CN116117169B - SLM technological process defect detection method and device - Google Patents
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- CN116117169B CN116117169B CN202310074931.5A CN202310074931A CN116117169B CN 116117169 B CN116117169 B CN 116117169B CN 202310074931 A CN202310074931 A CN 202310074931A CN 116117169 B CN116117169 B CN 116117169B
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- 230000007547 defect Effects 0.000 title claims abstract description 171
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000001514 detection method Methods 0.000 title claims abstract description 55
- 230000008569 process Effects 0.000 title claims abstract description 50
- 238000007639 printing Methods 0.000 claims abstract description 92
- 239000000843 powder Substances 0.000 claims abstract description 45
- 230000008859 change Effects 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 15
- 230000000694 effects Effects 0.000 claims abstract description 3
- 239000010410 layer Substances 0.000 claims description 86
- 230000001502 supplementing effect Effects 0.000 claims description 6
- 239000002356 single layer Substances 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000008021 deposition Effects 0.000 abstract 1
- 230000007480 spreading Effects 0.000 description 14
- 238000003892 spreading Methods 0.000 description 14
- 239000002184 metal Substances 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
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- 238000011161 development Methods 0.000 description 2
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- 239000000463 material Substances 0.000 description 2
- 238000002844 melting Methods 0.000 description 2
- 230000008018 melting Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000010146 3D printing Methods 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/80—Data acquisition or data processing
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y50/00—Data acquisition or data processing for additive manufacturing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
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Abstract
The invention relates to a defect detection method and device for an SLM (selective laser deposition) technical process, wherein the method comprises the steps of S1, importing part data to be printed into printing equipment, and setting corresponding printing parameters, wherein the part data comprises at least one of the number of parts to be printed at the time, the position of the parts and the size information of the parts; s2, performing one-time powder paving operation until the powder layer covers the whole printing breadth; s3, image acquisition is carried out on the printing format through image acquisition equipment, and acquired image data are input into a detection system; s4, performing image processing on the acquired image data, and performing defect detection according to the processed data; s5, feeding back a detection result of defect detection to the printing equipment, and determining whether to change printing parameters according to the detection result by the printing equipment; s6, the laser selectively scans and cures the powder layer through the scanning galvanometer effect; s7, repeating the steps S2 to S6 until printing is finished.
Description
Technical Field
The invention relates to the field of SLM equipment, in particular to a defect detection method and device for an SLM process.
Background
SLM is a 3D printing technology, is a short name of selective laser melting technology (SELECTIVE LASER MELTING) in metal additive manufacturing technology, and is characterized in that metal powder is completely melted by utilizing the heat effect of laser beams, and then is rapidly cooled and solidified, and then is formed by a layer-by-layer stacking principle. Firstly, designing a three-dimensional model of a part, slicing the model by using layering software, and importing generated information into a workbench; according to the obtained information, the laser is utilized to carry out selective scanning on the powder, the powder is quickly melted under the action of laser heat, and along with the movement of the laser, molten metal liquid is quickly cooled and solidified and is adhered together, so that a first layer is formed on the forming cylinder. Then the forming cylinder descends by one layer of height, the powder spreading device scrapes the powder to a forming area, the powder is melted again by the laser beam, and the powder is cooled again to solidify and bond to form a second layer. Subsequently, the forming cylinder descends again, and the powder spreading device spreads the powder, so that the powder is reciprocated until the whole formed piece is obtained. The entire printing process is performed in an atmosphere filled with inert gas. Compared with other metal additive manufacturing technologies, the SLM has the advantages of being multiple in formable metal types, high in forming precision and high in forming speed, and the SLM technology is applied to the fields of aviation, aerospace, military industry, automobiles and the like.
The SLM process is a complex metallurgical process. In the forming process, the laser with high energy density moves rapidly, so that the metal melts and cools rapidly, and each layer of metal and each micro-molten pool are subjected to periodic thermal cycles, which have important influence on the microstructure of the metal piece and the formation of defects. These defects are mainly manifested as tissue defects, which are in turn divided into two categories: one type is defects caused by the characteristics of raw materials, which cannot be solved by optimizing process parameters and are mainly represented as air holes; the other type is defects caused by technological parameters or equipment and the like, and mainly comprises warpage, insufficient powder laying, pores, cracks, high-density inclusions and the like. These influencing factors are not isolated but only of great importance.
