located in a single processor, or can be distributed on multiple processors.

[0096] It should be noted that although several modules or units of the device for action execution are mentioned in the foregoing detailed description, such a division is not mandatory. Indeed, according to an embodiment of the application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units.

[0097] In several embodiments provided in the present application, it should be understood that the disclosed system, modules and methods can be implemented in other ways. For example, the above-described module embodiments are only schematic. For example, the division of the units is only a logical function division, and actual implementation can be another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, and can be electrical, mechanical or other form.

[0098] The above examples are only used to illustrate the technical solutions of the present application, but not to limit them. The present application is not limited to the exact structure as has been described above and shown in the drawings, and the specific implementation of the present application should not be considered as limited to these descriptions. Any changes and modifications made by those skilled in the art without departing from the concept of the present application should be considered as falling within the scope of the present application.

Claims

1. A method for predicting the quality of laser powder bed fused parts, characterized in that, include: Acquire three-dimensional point cloud data of the formed part produced by laser powder bed melting; The three-dimensional point cloud data is aligned with the central axis, and the aligned three-dimensional point cloud data is divided into multiple cross-sectional point sets along the central axis direction; For each set of points on the cross-section, perform independent local geometric fitting to obtain the local fitting parameters for each cross-section; Based on the three-dimensional point cloud data and the local fitting parameters of each cross section, the three-dimensional original surface of the formed part is unfolded into a two-dimensional surface deviation topology map. The surface roughness of the formed part is calculated based on the two-dimensional surface deviation topology map, and the process-induced macro-geometric deviation of the formed part is statistically evaluated based on the local fitting parameters of each cross section. The step of performing independent local geometric fitting for each set of cross-sectional points to obtain local fitting parameters for each cross-section includes: For each set of cross-sectional points, a random sampling consensus algorithm is used to identify a set of interior points that conform to the main body contour, forming an interior point set; and the Levenburg-Mannelt algorithm is used to optimize the interior point set using nonlinear least squares method to find the optimal circle parameters and determine the local fitting parameters; wherein, for each interior point in the interior point set, the residual is defined as the difference between the actual radial distance from the point to the fitted circle and the fitted radius; The local fitting parameters of each cross section form a local fitting parameter sequence; based on the three-dimensional point cloud data and the local fitting parameters of each cross section, the three-dimensional original surface of the formed part is unfolded into a two-dimensional surface deviation topology map, including: For any point in the three-dimensional point cloud data, a dynamic local fitting reference for the axial position of the point is established by interpolating the discrete local fitting parameter sequence; the radial deviation value of any point in the three-dimensional point cloud data relative to the dynamic local fitting reference and the circumferential angular coordinate of the point in the unfolded coordinate system are calculated; based on natural neighborhood interpolation, the circumferential angular coordinates, axial position coordinates and radial deviation values ​​of all points in the three-dimensional point cloud data are mapped onto the created regular two-dimensional mesh to generate the two-dimensional surface deviation topology map; The calculation of the surface roughness of the formed part based on the two-dimensional surface deviation topology map includes: applying a two-dimensional Gaussian filter to the two-dimensional surface deviation topology map to separate and obtain the filtered roughness matrix; the standard deviation of the two-dimensional Gaussian filter is calculated from the user-defined cutoff wavelength and the pixel density of the grid, and their relationship is as follows: ； in, The standard deviation of the two-dimensional Gaussian filter; The cutoff wavelength; Pixel density; yes A function to calculate the square root of a non-negative number; The surface roughness is calculated based on the roughness matrix; the surface roughness includes surface roughness parameters and average axial profile roughness distributed along the circumference.

2. The method for predicting the quality of laser powder bed fusion-formed parts according to claim 1, characterized in that, The process of aligning the center axis of the three-dimensional point cloud data includes: The first principal component vector is determined by the principal component analysis algorithm, and the first principal component vector is determined as the actual central principal axis of the formed part; By calculating the rotation matrix, the actual central axis is aligned with the preset axis of the coordinate system to obtain the aligned three-dimensional point cloud data.

3. The method for predicting the quality of laser powder bed fusion-formed parts according to claim 2, characterized in that, The process of segmenting the aligned 3D point cloud data into multiple cross-sectional point sets along the central axis includes: Define a series of equally spaced virtual slice planes along the central axis; A thickness tolerance zone is defined around each of the virtual slice planes. For each virtual slice plane, three-dimensional points falling within the thickness tolerance zone are extracted, and coordinates perpendicular to the central axis are used to form the cross-sectional point set.

4. The method for predicting the quality of laser powder bed fusion-formed parts according to claim 3, characterized in that, The local fitting parameters for each cross section include the local radius fitting sequence and the local fitting center coordinate sequence for all cross sections; the statistical evaluation of the process-induced macroscopic geometric deviation of the formed part based on the local fitting parameters for each cross section includes: Statistical analysis is performed on the local fitted radius sequence to evaluate the dimensional deviation of the formed part; Statistical analysis is performed on the locally fitted center coordinate sequence to evaluate the waviness or bending deformation of the formed part caused by thermal stress; wherein, the statistical analysis of the locally fitted center coordinate sequence includes: The distance from the center of each circle to the actual central axis is calculated to form a distance sequence. Then, the overall bending deformation caused by the thermal stress is quantitatively characterized based on the maximum value or root mean square value of the distance sequence.

5. A method for predicting the quality of laser powder bed fused parts according to any one of claims 1-4, characterized in that, The laser powder bed melting forming part is a predicted forming part based on a multiphysics numerical model simulation of the laser powder bed melting process; the method further includes: A multiphysics numerical model of the laser powder bed melting process is established. Based on the forming process parameters of the part to be formed, the forming process is simulated in the multiphysics numerical model to obtain a predictive three-dimensional surface dataset of the formed part. The predictive 3D surface dataset is converted into 3D point cloud data.

6. The method for predicting the quality of laser powder bed fused parts according to claim 5, characterized in that, The laser powder bed fusion molded part is an actual molded part manufactured through physical means. For the actual molded part, scanning technology is used to obtain a real three-dimensional surface dataset of the surface morphology of the actual molded part; the method further includes: A third evaluation result is obtained by comparing the first evaluation result based on the predictive 3D surface dataset with the second evaluation result based on the actual 3D surface dataset. Based on the third evaluation results, the multiphysics numerical model is verified and calibrated, and / or the laser powder bed melting process parameters are optimized through feedback.

7. A laser powder bed fused casting quality prediction system, used to implement the laser powder bed fused casting quality prediction method according to any one of claims 1-6, characterized in that, include: The acquisition module is used to acquire the three-dimensional point cloud data of the laser powder bed fused parts; The processing module is used to perform central axis alignment processing on the three-dimensional point cloud data, and to divide the aligned three-dimensional point cloud data into multiple cross-sectional point sets along the central axis direction. The fitting module is used to perform independent local geometric fitting for each set of points on the cross-section to obtain the local fitting parameters for each cross-section. The unfolding module is used to unfold the three-dimensional original surface of the formed part into a two-dimensional surface deviation topology map based on the three-dimensional point cloud data and the local fitting parameters of each cross section. The evaluation module is used to calculate the surface roughness of the formed part based on the two-dimensional surface deviation topology map, and to perform statistical evaluation of the process-induced macro-geometric deviation of the formed part based on the local fitting parameters of each cross section.

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