In-situ quality evaluation system and method for melt processing component of laser powder bed

Abstract

The invention discloses an in-situ quality evaluation system and method for a melt processing component of a laser powder bed. The in-situ quality evaluation method for the laser powder bed melt processing component comprises the following steps: acquiring molten pool radiation information corresponding to a target processing layer, and carrying out filtering and noise reduction treatment; extracting the time / frequency domain characteristics of the molten pool radiation information corresponding to the target processing layer in a molten-channel-by-molten-channel manner; based on the extracted time / frequency domain features, analyzing and solving whether the melt channel is abnormal or not by using different unsupervised learning anomaly detection algorithms; and combining solving results of different unsupervised learning anomaly detection algorithms, calculating a performance expression index, and quantitatively evaluating the processing quality by using the performance expression index. According to the method, the time / frequency domain characteristics of the molten pool radiation information are extracted, different unsupervised learning anomaly detection algorithms are used for analyzing and solving whether the melting channel is abnormal or not so as to quantitatively evaluate the machining quality, and the problem that laser powder bed melting quality analysis greatly depends on the quality of a data set and priori knowledge is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to an in-situ quality evaluation system and method for a laser powder bed melting processing component. Background Art

[0002] Laser powder bed fusion (LPBF) is an additive manufacturing technology that uses a laser beam to melt and solidify metal powder layer by layer at an extremely high speed to form the required parts. It uses a wide range of materials, integrates design and manufacturing, has obvious advantages in the refined manufacturing of complex structures, and has been widely used in the manufacturing process of complex components of various equipment.

[0003] During the LPBF forming process, the laser interacts with the metal powder, the temperature and phase of the molten pool will change dramatically, the factors affecting the forming process are very complex, and random defects are easily generated, which in turn affects the microstructure and comprehensive performance of the parts. In recent years, powder bed melting process monitoring and quality analysis methods have received increasing attention. Using LPBF process monitoring technology to perceive the forming process information and combining intelligent prediction methods to predict forming defects can quickly make preliminary quality judgments during the forming process, make timely process adjustments, and ensure forming quality.

[0004] However, the existing laser powder bed melting quality analysis methods all use supervised learning methods similar to convolutional neural networks when predicting the internal defects and forming performance of parts based on the molten pool radiation data. First, the process data is collected, and then the process data is manually labeled to establish a data set, and then the model is obtained through training. The effectiveness of the model prediction mainly depends on the quality of the data set. The decline in the quality of the data set will greatly affect the effectiveness of the prediction. In addition, the amount of molten pool radiation light signal data of a complete project is very large, and the factors affecting the molten pool radiation light signal are complex. The abnormal data in the signal requires prior knowledge to judge, and it relies heavily on prior knowledge.

[0005] In summary, existing laser powder bed melting quality analysis methods have the problem of high dependence on the quality of the data set and high dependence on prior knowledge. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an in-situ quality evaluation system and method for laser powder bed melting processing components.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A system for evaluating the in-situ quality of a laser powder bed fusion-processed component comprises: a molten pool radiation acquisition device for acquiring molten pool radiation information of each processing layer during a printing process; a computer for acquiring the molten pool radiation information of each processing layer acquired by the molten pool radiation acquisition device, acquiring the molten pool radiation information corresponding to a target processing layer from the molten pool radiation information of each processing layer and performing filtering and noise reduction processing, extracting the time / frequency domain characteristics of the molten pool radiation information corresponding to the target processing layer melt by melt, using different unsupervised learning anomaly detection algorithms to analyze and solve whether anomalies occur in the melt, merging the solution results of different unsupervised learning anomaly detection algorithms, calculating a performance index and using the performance index to quantitatively evaluate the processing quality.

[0009] A method for in-situ quality evaluation of a laser powder bed fusion-processed component comprises the following steps: obtaining molten pool radiation information corresponding to a target processing layer and performing filtering and noise reduction processing; extracting time / frequency domain features of the molten pool radiation information corresponding to the target processing layer melt channel by melt channel; based on the extracted time / frequency domain features, using different unsupervised learning anomaly detection algorithms to analyze and solve whether anomalies occur in the melt channel; merging the solution results of different unsupervised learning anomaly detection algorithms, calculating a performance index, and using the performance index to quantitatively evaluate the processing quality.

