CN118883469B - A method and system for quality inspection of 3D printing engineering materials - Google Patents
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Abstract
The application discloses a quality detection method and a quality detection system for a 3D printing engineering material. The quality detection method of the 3D printing engineering material comprises the steps of collecting data in a 3D printing process, preprocessing a 3D printing sample, comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording spectral feature data of different areas, building a spectral feature data matrix, analyzing the spectral feature data, calculating defect values of the different areas, updating the defect values of the different areas into the spectral feature data matrix, calculating a total error value in the printing process according to the data in the 3D printing process, judging that the quality of the 3D printing sample is qualified if the total error value does not exceed a set threshold, and retrieving an area with the highest defect value from the spectral feature data matrix if the total error value exceeds the set threshold, and providing feedback information. The application provides multi-factor comprehensive evaluation for quality detection of the 3D printing engineering material, and is beneficial to quality control and fixed point detection.
Description
Technical Field
The invention relates to a spectrum analysis technology, in particular to a method and a system for detecting the quality of a 3D printing engineering material.
Background
Despite the rapid development of 3D printing technology, challenges in terms of material properties, mechanical strength, precision, and cost are still faced, and the introduction of spectroscopic analysis techniques has shown unique value to these limitations. As a flexible and nondestructive detection means, the method is suitable for rapid analysis of rare materials, reduces sample loss, can greatly improve detection efficiency by virtue of high-speed multichannel characteristics of spectrum analysis, and has high-precision analysis capability, especially the advantages of near infrared micro-spectrum on microstructure and component analysis, and is important for quality control of 3D printing materials.
Disclosure of Invention
The invention provides a quality detection method of a 3D printing engineering material, which comprises the following steps:
s110, collecting data of a 3D printing process, and preprocessing a 3D printing sample;
s120, comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording spectral feature data of different areas, and establishing a spectral feature data matrix;
s130, analyzing the spectral feature data, calculating defect values of different areas, and updating the defect values of the different areas into a spectral feature data matrix;
s140, calculating a total error value of the printing process according to the data of the 3D printing process;
and S150, if the total error value does not exceed the set threshold value, judging that the quality of the 3D printing sample is qualified, and if the total error value exceeds the set threshold value, retrieving the area with the highest defect value from the spectrum characteristic data matrix and providing feedback information.
A method of quality inspection of 3D printed engineering material as described above, wherein the 3D printing process comprises three-dimensional modeling, slicing processing, loading material and printing, and the data of the 3D printing process comprises three-dimensional model data, slicing data, material property data and printing configuration data.
The method for detecting the quality of the 3D printing engineering material comprises the steps of determining the quality of the 3D printing engineering material, wherein the spectral characteristic data comprise an intensity characteristic value, a baseline value and a signal-to-noise ratio, the intensity characteristic value represents the detected light intensity at a specific wavelength, the intensity change reflects the absorption, reflection or transmission characteristic of a sample to the light with the specific wavelength, the baseline value is the lowest continuous background of a spectrogram, the horizontal line is ideal, and the signal-to-noise ratio describes the ratio of a spectral signal to background noise.
The method for detecting the quality of the 3D printing engineering material comprises the following substeps of:
filtering the optical characteristic data;
Extracting the characteristics of the spectrum characteristic data after the filtering is finished, and determining the position and the intensity of key characteristic peaks;
calculating defect values of different areas according to the characteristic peak positions and the intensities of the characteristic extraction;
updating the defect values of different areas into a spectrum characteristic data matrix;
A method of quality inspection of 3D printed engineering materials as described above, wherein the process of calculating the process error value comprises the sub-steps of:
Calculating a geometric error value according to the three-dimensional model data;
calculating a material shrinkage compensation error value according to the material attribute data;
calculating an interlayer adhesion error value according to the slice data;
Calculating a print path deviation error value according to the print configuration data;
Calculating a total error value according to the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value and the printing path error value;
The invention also provides a system for detecting the quality of the 3D printing engineering material, which comprises a data collection and preprocessing module, a spectrum characteristic data matrix module, a defect value calculation module, an error value calculation module and a feedback module.