For the SLM process, the occurrence of partial defects is fatal to the printing process, for example, in the process of large-scale build printing with large format, because the powder spreading area is large, if the doctor blade is in the powder spreading process, the powder is not completely covered by the powder, so that the uncovered area is excessively sintered, the part is deformed, the collision risk of the part and the doctor blade in the subsequent printing process is greatly increased if the part cannot be processed in time, and the printing failure is very likely to be caused. For another example, in the case of performing multi-part printing or multi-process parameter test, since the printing state and parameters of each part may be different, the molding risk of each part is also different, and if one of the parts is severely warped and collides with the doctor blade during printing, all the parts printed in this batch will fail. These drawbacks greatly impact the production cost and efficiency of SLM processes, which are also difficult challenges faced by SLM processes in mass production and large part manufacturing.
Disclosure of Invention
In view of this, the invention provides a method and a device for detecting defects in an SLM process, which aim to detect and repair part of defects in the SLM process in real time so as to ensure smooth printing process and improve printing success rate of the SLM process. According to the method, the powder spreading condition and the part warping condition can be detected in the SLM printing process, if the powder spreading is found to be insufficient, printing is stopped, the powder spreading amount is adjusted, the powder spreading action is performed again until the powder spreading is normal, if the part has a warping trend, printing is stopped, and the printing task of the part is canceled, so that normal printing of other parts is ensured.
In order to achieve the above purpose, the invention adopts the following technical scheme: an SLM process defect detection method comprising the steps of:
S1, importing part data to be printed into printing equipment, and setting corresponding printing parameters, wherein the part data comprises at least one of the number of parts to be printed at the time, part positions and part size information.
S2, performing one-time powder paving operation until the powder layer covers the whole printing breadth.
S3, image acquisition is carried out on the printing format through image acquisition equipment, and acquired image data are input into a detection system.
S4, performing image processing on the acquired image data, and performing defect detection according to the processed data.
S5, feeding back a detection result of defect detection to the printing equipment, and determining whether to change printing parameters according to the detection result by the printing equipment.
S6, the laser selectively scans and cures the powder layer through the scanning galvanometer.
S7, repeating the steps S2 to S6 until printing is finished.
Further, the step of performing image processing on the acquired image data further includes:
The acquired image data is processed by a digital image processing algorithm, and contour information of each defect is extracted from the acquired image data, wherein the contour information comprises edge closed contours of the defects.
Further, the step of performing image processing on the acquired image data further includes:
And calculating the number and total area of defect contours of the current layer to serve as defect data of a single layer, and storing defect data of the nearest n layers, wherein n is more than or equal to 2.
Further, the step of the printing device confirming whether to change the printing parameters according to the detection result further includes:
Defects are classified according to defect data of the nearest n layers, wherein the defect data of the nearest n layers comprises a total defect area of the nearest n layers and a change rate of the defect area of the nearest n layers.
Further, the step of classifying the defects according to the defect data of the nearest n layers includes:
the defect data of the nearest n layers includes a defect feedback value including an initial value.
If the total defect area of the nearest m layers is larger than a preset threshold S, adding a first additional value to the initial value, wherein m is larger than or equal to 1 and smaller than or equal to n; if the change rate of the defect area obtained by the nearest n layers is larger than a preset slope threshold K, adding a second additional value to the initial value; the defect feedback value of the last n layers is the sum of the initial value, the first additional value and the second additional value.
Further, the defect data of the nearest n layers further comprises position information of the defects, and a rectangular area where the defects are located is confirmed according to the position information of the defects and outline information of the defects, wherein the outline of the edge of each defect is located in the rectangular area of the defects, and the rectangular area is marked on the acquired image.
Further, the step of confirming the rectangular area where the defect is located according to the position information of the defect and the outline information of the defect further comprises the step of corresponding the defect position information to the part data based on the position and the size of each part, and the rectangular area covers the section of the whole part corresponding to the defect in the current printing layer in the acquired image.
Further, the step of the printing device confirming whether to change the printing parameters according to the detection result further includes:
If the defect feedback value of the nearest n layers in the detection result is smaller than a first preset value, directly entering the step S6 without changing the printing parameters; if the defect feedback value of the nearest n layers in the detection result is larger than or equal to the first preset value and smaller than the second preset value, entering a powder supplementing operation, and entering a step S6 after powder supplementing is completed; if the defect feedback value of the nearest n layers in the detection result is larger than or equal to the second preset value and smaller than the third preset value, confirming a rectangular area corresponding to the defect with abnormal defect area change rate according to the position information of the defect, and scanning a vibrating mirror to skip the rectangular area in the step S6; if the defect feedback value of the nearest n layers in the detection result is larger than or equal to a third preset value, directly stopping printing.
Preferably, the initial value is 0, the first additional value is 1, and the second additional value is 3; the corresponding first preset value is 1, the second preset value is 3, and the third preset value is 4.