[0010] The beneficial technical effect of the present invention is that the above-mentioned in-situ quality evaluation system and method for laser powder bed fusion processing components extracts the time / frequency domain characteristics of the molten pool radiation information, and based on the extracted time / frequency domain characteristics, uses different unsupervised learning anomaly detection algorithms to analyze and solve whether the melt path is abnormal to quantitatively evaluate the processing quality, thereby solving the problem that the laser powder bed fusion quality analysis is highly dependent on the quality of the data set and on prior knowledge. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic structural diagram of the in-situ quality evaluation system for laser powder bed melting processing components of the present invention;

[0012] Figure 2 It is a schematic flow chart of the in-situ quality evaluation method of a laser powder bed melting processed component of the present invention.

[0013] Description of reference numerals:

[0014] 1-forming chamber, 2-powder, 3-powder spreading mechanism, 4-feeding mechanism, 5-forming platform, 6-forming part, 7-laser, 8-laser beam, 9-dichroic mirror, 10-laser deflection and focusing system, 11-coaxial optical path molten pool radiation, 12-spatial molten pool radiation, 13-spatial molten pool radiation acquisition unit, 14-signal acquisition card, 15-computer, 100-coaxial optical path molten pool radiation acquisition unit, 201-first semi-transparent and semi-reflective mirror, 202-first focusing filter Mirror group, 203-visible light radiation sensor, 301-second semi-transparent and semi-reflective mirror, 302-second focusing filter group, 303-near-infrared radiation sensor, 401-third semi-transparent and semi-reflective mirror, 402-third focusing filter group, 403-colorimetric temperature sensor, 501-fourth semi-transparent and semi-reflective mirror, 502-fourth focusing filter group, 503-laser reflection light sensor, 601-reflector, 6021-fifth focusing filter group, 603-infrared radiation sensor. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to more clearly understand the objectives, technical solutions and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0016] The invention provides an in-situ quality evaluation system for a laser powder bed melting processing component.

[0017] like Figure 1 As shown, in one embodiment of the present invention, the laser powder bed fusion processing component in-situ quality evaluation system includes a molten pool radiation acquisition device and a computer 15, the molten pool radiation acquisition device is communicatively connected to the computer 15 via a signal acquisition card 14; the molten pool radiation acquisition device is used to collect the molten pool radiation information of each processing layer during the part processing process; the computer 15 is used to obtain the molten pool radiation information of each processing layer collected by the molten pool radiation acquisition device, obtain the molten pool radiation information corresponding to the target processing layer from the molten pool radiation information of each processing layer and perform filtering and noise reduction processing, extract the time / frequency domain characteristics of the molten pool radiation information corresponding to the target processing layer one by one, use different unsupervised learning anomaly detection algorithms to analyze and solve whether anomalies occur in the melt path, merge the solution results of different unsupervised learning anomaly detection algorithms, calculate the performance index and use the performance index to quantitatively evaluate the processing quality.

[0018] In this embodiment, the molten pool radiation collection device includes a spatial molten pool radiation collection unit 13 and a coaxial optical path molten pool radiation collection unit 100; the spatial molten pool radiation collection unit 13 is arranged at the upper top of the forming chamber 1, and is used to collect spatial molten pool radiation information of one or more bands of each processing layer; the coaxial optical path molten pool radiation collection unit 100 is arranged in the laser processing optical path, and is used to collect coaxial optical path molten pool radiation information of one or more bands of each processing layer. In other embodiments, the molten pool radiation collection device may also only include the spatial molten pool radiation collection unit 13, or only include the coaxial optical path molten pool radiation collection unit 100.