The data collection and preprocessing module is used for collecting data of the 3D printing process and preprocessing the 3D printing sample;
The spectrum characteristic data matrix module is used for comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording spectrum characteristic data of different areas and establishing a spectrum characteristic data matrix;
The defect value calculation module is used for analyzing the spectral feature data, calculating defect values of different areas and updating the defect values of the different areas into a spectral feature data matrix;
the error value calculation module is used for calculating the total error value of the printing process according to the data of the 3D printing process;
The feedback module judges that the quality of the 3D printing sample is qualified if the total error value does not exceed the set threshold value, and searches the area with the highest defect value from the spectrum characteristic data matrix to provide feedback information if the total error value exceeds the set threshold value;
A system for quality inspection of 3D printed engineering materials as described above wherein the 3D printing process comprises three-dimensional modeling, slicing processing, loading materials and printing and the data of the 3D printing process comprises three-dimensional model data, slicing data, material property data and printing configuration data.
A system for quality detection of a 3D printed engineered material as described above, wherein the spectral signature data comprises an intensity signature value, a baseline value and a signal-to-noise ratio, wherein the intensity signature value represents a detected light intensity at a specific wavelength, the intensity variation reflects absorption, reflection or transmission characteristics of the sample for the specific wavelength light, the baseline value is the lowest continuous background of the spectrogram, ideally a horizontal line, and the signal-to-noise ratio describes a ratio of the spectral signal to background noise.
The system for detecting the quality of the 3D printing engineering material comprises the following substeps:
filtering the optical characteristic data;
Extracting the characteristics of the spectrum characteristic data after the filtering is finished, and determining the position and the intensity of key characteristic peaks;
calculating defect values of different areas according to the characteristic peak positions and the intensities of the characteristic extraction;
updating the defect values of different areas into a spectrum characteristic data matrix;
a system for quality inspection of 3D printed engineering materials as described above, wherein the process of calculating the process error value comprises the sub-steps of:
Calculating a geometric error value according to the three-dimensional model data;
calculating a material shrinkage compensation error value according to the material attribute data;
calculating an interlayer adhesion error value according to the slice data;
Calculating a print path deviation error value according to the print configuration data;
Calculating a total error value according to the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value and the printing path error value;
the method has the beneficial effects that multi-factor comprehensive evaluation is provided for quality detection of the 3D printing engineering material, and quality control and fixed point detection are facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of a method for quality inspection of a 3D printing engineering material according to an embodiment of the present application;
Fig. 2 is a schematic diagram of a system for quality detection of a 3D printing engineering material according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a first embodiment of the present application provides a quality detection method for a 3D printing engineering material.
S110, collecting data of a 3D printing process, and preprocessing a 3D printing sample;
The 3D printing process includes three-dimensional modeling, slicing processing, loading materials and printing, and the data of the 3D printing process includes three-dimensional model data, slicing data, material property data and printing configuration data. Wherein the three-dimensional model data is typically generated by CAD (computer aided design) software, the geometric information of the three-dimensional model is described by a triangular patch, the slice data contains instruction information of printing path, filling mode, speed, temperature, etc. of each layer, the material attribute data includes physical and chemical properties of the used material, such as melting temperature, viscosity, curing rate, strength, etc., and the printer configuration data relates to hardware settings of the printer, such as nozzle diameter, platform temperature, upper printing speed limit, etc.
After 3D printing is completed, the sample is preprocessed, the supporting structure is removed, and the flatness and cleanliness of the detection surface are ensured.
S120, comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording spectral feature data of different areas, and establishing a spectral feature data matrix;
the high-precision spectrometer is used for scanning the surface and the near surface of the 3D printing component, and the components, microstructures and potential defects (such as air holes, cracks and the like) of the material are identified through the absorption and reflection characteristics of light rays with different wavelengths.
The spectral signature data includes intensity signature values, baseline values, and signal to noise ratios. The characteristic value of the intensity represents the light intensity detected under the specific wavelength, the change of the intensity reflects the absorption, reflection or transmission characteristic of the sample on the light with the specific wavelength, the base line value is the lowest continuous background of the spectrogram, the ideal condition is a horizontal line, the signal to noise ratio describes the proportion of the spectral signal to the background noise, the high signal to noise ratio means that the data is more reliable, and the analysis result is more accurate.
The spectral data of different areas are recorded by using a spectral feature data matrix, specifically, the spectral feature data matrix is expressed as: Wherein A represents a spectral feature data matrix, a ij represents a spectral feature data subset of an ith row and jth column region, m represents a region maximum line number, n represents a region maximum column number, I ij represents an intensity feature value of the ith row and jth column region, B ij represents a baseline value of the ith row and jth column region, SNR ij represents a signal-to-noise ratio of the ith row and jth column region, DF ij represents defect values, and initial values are all 0.