The application also discloses a defect detection system for the SLM process, which comprises the following steps:
the image acquisition unit is used for acquiring images of the printing breadth;
The target detection unit is used for preprocessing the acquired image;
The image processing unit is used for extracting a defect area by utilizing a Gaussian mixture distribution algorithm according to the preprocessed image and extracting the contour edge of the defect area;
and a defect marking unit marking the image based on the number, position, and total area of the defect contours.
Compared with the prior art, the invention has the beneficial effects that:
(1) The printing success rate of the SLM process is improved, a plurality of new material types and part types are faced in the continuous development process of the SLM process, a plurality of unknown risks are brought to the SLM process, and the defects possibly occurring can be corrected in time through detecting and repairing the defects in the printing process of the SLM process, so that the printing success rate of the SLM process is remarkably improved, and the cost loss caused by printing failure is effectively reduced;
(2) The intelligent level of the SLM process is improved, the human monitoring work in the SLM printing process is effectively reduced, and the labor cost of the SLM in mass production is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a schematic diagram of an SLM process defect detection flow provided by an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present 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.
Fig. 1 shows a schematic diagram of an SLM process defect detection flow provided by an embodiment of the invention. As shown in fig. 1, the SLM process defect detection method comprises the steps of:
S1, carrying out region division and path planning on a slice file (CLI format) of a printed part, setting corresponding technological parameters, then exporting a printable engineering file, and importing the engineering file into upper computer software of SLM equipment, wherein the slice file of the printed part comprises at least one of the number of the parts, the positions of the parts and the size information of the parts which are printed at the time.
S2, starting printing preparation work in the upper computer software, setting powder spreading quantity, and starting first-layer powder spreading, so that the first-layer powder spreading state is ensured to be good, and the whole printing breadth is covered by the powder layer.
As an alternative, if the substrate is smoother or the print layer is smaller, several layers of printing and powdering can be performed first until the powder covers the entire print area evenly.
S3, image acquisition is carried out on the printing format through image acquisition equipment, and acquired image data are input into a detection system;
s4, performing image processing on the acquired image data, and performing defect detection according to the processed data.
S5, feeding back a detection result of defect detection to the printing equipment, and determining whether to change printing parameters according to the detection result by the printing equipment.
S6, the laser selectively scans and cures the powder layer through the scanning galvanometer.
S7, repeating the steps S2 to S6 until printing is finished.
Further, the step of performing image processing on the acquired image data further includes:
The acquired image data is processed by a digital image processing algorithm, and contour information of each defect is extracted from the acquired image data, wherein the contour information comprises edge closed contours of the defects.
The detection system is developed based on OpenCV, and the acquired image is processed by using a Gaussian mixture distribution algorithm, so that a defect area can be extracted; and extracting the contour edge of the defect area, storing the contour data into a two-dimensional array, and marking the defect area on the acquired image.
Further, the step of performing image processing on the acquired image data further includes:
And calculating the number and total area of defect contours of the current layer to serve as defect data of a single layer, and storing defect data of the nearest n layers, wherein n is more than or equal to 2.
As an alternative implementation, the number and total area of defect contours of the current layer are calculated, and the number data and total area data are respectively inserted into two groups, and optionally, the two groups store the defect number and total area data of the last 10 layers, namely, 2.ltoreq.n.ltoreq.10.
Further, the step of the printing device confirming whether to change the printing parameters according to the detection result further includes:
Defects are classified according to defect data of the nearest n layers, wherein the defect data of the nearest n layers comprises a total defect area of the nearest n layers and a change rate of the defect area of the nearest n layers. And performing linear fitting by using a least square method according to the recorded defect area data of a plurality of layers to obtain a slope value, namely the defect area change rate of the nearest n layers.
Further, the step of classifying the defects according to the defect data of the nearest n layers includes:
the defect data of the nearest n layers includes a defect feedback value including an initial value.
If the total defect area of the nearest m layers is larger than a preset threshold S, adding a first additional value to the initial value, wherein m is larger than or equal to 1 and smaller than or equal to n. As a preferred embodiment, m=1, that is, for the defect area, only consider whether the total defect area of the nearest layer is greater than the preset threshold S, and for the case that the total defect area is greater than the preset threshold, prioritize whether repair can be performed by means of single-layer powder filling.