[0019] In this embodiment, the coaxial optical path molten pool radiation collection unit 100 includes a visible light radiation monitoring subunit, a near infrared radiation monitoring subunit, a colorimetric temperature measurement subunit, a laser reflection monitoring subunit, and an infrared radiation monitoring subunit. In other embodiments, the coaxial optical path molten pool radiation collection unit 100 may also include any one or any combination of a visible light radiation monitoring subunit, a near infrared radiation monitoring subunit, a colorimetric temperature measurement subunit, a laser reflection monitoring subunit, and an infrared radiation monitoring subunit.

[0020] The visible light radiation monitoring subunit includes a first semi-transparent and semi-reflective mirror 201, a first focusing filter group 202 and a visible light radiation sensor 203, wherein the first semi-transparent and semi-reflective mirror 201 can reflect radiation in the 400nm-750nm band and can transmit radiation in other bands, the first focusing filter group 202 is used to focus and combine the radiation in the 400nm-750nm band and transmit it to the visible light radiation sensor 203, and the visible light radiation sensor 203 is used to collect radiation in the 400nm-750nm band for in-situ monitoring of the status information of metal plume and plasma cloud generated in the laser powder bed melting process.

[0021] The near-infrared radiation monitoring subunit includes a second semi-transparent and semi-reflective mirror 301, a second focusing filter group 302 and a near-infrared radiation sensor 303, wherein the second semi-transparent and semi-reflective mirror 301 can reflect radiation in the 750nm-900nm band and can transmit radiation in other bands, the second focusing filter group 302 is used to focus and combine the radiation in the 750nm-900nm band and transmit it to the near-infrared radiation sensor 303, and the near-infrared radiation sensor 303 is used to collect radiation in the 750nm-900nm band for in-situ monitoring of the status information of the plasma cloud and the molten pool thermal radiation generated in the laser powder bed melting process, and can also be used to reflect the melting depth.

[0022] The colorimetric temperature measurement subunit includes a third semi-transparent and semi-reflective mirror 401, a third focusing filter group 402 and a colorimetric temperature measurement sensor 403, wherein the third semi-transparent and semi-reflective mirror 401 can reflect radiation in the 900nm-950nm band and can transmit radiation in other bands, the third focusing filter group 402 is used to focus and combine the radiation in the 900nm-950nm band and transmit it to the colorimetric temperature measurement sensor, and the colorimetric temperature measurement sensor 403 is used to collect radiation in the 900nm-950nm band for in-situ measurement of temperature changes in the molten pool during laser powder bed melting processing.

[0023] The laser reflection monitoring subunit includes a fourth semi-transparent and semi-reflective mirror 501, a fourth focusing filter group 502 and a laser reflection light sensor 503. The fourth semi-transparent and semi-reflective mirror 501 can reflect radiation in the 1030nm-1080nm band and can transmit radiation in other bands. The fourth focusing filter group 502 is used to focus and combine the radiation in the 1030nm-1080nm band and transmit it to the laser reflection light sensor 503. The laser reflection light sensor 503 is used to collect radiation in the 1030nm-1080nm band to monitor the reflected laser formed by the laser that is not absorbed when the laser interacts with the powder, thereby reflecting the laser defocus state during the laser powder bed melting process and the roughness and thickness changes of the processed surface.

[0024] The infrared radiation monitoring subunit includes a reflector 601, a fifth focusing filter group 602 and an infrared radiation sensor 603, wherein the reflector 601 can reflect radiation in a band greater than or equal to 1200nm, the fifth focusing filter group 602 is used to focus and combine the radiation in a band greater than or equal to 1200nm and transmit it to the infrared radiation sensor 603, and the infrared radiation sensor 603 is used to collect radiation in a band greater than or equal to 1200nm to monitor the surface and surrounding thermal information of the molten pool, plume, spatter, etc. during laser powder bed melting processing.