S130, analyzing the spectral feature data, calculating defect values of different areas, and updating the defect values of the different areas into a spectral feature data matrix;
The spectral feature data analysis and defect value calculation specifically comprises the following sub-steps:
S131, filtering the optical characteristic data;
Removing random noise in the spectrum characteristic data by using a filtering technology, removing a non-uniform background baseline of a spectrum by using an iterative correction method, carrying out normalization processing on the spectrum data by using the corrected intensity characteristic value of I corrected so as to eliminate light intensity difference, and facilitating comparison, wherein the formula is specifically adopted: The normalized intensity characteristic value is calculated, wherein I ref represents the normalized intensity characteristic value, I corrected represents the corrected intensity characteristic value, and max (I corrected) represents the maximum value of the corrected intensity.
S132, carrying out feature extraction on the spectral feature data after the completion of the filtration, and determining the position and the intensity of key feature peaks;
And carrying out feature extraction on the spectral feature data after the filtering is completed, and determining the position and the intensity of a key feature peak, wherein the feature peak is represented as lambda peak.
S133, calculating defect values of different areas according to the characteristic peak positions and the intensity of the characteristic extraction;
The application obtains the comprehensive defect score by weighting calculation of the two defect index scores, wherein the comprehensive defect score is the defect value, and the quality condition of different areas of the 3D printing sample is evaluated by the defect value, and the method comprises the following concrete steps:
1. Calculating the deviation score of the intensity characteristic value;
Let the intensity of the reference region at the characteristic peak lambda peak be I ref(λpeak), the corresponding intensity of the region to be measured be I ij(λpeak), define the intensity characteristic value deviation score DF intensity to quantify the intensity difference, DF intensity close to 0 indicates that the intensity match is good, the larger the deviation indicates the higher the defect probability, specifically, the formula is adopted: The intensity characteristic value deviation score of each region is calculated, wherein (DF intensity)ij represents the intensity characteristic value deviation score of the ith row and jth column regions, I ref(λpeak) represents the intensity of the reference region at characteristic peak lambda peak, and I ij(λpeak) represents the intensity of the region to be measured at characteristic peak lambda peak.
2. Calculating a signal-to-noise ratio difference score;
Let SNR under the reference area be SNR ref, SNR under the region to be measured be SNR ij, define SNR difference score DF SNR,DFSNR and reflect the degree of degradation of SNR, the smaller the value is the closer the data quality is to the standard or reference state, specifically, adopt the formula: The SNR difference score for each region is calculated, where (DF SNR)ij represents the SNR difference score for the ith row and jth column regions, SNR ref represents the SNR under the reference region, and SNR ij represents the SNR for the region under test.
3. Calculating a comprehensive defect score according to the intensity characteristic value deviation score and the signal-to-noise ratio difference score;
The intensity deviation and the signal to noise ratio difference are comprehensively considered, the comprehensive defect score DF total is defined, and specifically, the formula is adopted: Calculating an integrated defect score, wherein (DF total)ij represents the integrated defect score of the ith row and jth column regions, (DF intensity)ij represents the intensity characteristic value deviation score of the ith row and jth column regions, (DF SNR)ij represents the signal-to-noise ratio deviation score of the ith row and jth column regions), And (5) representing the weight of the intensity characteristic value deviation score, and beta represents the weight of the signal-to-noise ratio difference score.
S134, updating the defect values of different areas into a spectrum characteristic data matrix;
The region comprehensive defect score DF total obtained by the calculation in the above steps is assigned to the defect value DF ij of the corresponding region in the spectral feature data matrix A, specifically, the formula is adopted:
DF ij＝DFtotal, i=1, 2,3,..m, j=1, 2,3,.. where DF ij represents a defect value of the ith row and jth column regions, DF total represents the integrated defect score for the ith row and jth column regions, m represents the maximum row number of the region, n represents the maximum column number of the region, and all region defect values of the spectrum characteristic data matrix are assigned.
S140, calculating a total error value of the printing process according to the data of the 3D printing process;
in the 3D printing process, as the deviation exists between the chemical properties and the working conditions of the printing materials and the 3D printer under different temperature environments, a certain error value exists in a finished product, the error value in the 3D printing process is calculated by establishing a multi-factor integrated error evaluation model, the data collected in the 3D printing process is utilized to comprise three-dimensional model data, slice data, material attribute data and printing configuration data, the three-dimensional model data, the slice data, the material attribute data and the printing configuration data are used as input data of the multi-factor integrated error evaluation model, and the process for calculating the error value specifically comprises the following substeps:
s141, calculating a geometric error value according to the three-dimensional model data;
in the 3D printing process, a certain error exists between the geometric value of the expected sample and the geometric value of the actual sample, specifically, the formula is adopted:
calculating a geometric error value, wherein E geom represents the geometric error value, Representing the expected sample geometry setpoint coordinates,The geometrical fixed point coordinates of the actual sample are represented, i represents a coordinate index, and the value is 1.