If the defect area change rate of the nearest n layers is larger than a preset slope threshold K, adding a second additional value to the initial value; the defect feedback value of the last n layers is the sum of the initial value, the first additional value and the second additional value. As an alternative implementation manner, when the value of n is 10, if the number of printing layers is less than 10 layers, the defect area change rate of the nearest n layers can be evaluated by setting a stage threshold, for example, a first slope threshold K1 is set for the case that the number of printing layers is 2-4 layers (the actual number of printing layers is less than 1/2 n), a second slope threshold K2 is set for the case that the number of printing layers is 5-10 layers (1/2 n is less than or equal to the actual number of printing layers is less than n), a third slope threshold K3 is set for the case that the number of printing layers is greater than 10 layers (the actual number of printing layers is greater than or equal to n), and the problem of missing statistical data is solved by setting the stage threshold. The defect feedback value of the nearest n layers may be only the sum of the initial value and the first additional value.
Further, the defect data of the nearest n layers further comprises position information of the defects, and a rectangular area where the defects are located is confirmed according to the position information of the defects and outline information of the defects, wherein the outline of the edge of each defect is located in the rectangular area of the defects, and the rectangular area is marked on the acquired image.
Further, the step of the printing device confirming whether to change the printing parameters according to the detection result further includes:
If the defect feedback value of the nearest n layers in the detection result is smaller than a first preset value, directly entering the step S6 without changing the printing parameters; if the defect feedback value of the nearest n layers in the detection result is larger than or equal to the first preset value and smaller than the second preset value, entering a powder supplementing operation, and entering a step S6 after powder supplementing is completed; if the defect feedback value of the nearest n layers in the detection result is larger than or equal to the second preset value and smaller than the third preset value, confirming a rectangular area corresponding to the defect with abnormal defect area change rate according to the position information of the defect, and scanning a vibrating mirror to skip the rectangular area in the step S6; if the defect feedback value of the nearest n layers in the detection result is larger than or equal to a third preset value, directly stopping printing.
Preferably, the initial value is 0, the first additional value is 1, and the second additional value is 3; the corresponding first preset value is 1, the second preset value is 3, and the third preset value is 4. If the returned defect number value is 0, the printing process is normal, the printing can be continued without defects; if the returned defect number value is 1, indicating that the powder is not covered in a large area, sending an alarm by the system, adjusting the powder spreading amount according to the latest recorded defect area, and executing the steps S2-S4 until the powder spreading is normal; if the returned defect number value is 3, the part is gradually warped and deformed in the printing process of a plurality of layers, and at the moment, the system sends out alarm information and pauses printing of the warping position; if the return defect number is 4, both of the above cases are indicated, and the case that the part suddenly has a large area of warpage is indicated, the reason for this phenomenon may be that the support suddenly breaks, or the part has collided with the doctor blade, and the system gives an alarm and pauses printing.
As an optional implementation manner, the step of confirming the rectangular area where the defect is located according to the position information of the defect and the outline information of the defect further comprises the step of corresponding the position information of the defect to the part data based on each part position and part size, and then in the acquired image, the rectangular area covers the section of the whole part corresponding to the defect in the current printing layer. At this time, if the returned defect number value is 3, it indicates that the part has a tendency of gradual warp deformation in the printing process of several layers, at this time, the system sends out alarm information, and the printing of the whole part at the position where the warp occurs is suspended.
The application also discloses a defect detection system for the SLM process, which comprises the following steps:
the image acquisition unit is used for acquiring images of the printing breadth;
The target detection unit is used for preprocessing the acquired image;
The image processing unit is used for extracting a defect area by utilizing a Gaussian mixture distribution algorithm according to the preprocessed image and extracting the contour edge of the defect area;
and a defect marking unit marking the image based on the number, position, and total area of the defect contours.
Compared with the prior art, the invention improves the printing success rate of the SLM process, and the SLM process can be faced with a plurality of new material types and part types in the continuous development process, which brings a plurality of unknown risks to the SLM process, and the defects possibly occurring can be corrected in time by detecting and repairing the defects in the printing process of the SLM process, thereby obviously improving the printing success rate of the SLM process and effectively reducing the cost loss caused by printing failure.