[0025] The spatial molten pool radiation acquisition unit 13 includes any one or any combination of a visible light radiation sensor, a near infrared radiation sensor, a colorimetric temperature sensor, a laser reflection light sensor, and an infrared radiation sensor. In specific implementation, if it is necessary to monitor the spatial molten pool radiation of a certain band, a corresponding sensor can be selected from the visible light radiation sensor, the near infrared radiation sensor, the colorimetric temperature sensor, the laser reflection light sensor, and the infrared radiation sensor to be installed on the upper top of the forming chamber 1; if it is necessary to monitor the spatial molten pool radiation of several bands, several corresponding sensors can be selected from the visible light radiation sensor, the near infrared radiation sensor, the colorimetric temperature sensor, the laser reflection light sensor, and the infrared radiation sensor to be distributed and installed on the upper top of the forming chamber 1.

[0026] based on Figure 1 The laser powder bed fusion processing component in-situ quality evaluation system shown in the figure provides a laser powder bed fusion processing component in-situ quality evaluation method, and the laser powder bed fusion processing component in-situ quality evaluation method is applied to a computer.

[0027] like Figure 2 As shown, in one embodiment of the present invention, the in-situ quality evaluation method of a laser powder bed fusion processed component includes steps S10 to S40:

[0028] S10, obtaining the molten pool radiation information corresponding to the target processing layer and performing filtering and noise reduction processing.

[0029] In specific implementation, a laser powder bed melting device equipped with a molten pool radiation collection device is used to print parts, and the molten pool radiation information is collected during the printing process. During the printing process, the forming platform 5 in the forming chamber 1 drops a layer thickness, the feeding mechanism 4 provides a layer of powder 2, and then the powder spreading mechanism 3 provides a layer of powder to the forming platform through movement; then the laser 7 emits a laser beam 8 through a dichroic mirror 9 and a laser deflection and focusing system 10 to reach the focal plane of the forming platform 5, that is, the surface of the formed part 6, and the laser and the powder interact to form a molten pool, at which time molten pool radiation is generated.

[0030] The molten pool radiation collection device can collect molten pool radiation information in two ways: one is that the spatial molten pool radiation 12 generated by the molten pool is freely transmitted to the spatial molten pool radiation collection unit 13, and is collected by the sensor inside the spatial molten pool radiation collection unit 13; the other is that the coaxial optical path molten pool radiation 11 generated by the molten pool is continuously reflected and transmitted to the various subunits of the coaxial optical path molten pool radiation collection unit 100 through the laser deflection and focusing system 10 and the dichroic mirror 9, and is collected by the various subunits of the coaxial optical path molten pool radiation collection unit 100.

[0031] It should be noted that, during the printing process, the wavelength of the laser beam 8 emitted by the laser 7 is 1064 nm, and the dichroic mirror 9 can transmit the wavelength of the laser beam of 1064 nm, and the remaining wavelengths are fully reflected. When collecting the radiation information of the molten pool, only the spatial molten pool radiation collection unit 13 can be used to collect the spatial molten pool radiation information of one or more wavelengths of each processing layer, or only the coaxial optical path molten pool radiation collection unit 100 can be used to collect the coaxial optical path molten pool radiation information of one or more wavelengths of each processing layer, or the spatial molten pool radiation collection unit 13 and the coaxial optical path molten pool radiation collection unit 100 can be used simultaneously to collect the spatial molten pool radiation information of one or more wavelengths and the coaxial optical path molten pool radiation information of one or more wavelengths.

[0032] The molten pool radiation information collected by the molten pool radiation collection device is transmitted to the signal collection card 14. The computer 15 is connected to the signal collection card 14 for communication, and can obtain the molten pool radiation information of each processing layer collected by the molten pool radiation collection device for subsequent in-situ calculation and analysis.

[0033] Laser powder bed fusion printing usually processes thousands of layers of a part. For rapid evaluation, the target processing layer can be selected, and the molten pool radiation information corresponding to the target processing layer can be used as the original data for in-situ calculation and analysis. The following strategies can be adopted to select the target processing layer: randomly select any n processing layers as the target processing layer; or use the processing risk layer determined by the evaluation as the target processing layer, where the processing risk layer is obtained through technical evaluation (including personnel experience evaluation or computational simulation evaluation). Of course, in some embodiments that do not have high requirements for the timeliness of the evaluation, all processing layers of the part processing can also be used as the target processing layer.