S142, calculating a material shrinkage compensation error value according to the material attribute data;
The printing material used in the 3D printing process has a certain error value due to shrinkage caused by actual conditions such as temperature, and specifically adopts the formula:
E shrink＝Vmodel×(ηshrink+εtemp×(ΔTcooling)), where E shrink represents a material shrinkage compensation error value, V model represents a sample volume, η shrink represents a base shrinkage, epsilon temp represents a thermal expansion coefficient, and Δt cooling represents a change in printing temperature cooling to room temperature.
S143, calculating an interlayer adhesion error value according to the slice data;
In the 3D printing process, due to uneven illumination intensity and temperature distribution, different curing degrees of all layers can be caused, adhesion between layers is affected, interlayer adhesion error values exist, and specifically, the formula is adopted: An interlayer adhesion error value was calculated, where E layer represents the interlayer adhesion error value, σ j represents the actual interlayer adhesion, σ ideal represents the expected interlayer adhesion, j represents the number of layers, and the value is 1..m.
S144, calculating a printing path deviation error value according to the printing configuration data;
In the 3D printing process, as the actual motion trail of the 3D printer is not completely matched with the designed or expected path, a printing path deviation error value exists, and specifically, a formula is adopted:
Calculating a print path deviation error value, wherein E path represents the print path deviation error value, Representing the coordinates of the points of the intended path,The actual route point coordinates are represented, and k represents the route point number and has a value of 1.
S145, calculating a total error value according to the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value and the printing path deviation error value;
Calculating a total error value from the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value, and the printing path error value, specifically using the formula:
E total＝γ1Epath+γ2Elayer+γ3Eshrink+γ4Egeom calculates the total error value, where E total represents the total error value, E path represents the print path error value, E layer represents the interlayer adhesion error value, E shrink represents the material shrinkage compensation error value, E geom represents the geometry error value, γ 1 represents the print path error value weight, γ 2 represents the interlayer adhesion error value, γ 3 represents the material shrinkage compensation error value weight, and γ 4 represents the geometry error value weight.
S150, if the total error value does not exceed the set threshold value, judging that the quality of the 3D printing sample is qualified, and if the total error value exceeds the set threshold value, retrieving the area with the highest defect value from the spectrum characteristic data matrix and providing feedback information;
If the total error value does not exceed the set threshold, judging that the quality of the 3D printing sample is qualified, if the total error value exceeds the set threshold, retrieving the region with the highest defect value from the spectrum characteristic data matrix, providing feedback information, wherein the feedback information indicates the region with the largest problem of the 3D sample, and is favorable for fixed-point modification of operators and improves the working efficiency. Specifically, using the formula E total＞Ω?true:max(A[aij(DFij)), where E total represents the total error value, Ω represents the set threshold, and max (A [ a ij(DFij) ] represents the highest subset a ij of defect values DF ij in the spectral feature data matrix A.
Example two
As shown in fig. 2, a second embodiment of the present application provides a quality detection system for a 3D printing engineering material, including:
The data collection and preprocessing module 21 is used for collecting data of the 3D printing process and preprocessing a 3D printing sample;
The 3D printing process includes three-dimensional modeling, slicing processing, loading materials, printing, and the like, and the data of the 3D printing process includes three-dimensional model data, slicing data, material property data, printing configuration data, and the like. Wherein the three-dimensional model data is typically generated by CAD (computer aided design) software, the geometric information of the three-dimensional model is described by a triangular patch, the slice data contains instruction information of printing path, filling mode, speed, temperature, etc. of each layer, the material attribute data includes physical and chemical properties of the used material, such as melting temperature, viscosity, curing rate, strength, etc., and the printer configuration data relates to hardware settings of the printer, such as nozzle diameter, platform temperature, upper printing speed limit, etc.
After 3D printing is completed, the sample is preprocessed, the supporting structure is removed, and the flatness and cleanliness of the detection surface are ensured.