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A method for detecting defects in an SLM process comprising the steps of:
S1, importing part data to be printed into printing equipment, and setting corresponding printing parameters, wherein the part data comprises at least one of the number of parts to be printed at the time, part positions and part size information;
S2, performing one-time powder paving operation until the powder layer covers the whole printing breadth;
S3, image acquisition is carried out on the printing format through image acquisition equipment, and acquired image data are input into a detection system;
s4, performing image processing on the acquired image data, and performing defect detection according to the processed data;
S5, feeding back a detection result of defect detection to the printing equipment, and determining whether to change printing parameters according to the detection result by the printing equipment;
S6, the laser selectively scans and cures the powder layer through the scanning galvanometer effect;
S7, repeating the steps S2 to S6 until printing is finished;
The step of performing image processing on the acquired image data includes:
processing the acquired image data by using a digital image processing algorithm, and extracting contour information of each defect from the acquired image data, wherein the contour information comprises edge closed contours of the defects;
Calculating the number and total area of defect contours of the current layer as single-layer defect data, and storing the defect data of the nearest n layers, wherein n is more than or equal to 2;
the step of the printing device confirming whether to change the printing parameters according to the detection result comprises the following steps:
Classifying defects according to defect data of the nearest n layers, wherein the defect data of the nearest n layers comprise the total defect area of the nearest n layers and the change rate of the defect area of the nearest n layers;
the step of classifying defects according to defect data of the nearest n layers includes:
the defect data of the nearest n layers comprises a defect feedback value, wherein the defect feedback value comprises an initial value;
If the total defect area of the nearest m layers is larger than a preset threshold S, adding a first additional value to the initial value, wherein m is larger than or equal to 1 and smaller than or equal to n;
if the defect area change rate of the nearest n layers is larger than a preset slope threshold K, adding a second additional value to the initial value;
the defect feedback value of the last n layers is the sum of the initial value, the first additional value and the second additional value.
2. The SLM process defect detection method of claim 1 wherein said nearest n layers of defect data further comprises defect location information, and wherein a rectangular region where said defect is located is identified based on said defect location information and defect profile information, wherein an edge profile of each defect is located within said rectangular region where said defect is located, and said rectangular region is marked on said acquired image.
3. The SLM process defect detection method of claim 2 wherein the step of identifying a rectangular area where a defect is located based on position information of said defect and profile information of the defect comprises: and based on each part position and part size, the defect position information is corresponding to the part data, and then in the acquired image, the rectangular area covers the section of the whole part corresponding to the defect in the current printing layer.
4. The SLM process defect detection method of claim 2 wherein said printing apparatus step of confirming whether to change print parameters based on said detection result further comprises:
if the defect feedback value of the nearest n layers in the detection result is smaller than a first preset value, directly entering the step S6 without changing the printing parameters;
If the defect feedback value of the nearest n layers in the detection result is larger than or equal to the first preset value and smaller than the second preset value, entering a powder supplementing operation, and entering a step S6 after powder supplementing is completed;
If the defect feedback value of the nearest n layers in the detection result is larger than or equal to the second preset value and smaller than the third preset value, confirming a rectangular area corresponding to the defect with abnormal defect area change rate according to the position information of the defect, and scanning a vibrating mirror to skip the rectangular area in the step S6;
If the defect feedback value of the nearest n layers in the detection result is greater than or equal to a third preset value, directly stopping printing.
5. The SLM process defect detection method of claim 4 wherein said initial value is 0, said first added value is 1 and said second added value is 3; the corresponding first preset value is 1, the second preset value is 3, and the third preset value is 4.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107175329A (en) * | 2017-04-14 | 2017-09-19 | 华南理工大学 | A kind of 3D printing successively detects reverse part model and positioning defect apparatus and method |
| CN109164111A (en) * | 2018-09-28 | 2019-01-08 | 东南大学 | Based on shared galvanometer SLM in line laser defects detection equipment and method |
| CN112179312A (en) * | 2020-09-29 | 2021-01-05 | 华中科技大学 | Online detection system and method suitable for surface quality of building 3D printed piece |
| CN113732308A (en) * | 2021-08-10 | 2021-12-03 | 广东工业大学 | Detection and repair method and detection and repair device for 3D printing pore defects |
| JP2022067408A (en) * | 2020-10-20 | 2022-05-06 | 石川県 | Molding state estimation system, method, computer program and method for learning learning model |
-
2023
- 2023-01-29 CN CN202310074931.5A patent/CN116117169B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107175329A (en) * | 2017-04-14 | 2017-09-19 | 华南理工大学 | A kind of 3D printing successively detects reverse part model and positioning defect apparatus and method |
| CN109164111A (en) * | 2018-09-28 | 2019-01-08 | 东南大学 | Based on shared galvanometer SLM in line laser defects detection equipment and method |
| CN112179312A (en) * | 2020-09-29 | 2021-01-05 | 华中科技大学 | Online detection system and method suitable for surface quality of building 3D printed piece |
| JP2022067408A (en) * | 2020-10-20 | 2022-05-06 | 石川県 | Molding state estimation system, method, computer program and method for learning learning model |
| CN113732308A (en) * | 2021-08-10 | 2021-12-03 | 广东工业大学 | Detection and repair method and detection and repair device for 3D printing pore defects |
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