[0034] After the target processing layer is selected, the molten pool radiation information corresponding to the target processing layer is obtained from the collected molten pool radiation information of each processing layer and filtered and denoised. The filtering and denoising processing methods used in the filtering and denoising processing include but are not limited to time domain filtering and denoising processing methods, frequency domain filtering and denoising processing methods, time-frequency domain comprehensive filtering and denoising processing methods, and machine learning denoising processing methods.

[0035] S20, extracting the time / frequency domain characteristics of the molten pool radiation information corresponding to the target processing layer one by one.

[0036] First, the molten pool radiation information corresponding to each target processing layer after filtering and noise reduction is segmented and numbered through the characteristic process identification, laser switch and position vector to form an index relationship of "number-feature-melt channel-intensity signal".

[0037] Then, for the molten pool radiation information corresponding to each target processing layer after segmentation and numbering, the time / frequency domain features corresponding to each molten track are extracted one by one. In an embodiment of the present invention, the time / frequency domain features extracted in this step include 16 time domain features p1-p16 and 12 frequency domain features p17-p28, as shown in Table 1. Among them, the 16 time domain features include 10 dimensional features and 6 dimensionless features. The 10 dimensional features are mean, maximum, minimum, peak-to-peak value, median, variance, standard deviation, mean absolute deviation, root mean square and root amplitude, which mainly reflect the changes in the signal amplitude and energy of the molten pool radiation; the 6 dimensionless features are skewness, kurtosis, peak factor, waveform factor, pulse factor and margin factor, which mainly reflect the distribution of the molten pool radiation signal in the time domain. The frequency domain feature p17 reflects the magnitude of the radiation energy of the molten pool in the frequency domain. The frequency domain features p18-p20, p22, and p26-p28 characterize the degree of dispersion or concentration of the spectrum. The frequency domain features p21 and p23-p25 reflect the changes in the position of the main frequency band.

[0038]

[0039]

[0040] Table 1: Definition of time domain features and frequency domain features extracted by the present invention

[0041] Where x(n) is the time domain signal, n = 1, 2, ..., N; N is the total number of samples of any radiation signal on each melting channel. s(k) is the signal spectrum, k = 1, 2, ..., K; K is the number of spectrum lines of any radiation signal on each melting channel. k is the frequency value of the kth spectral line.

[0042] S30. Based on the extracted time / frequency domain features, different unsupervised learning anomaly detection algorithms are used to analyze and determine whether an anomaly occurs in the melt channel.

[0043] Since the causes and manifestations of abnormal melt paths in the LPBF process are often very complex, and a single algorithm model has a certain degree of singularity, and different algorithms have their own advantages and disadvantages, no single algorithm can give the optimal solution to the problem. Studies have shown that the combination of multiple classifiers is better than a single classifier, and the combination of multiple classifiers has been used in the field of computer vision and fault diagnosis of rotating machinery.

[0044] Based on the extracted time / frequency domain features, the present invention uses different unsupervised learning anomaly detection algorithms to analyze and solve the problem of whether the melt channel is abnormal, and then combines the solution results of different algorithms according to certain principles to obtain the optimal solution to the problem.

[0045] The present invention can use different anomaly detection algorithms based on unsupervised learning to analyze and solve the problem of whether the melt channel is abnormal. As shown in Table 2, the anomaly detection algorithms based on unsupervised learning mainly include: ABOD and COPOD based on probability model, PCA based on linear model, KNN, HBOS, LOF, CBLOF based on neighbor degree model, IForest, FB, LODA based on integration method, AutoEncoder and VAE based on neural network.

[0046]

[0047]

[0048] Table 2: Anomaly detection algorithms based on unsupervised learning

[0049] S40, combining the solution results of different unsupervised learning anomaly detection algorithms, calculating a performance index and using the performance index to quantitatively evaluate the processing quality.

[0050] After step S30, n different solution results can be obtained, which are recorded as O n (X), O n (X) is the solution result of the nth anomaly detection algorithm model, which represents the number of abnormal melt paths predicted by the nth anomaly detection algorithm model, n = 1, 2, 3, ... n. X is the input analysis data, which represents the time / frequency domain characteristics corresponding to each melt path in the target processing layer.