The spectral feature data matrix module 22 is used for comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording the spectral feature data of different areas and establishing a spectral feature data matrix;
the high-precision spectrometer is used for scanning the surface and the near surface of the 3D printing component, and the components, microstructures and potential defects (such as air holes, cracks and the like) of the material are identified through the absorption and reflection characteristics of light rays with different wavelengths.
The spectral signature data includes intensity signature values, baseline values, and signal to noise ratios. The characteristic value of the intensity represents the light intensity detected under the specific wavelength, the change of the intensity reflects the absorption, reflection or transmission characteristic of the sample on the light with the specific wavelength, the base line value is the lowest continuous background of the spectrogram, the ideal condition is a horizontal line, the signal to noise ratio describes the proportion of the spectral signal to the background noise, the high signal to noise ratio means that the data is more reliable, and the analysis result is more accurate.
The spectral data of different areas are recorded by using a spectral feature data matrix, specifically, the spectral feature data matrix is expressed as: Wherein A represents a spectral feature data matrix, a ij represents a spectral feature data subset of an ith row and jth column region, m represents a region maximum line number, n represents a region maximum column number, I ij represents an intensity feature value of the ith row and jth column region, B ij represents a baseline value of the ith row and jth column region, SNR ij represents a signal-to-noise ratio of the ith row and jth column region, DF ij represents defect values, and initial values are all 0.
The defect value calculation module 23 is used for analyzing the spectral feature data, calculating defect values of different areas and updating the defect values of the different areas into a spectral feature data matrix;
The spectral feature data analysis and defect value calculation specifically comprises the following sub-steps:
The data filtering module is used for filtering the optical characteristic data;
Removing random noise in the spectrum characteristic data by using a filtering technology, removing a non-uniform background baseline of a spectrum by using an iterative correction method, carrying out normalization processing on the spectrum data by using the corrected intensity characteristic value of I corrected so as to eliminate light intensity difference, and facilitating comparison, wherein the formula is specifically adopted: The normalized intensity characteristic value is calculated, wherein I ref represents the normalized intensity characteristic value, I corrected represents the corrected intensity characteristic value, and max (I corrected) represents the maximum value of the corrected intensity.
The characteristic extraction module is used for carrying out characteristic extraction on the spectral characteristic data after the filtering is finished, and determining the position and the intensity of key characteristic peaks;
And carrying out feature extraction on the spectral feature data after the filtering is completed, and determining the position and the intensity of a key feature peak, wherein the feature peak is represented as lambda peak.
The calculating module is used for calculating defect values of different areas according to the characteristic peak positions and the intensity of the characteristic extraction;
The application obtains the comprehensive defect score by weighting calculation of the two defect index scores, wherein the comprehensive defect score is the defect value, and the quality condition of different areas of the 3D printing sample is evaluated by the defect value, and the method comprises the following concrete steps:
1. Calculating the deviation score of the intensity characteristic value;
Let the intensity of the reference region at the characteristic peak lambda peak be I ref(λpeak), the corresponding intensity of the region to be measured be I ij(λpeak), define the intensity characteristic value deviation score DF intensity to quantify the intensity difference, DF intensity close to 0 indicates that the intensity match is good, the larger the deviation indicates the higher the defect probability, specifically, the formula is adopted: The intensity characteristic value deviation score of each region is calculated, wherein (DF intensity)ij represents the intensity characteristic value deviation score of the ith row and jth column regions, I ref(λpeak) represents the intensity of the reference region at characteristic peak lambda peak, and I ij(λpeak) represents the intensity of the region to be measured at characteristic peak lambda peak.
2. Calculating a signal-to-noise ratio difference score;
Let SNR under the reference area be SNR ref, SNR under the region to be measured be SNR ij, define SNR difference score DF SNR,DFSNR and reflect the degree of degradation of SNR, the smaller the value is the closer the data quality is to the standard or reference state, specifically, adopt the formula: The SNR difference score for each region is calculated, where (DF SNR)ij represents the SNR difference score for the ith row and jth column regions, SNR ref represents the SNR under the reference region, and SNR ij represents the SNR for the region under test.
3. Calculating a comprehensive defect score according to the intensity characteristic value deviation score and the signal-to-noise ratio difference score;
The intensity deviation and the signal to noise ratio difference are comprehensively considered, the comprehensive defect score DF total is defined, and specifically, the formula is adopted: Calculating an integrated defect score, wherein (DF total)ij represents the integrated defect score of the ith row and jth column regions, (DF intensity)ij represents the intensity characteristic value deviation score of the ith row and jth column regions, (DF SNR)ij represents the signal-to-noise ratio deviation score of the ith row and jth column regions), And (5) representing the weight of the intensity characteristic value deviation score, and beta represents the weight of the signal-to-noise ratio difference score.
The matrix updating module is used for updating the defect values of different areas into the spectrum characteristic data matrix;
The region comprehensive defect score DF total obtained by the calculation in the above steps is assigned to the defect value DF ij of the corresponding region in the spectral feature data matrix A, specifically, the formula is adopted:
DF ij＝DFtotal, i=1, 2,3,..m, j=1, 2,3,.. where DF ij represents a defect value of the ith row and jth column regions, DF total represents the integrated defect score for the ith row and jth column regions, m represents the maximum row number of the region, n represents the maximum column number of the region, and all region defect values of the spectrum characteristic data matrix are assigned.
An error value calculation module 24 for calculating a total error value of the printing process according to the data of the 3D printing process;
in the 3D printing process, as the deviation exists between the chemical properties and the working conditions of the printing materials and the 3D printer under different temperature environments, a certain error value exists in a finished product, the error value in the 3D printing process is calculated by establishing a multi-factor integrated error evaluation model, the data collected in the 3D printing process is utilized to comprise three-dimensional model data, slice data, material attribute data and printing configuration data, the three-dimensional model data, the slice data, the material attribute data and the printing configuration data are used as input data of the multi-factor integrated error evaluation model, and the process for calculating the error value specifically comprises the following substeps:
1. calculating a geometric error value according to the three-dimensional model data;
in the 3D printing process, a certain error exists between the geometric value of the expected sample and the geometric value of the actual sample, specifically, the formula is adopted:
calculating a geometric error value, wherein E geom represents the geometric error value, Representing the expected sample geometry setpoint coordinates,The geometrical fixed point coordinates of the actual sample are represented, i represents a coordinate index, and the value is 1.
2. Calculating a material shrinkage compensation error value according to the material attribute data;
The printing material used in the 3D printing process has a certain error value due to shrinkage caused by actual conditions such as temperature, and specifically adopts the formula:
E shrink＝Vmodel×(ηshrink+εtemp×(ΔTcooling)), where E shrink represents a material shrinkage compensation error value, V model represents a sample volume, η shrink represents a base shrinkage rate, epsilon temp represents a coefficient of expansion, and Δt cooling represents a change in printing temperature cooling to room temperature.
3. Calculating an interlayer adhesion error value according to the slice data;
In the 3D printing process, due to uneven illumination intensity and temperature distribution, different curing degrees of all layers can be caused, adhesion between layers is affected, interlayer adhesion error values exist, and specifically, the formula is adopted: An interlayer adhesion error value was calculated, where E layer represents the interlayer adhesion error value, σ j represents the actual interlayer adhesion, σ ideal represents the expected interlayer adhesion, j represents the number of layers, and the value is 1..m.
4. Calculating a print path deviation error value according to the print configuration data;
In the 3D printing process, as the actual motion trail of the 3D printer is not completely matched with the designed or expected path, a printing path deviation error value exists, and specifically, a formula is adopted:
Calculating a print path deviation error value, wherein E path represents the print path deviation error value, Representing the coordinates of the points of the intended path,The actual route point coordinates are represented, and k represents the route point number and has a value of 1.
5. Calculating a total error value according to the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value and the printing path error value;
Calculating a total error value from the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value, and the printing path error value, specifically using the formula:
E total＝γ1Epath+γ2Elayer+γ3Eshrink+γ4Egeom calculates the total error value, where E total represents the total error value, E path represents the print path error value, E layer represents the interlayer adhesion error value, E shrink represents the material shrinkage compensation error value, E geom represents the geometry error value, γ 1 represents the print path error value weight, γ 2 represents the interlayer adhesion error value, γ 3 represents the material shrinkage compensation error value weight, and γ 4 represents the geometry error value weight.
The feedback module 25 judges that the 3D printing sample quality is qualified if the total error value does not exceed the set threshold value, and searches the area with the highest defect value from the spectrum characteristic data matrix to provide feedback information if the total error value exceeds the set threshold value;
If the total error value does not exceed the set threshold, judging that the quality of the 3D printing sample is qualified, if the total error value exceeds the set threshold, retrieving the region with the highest defect value from the spectrum characteristic data matrix, providing feedback information, wherein the feedback information indicates the region with the largest problem of the 3D sample, and is favorable for fixed-point modification of operators and improves the working efficiency. Specifically, using the formula E total＞Ω?true:max(A[aij(DFij)), where E total represents the total error value, Ω represents the set threshold, and max (A [ a ij(DFij) ] represents the highest subset a ij of defect values DF ij in the spectral feature data matrix A.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention in further detail, and are not to be construed as limiting the scope of the invention, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the invention.