[0051] The present invention uses n different anomaly detection algorithm models to predict the number of abnormal melt channels in the target processing layer, obtains n different prediction results (solution results), and then merges the solution results of different algorithms according to certain principles to obtain the final number of abnormal melt channels MCOT. Since the averaging method is the simplest and most efficient multi-classifier combination strategy, the embodiment of the present invention selects the averaging method as the merging strategy for the solution results of multiple different anomaly detection algorithms, that is, the mean (mean) of the solution results of each different anomaly detection algorithm is taken to obtain the final number of abnormal melt channels MCOT:

[0052] MCOT=Mean(O 1 (X),O 2 (X),O 3 (X),…,O n (X)).

[0053] In other embodiments of the present invention, an extreme value method, a weighted average method, a weighted extreme value method, etc. may also be used as a strategy for merging the solution results of multiple different anomaly detection algorithms to calculate the final abnormal melt channel number MCOT.

[0054] It is known that the more normal melt channels there are, the better the printing quality must be, and vice versa. According to this principle, the present invention proposes a calculation method for measuring the quality performance index. Since the more abnormal melt channels there are, the worse the quality performance is, the performance index pp should be expressed as the inverse of MCOT. Therefore, the performance index pp can be calculated using the following formula:

[0055]

[0056] The in-situ quality evaluation method for laser powder bed fusion-processed components of the present invention extracts the time / frequency domain characteristics of the molten pool radiation information, and based on the extracted time / frequency domain characteristics, uses different unsupervised learning anomaly detection algorithms to analyze and solve whether the melt path has anomalies to quantitatively evaluate the processing quality, thereby solving the problem that the laser powder bed fusion quality analysis is highly dependent on the quality of the data set and on prior knowledge.

[0057] The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Those skilled in the art can make various equivalent changes and improvements based on the above embodiments, and all equivalent changes or modifications made within the scope of the claims should fall within the protection scope of the present invention.

Claims

1. An in-situ quality evaluation system for laser powder bed fusion processing components, characterized in that: Included are: A molten pool radiation collection device is used to collect molten pool radiation information of each processing layer during the printing process; The computer is used to obtain the molten pool radiation information of each processing layer collected by the molten pool radiation collection device, obtain the molten pool radiation information corresponding to the target processing layer from the molten pool radiation information of each processing layer and perform filtering and noise reduction processing, extract the time / frequency domain characteristics of the molten pool radiation information corresponding to the target processing layer one by one, use different unsupervised learning anomaly detection algorithms to analyze and solve whether an abnormality occurs in the melt path, merge the solution results of different unsupervised learning anomaly detection algorithms, calculate the performance index and use the performance index to quantitatively evaluate the processing quality.

2. The in-situ quality evaluation system for laser powder bed fusion processing components according to claim 1, characterized in that: The molten pool radiation collection device comprises: A spatial molten pool radiation collection unit is arranged at the upper top of the forming chamber and is used to collect spatial molten pool radiation information of one band or multiple bands of each processing layer; The coaxial optical path molten pool radiation collection unit is arranged in the laser processing optical path and is used to collect the coaxial optical path molten pool radiation information of one band or multiple bands of each processing layer.

3. The in-situ quality evaluation system for laser powder bed fusion processing components according to claim 2, characterized in that: The coaxial optical path molten pool radiation collection unit includes any one or any combination of a visible light radiation monitoring subunit, a near infrared radiation monitoring subunit, a colorimetric temperature measurement subunit, a laser reflection monitoring subunit, and an infrared radiation monitoring subunit.