Claims (6)
1. The quality detection method of the 3D printing engineering material is characterized by comprising the following steps of:
s110, collecting data of a 3D printing process, and preprocessing a 3D printing sample;
s120, comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording spectral feature data of different areas, and establishing a spectral feature data matrix;
s130, analyzing the spectral feature data, calculating defect values of different areas, and updating the defect values of the different areas into a spectral feature data matrix, wherein the method specifically comprises the following sub-steps:
S131, filtering the optical characteristic data;
Removing random noise in the spectrum characteristic data by applying a filtering technology, removing a non-uniform background baseline of the spectrum by adopting an iterative correction method, wherein the corrected intensity characteristic value is as follows Normalization processing is carried out on the optical data to eliminate the light intensity difference, so that comparison is convenient, and specifically, the formula is adopted: calculating normalized intensity characteristic values, wherein The intensity characteristic value after normalization processing is represented,Representing the corrected intensity characteristic value(s),Representing the maximum value of the corrected intensity;
S132, carrying out feature extraction on the spectral feature data after the completion of the filtration, and determining the position and the intensity of key feature peaks;
extracting the characteristics of the spectrum characteristic data after the filtering is finished, determining the position and the intensity of key characteristic peaks, wherein the characteristic peaks are expressed as ;
S133, calculating defect values of different areas according to the characteristic peak positions and the intensity of the characteristic extraction;
The defect value is divided into two defect indexes, wherein the first defect index is an intensity characteristic value deviation score, the second defect index is a signal-to-noise ratio difference score, the two defect index scores are weighted to obtain a comprehensive defect score, the comprehensive defect score is the defect value, and the quality conditions of different areas of the 3D printing sample are evaluated through the defect value, and the method specifically comprises the following substeps:
1. Calculating the deviation score of the intensity characteristic value;
set the reference region at the characteristic peak The strength at the point isThe corresponding intensity of the region to be measured isDefining intensity characteristic value deviation fractionTo quantify the intensity difference,A near 0 indicates a good intensity match, a larger deviation indicates a higher probability of defects, and specifically, the formula is used: Calculating intensity characteristic value deviation scores of the areas, wherein Represent the firstLine 1The intensity characteristic value deviation score of the column region,Representing the reference region at the characteristic peakThe strength of the part is that of the part,Indicating the region to be measured at the characteristic peakStrength at;
2. calculating a signal-to-noise ratio difference score;
let the signal-to-noise ratio under the reference area be The signal to noise ratio of the region to be measured isDefining a signal-to-noise ratio difference score,Reflecting the degree of degradation in signal-to-noise ratio, a smaller value indicates that the data quality is closer to a standard or reference state, specifically using the formula: Calculating a signal-to-noise ratio difference score for each region, wherein Represent the firstLine 1The signal-to-noise ratio difference score for the column region,Representing the signal-to-noise ratio under the reference region,Representing the signal to noise ratio of the region to be measured;
3. calculating a comprehensive defect score according to the intensity characteristic value deviation score and the signal-to-noise ratio difference score;
comprehensively considering the intensity deviation and the signal-to-noise ratio difference, and defining the comprehensive defect fraction Specifically, the formula is employed: Calculating a composite defect score, wherein Represent the firstLine 1The integrated defect score for the column region,Represent the firstLine 1The intensity characteristic value deviation score of the column region,Represent the firstLine 1The signal-to-noise ratio difference score for the column region,A weight representing the intensity characteristic value deviation score,A weight representing a signal-to-noise ratio difference score;
s134, updating the defect values of different areas into a spectrum characteristic data matrix;
the region comprehensive defect fraction obtained by calculation in the steps is calculated Defect values assigned to corresponding regions in the spectral feature data matrix ASpecifically, the formula is employed: Performing assignment, wherein Represent the firstLine 1The defect value of the column region,Represent the firstLine 1The integrated defect score for the column region,The maximum number of rows of the region is indicated,Representing the maximum column number of the region until all region defect value assignment of the spectrum characteristic data matrix is completed;
S140, calculating a total error value of the printing process according to the data of the 3D printing process, wherein the method comprises the following steps:
s141, calculating a geometric error value according to the three-dimensional model data;
The formula is adopted: calculating a geometric error value, wherein The value of the geometric error is represented,Representing the expected sample geometry setpoint coordinates,Representing the geometric fixed point coordinates of the actual sample,Representing a coordinate index, wherein the value is 1..n;
S142, calculating a material shrinkage compensation error value according to the material attribute data;
The formula is adopted: Calculating a material shrinkage compensation error value, wherein Indicating the value of the material shrinkage compensation error,The volume of the sample is indicated and,The base shrinkage is indicated as being indicative of the base shrinkage,Which means that the coefficient of thermal expansion is,A change value indicating that the printing temperature is cooled to room temperature;
s143, calculating an interlayer adhesion error value according to the slice data;
The formula is adopted: calculating an interlayer adhesion error value, wherein Indicating the value of the interlayer adhesion error,Indicating the degree of adhesion between the layers in reality,Indicating the expected interlayer adhesion, j indicating the number of layers, and the value is 1..m;
S144, calculating a printing path deviation error value according to the printing configuration data;
The formula is adopted: Calculating a print path deviation error value, wherein Representing a print path deviation error value,Representing the coordinates of the points of the intended path,Representing actual path point coordinates, wherein k represents path point number, and the value is 1..l;
s145, calculating a total error value according to the geometric error value, the material shrinkage compensation error value, the interlayer adhesion error value and the printing path deviation error value, wherein a calculation formula of the total error value is expressed as follows: Wherein The value of the total error is indicated,Representing a print path deviation error value,Indicating the value of the interlayer adhesion error,Indicating the value of the material shrinkage compensation error,The value of the geometric error is represented,Representing the print path deviation error value weight,Indicating an interlayer adhesion error value,Representing the weight of the material shrinkage compensation error value,Representing a geometric error value weight;
and S150, if the total error value does not exceed the set threshold value, judging that the quality of the 3D printing sample is qualified, and if the total error value exceeds the set threshold value, retrieving the area with the highest defect value from the spectrum characteristic data matrix and providing feedback information.
2. The method for quality inspection of 3D printed engineering materials according to claim 1, wherein the 3D printing process comprises three-dimensional modeling, slicing processing, loading materials and printing, and the data of the 3D printing process comprises three-dimensional model data, slicing data, material property data and printing configuration data.
3. A quality inspection method of 3D printed engineering materials according to claim 1, characterized in that the spectral characteristic data comprises an intensity characteristic value, a baseline value and a signal-to-noise ratio, wherein the intensity characteristic value represents the intensity of light detected at a specific wavelength, the intensity change reflects the absorption, reflection or transmission characteristics of the sample for the light of the specific wavelength, the baseline value is the lowest continuous background of the spectrogram, ideally should be a horizontal line, and the signal-to-noise ratio describes the ratio of the spectral signal to the background noise.
4. A system for quality inspection of 3D printed engineering material for performing a quality inspection method of 3D printed engineering material according to any one of claims 1-3, comprising:
The data collection and preprocessing module is used for collecting data of the 3D printing process and preprocessing the 3D printing sample;
The spectrum characteristic data matrix module is used for comprehensively scanning the preprocessed sample by using a high-resolution spectrometer, recording spectrum characteristic data of different areas and establishing a spectrum characteristic data matrix;
The defect value calculation module is used for analyzing the spectral feature data, calculating defect values of different areas and updating the defect values of the different areas into a spectral feature data matrix;
And the error value calculation module is used for calculating the total error value of the printing process according to the data of the 3D printing process, judging that the quality of the 3D printing sample is qualified if the total error value does not exceed a set threshold value, and searching the area with the highest defect value from the spectrum characteristic data matrix if the total error value exceeds the set threshold value to provide feedback information.
5. The system for quality inspection of 3D printed engineering materials of claim 4, wherein the 3D printing process comprises three-dimensional modeling, slicing processing, loading materials and printing, and the data of the 3D printing process comprises three-dimensional model data, slicing data, material property data and printing configuration data.
6. The system for quality inspection of 3D printed engineering materials of claim 4, wherein the spectral signature data comprises an intensity signature value, a baseline value and a signal-to-noise ratio, wherein the intensity signature value is indicative of a detected light intensity at a specific wavelength, wherein the intensity variation reflects absorption, reflection or transmission characteristics of the sample for the specific wavelength, wherein the baseline value is a lowest continuous background of the spectrogram, and ideally should be a horizontal line, and wherein the signal-to-noise ratio is descriptive of a ratio of the spectral signal to background noise.
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