4. The in-situ quality evaluation system for laser powder bed fusion processing components according to claim 3, characterized in that: The visible light radiation monitoring subunit includes a first semi-transparent and semi-reflective mirror, a first focusing filter group and a visible light radiation sensor, wherein the first semi-transparent and semi-reflective mirror can reflect radiation in the 400nm-750nm band and can transmit radiation in other bands, the first focusing filter group is used to focus and combine the radiation in the 400nm-750nm band and transmit it to the visible light radiation sensor, and the visible light radiation sensor is used to collect radiation in the 400nm-750nm band; The near-infrared radiation monitoring subunit includes a second semi-transparent and semi-reflective mirror, a second focusing filter group and a near-infrared radiation sensor, wherein the second semi-transparent and semi-reflective mirror can reflect radiation in the 750nm-900nm band and can transmit radiation in other bands, the second focusing filter group is used to focus and combine the radiation in the 750nm-900nm band and transmit it to the near-infrared radiation sensor, and the near-infrared radiation sensor is used to collect radiation in the 750nm-900nm band; The colorimetric temperature measurement subunit includes a third semi-transparent and semi-reflective mirror, a third focusing filter group and a colorimetric temperature measurement sensor. The third semi-transparent and semi-reflective mirror can reflect radiation in the 900nm-950nm band and can transmit radiation in other bands. The third focusing filter group is used to focus and combine the radiation in the 900nm-950nm band and transmit it to the colorimetric temperature measurement sensor. The colorimetric temperature measurement sensor is used to collect radiation in the 900nm-950nm band. The laser reflection monitoring subunit includes a fourth semi-transparent and semi-reflective mirror, a fourth focusing filter group and a laser reflection light sensor, wherein the fourth semi-transparent and semi-reflective mirror can reflect radiation in the 1030nm-1080nm band and can transmit radiation in other bands, the fourth focusing filter group is used to focus and combine the radiation in the 1030nm-1080nm band and transmit it to the laser reflection light sensor, and the laser reflection light sensor is used to collect radiation in the 1030nm-1080nm band; The infrared radiation monitoring subunit includes a reflector, a fifth focusing filter group and an infrared radiation sensor. The reflector can reflect radiation in a band greater than or equal to 1200nm. The fifth focusing filter group is used to focus and combine radiation in a band greater than or equal to 1200nm and transmit it to the infrared radiation sensor. The infrared radiation sensor is used to collect radiation in a band greater than or equal to 1200nm.

5. The in-situ quality evaluation system for a laser powder bed fusion processed component according to any one of claims 2 to 4, characterized in that: The spatial molten pool radiation collection unit includes any one or any combination of a visible light radiation sensor, a near infrared radiation sensor, a colorimetric temperature sensor, a laser reflection light sensor, and an infrared radiation sensor.

6. A method for in-situ quality evaluation of laser powder bed fusion processed components, characterized in that: The steps include: Obtain the molten pool radiation information corresponding to the target processing layer and perform filtering and noise reduction processing; Extract the time / frequency domain characteristics of the molten pool radiation information corresponding to the target processing layer one by one; Based on the extracted time / frequency domain features, different unsupervised learning anomaly detection algorithms are used to analyze and determine whether the melt channel is abnormal. The solution results of different unsupervised learning anomaly detection algorithms are merged, the performance index is calculated and used to quantitatively evaluate the processing quality.

7. The in-situ quality evaluation method for a laser powder bed melting processed component according to claim 6, characterized in that: The target processing layer is selected by adopting the following strategies: randomly selecting any n processing layers as the target processing layer; or taking the processing risk layer determined by evaluation as the target processing layer; or taking all processing layers of part processing as the target processing layer.

8. The in-situ quality evaluation method for a laser powder bed melting processed component according to claim 6, characterized in that: The time / frequency domain features include 16 time domain features and 12 frequency domain features.

9. The in-situ quality evaluation method for a laser powder bed fusion processed component according to claim 6, characterized in that: The performance index is calculated using the following formula: Among them, O n (X) is the solution result of the nth anomaly detection algorithm model, n=1, 2, 3, ...n, MCOT is the number of abnormal melt channels, and pp is the performance index.

10. The in-situ quality evaluation method for a laser powder bed fusion processed component according to any one of claims 6 to 9, characterized in that: The molten pool radiation information includes: spatial molten pool radiation information of one band or multiple bands; and / or coaxial optical path molten pool radiation information of one band or multiple bands.

Images