A method for predicting surface roughness of parts manufactured by selective laser melting additive manufacturing

Abstract

The present invention discloses a method for predicting the surface roughness of parts manufactured by selective laser melting additive manufacturing, which belongs to the field of additive manufacturing technology. This method constructs different roughness prediction models for the first time based on the relationship between the inclination direction of the printed workpiece and the scraping direction of the scraper, thereby improving the prediction accuracy of the surface roughness of the workpiece prepared by the SLM process. The constructed model is a polynomial model. Compared with the traditional model, on the one hand, the model considers the relationship between the change of surface roughness with the inclination direction in both the forward scraper and reverse scraper cases, thereby improving the prediction accuracy. On the other hand, compared with the traditional linear model, the model enhances the fitting of nonlinear relationships; and compared with black box nonlinear prediction models such as artificial neural networks, the model has an explicit and interpretable expression, which is convenient for direct application by engineers. This method has a certain degree of universality and is applicable to different SLM printing process parameters, printing shapes and printing materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of metal selective laser melting additive manufacturing, and relates to a method for predicting the surface roughness of a part manufactured by selective laser melting additive manufacturing. Background Art

[0002] Selective laser melting (SLM) is an advanced additive manufacturing technology that creates complex metal parts by depositing metal powder layer by layer, then laser melting and rapidly cooling it. Due to its unparalleled advantages in material processing and structural design, it has found widespread application in key industries such as aviation, aerospace, and automotive manufacturing, and has become a new research hotspot in the field of structurally integrated manufacturing of metal materials.

[0003] Different placement strategies for additively manufactured 3D parts result in varying inclinations of the part surface relative to the horizontal substrate. For workpieces with inclined plates, this can have the following impacts during the SLM process: 1) Surfaces with varying inclinations experience varying stresses due to the weight of the material above and the support provided by the material, as well as the rapid cooling during the additive manufacturing process; and 2) the degree of the "step effect" caused by slicing varies across surfaces with varying inclinations. These factors can lead to differences in surface roughness at different inclinations, thus affecting the surface quality of the printed part. Therefore, monitoring and predicting the surface quality of SLM parts at different inclinations is crucial.

[0004] Currently, researchers at home and abroad have conducted experimental quantitative testing and statistical analysis on the inclination angle and surface roughness of printed workpieces, obtaining qualitative or simple quantitative relationships. However, since the relationship between the surface roughness of different materials and the inclination angle is not a single linear one, a simple linear model cannot accurately fit it. Therefore, existing methods still suffer from low accuracy and poor applicability, making them incapable of direct application to the prediction of surface roughness of different metal parts in actual SLM forming. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a surface roughness prediction method for selective laser melting additively manufactured parts to solve the problems of low surface roughness prediction accuracy and poor applicability of existing additively manufactured parts.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for predicting the surface roughness of a part manufactured by selective laser melting additive manufacturing comprises the following steps:

[0008] Step 1: Design two sets of inclined plate models in the simulation software. Each set of inclined plate models includes several inclined plates with the same inclination direction. The inclined plates in one set of inclined plate models have the same inclination direction as the scraper spreading direction, while the inclined plates in another set of inclined plate models have the opposite inclination direction to the scraper spreading direction.

[0009] Step 2: Print materials according to the two sets of inclined plate models through selective laser melting to obtain two sets of inclined workpieces, namely, a forward scraper inclined workpiece group and a reverse scraper inclined workpiece group;

[0010] Step 3: Separate the inclined workpiece from the substrate, and measure and calculate the roughness of the upper inclined surface and the roughness of the lower inclined surface of all the inclined workpieces;

[0011] Step 4: Based on the roughness of the upper inclined surface and the roughness of the lower inclined surface in the group of workpieces tilted with the scraper, a mathematical relationship between the inclination angle of the inclined surface and the surface roughness in the group of workpieces tilted with the scraper is constructed; based on the roughness of the upper inclined surface and the roughness of the lower inclined surface in the group of workpieces tilted with the scraper, a mathematical relationship between the inclination angle of the inclined surface and the surface roughness in the group of workpieces tilted with the scraper is constructed, and two sets of roughness prediction models are obtained;

[0012] Step 5: When performing selective laser melting printing on an inclined workpiece of the same material, the roughness of the printed inclined workpiece is predicted using the corresponding roughness prediction model.

[0013] A further improvement of the present invention is:

[0014] Preferably, in step 1, in the same group of inclined plate models, the inclination angles of the inclined plates are different.

[0015] Preferably, in the same set of inclined plate models, the angle between the upper inclined surface and the horizontal base plate, and the angle between the lower inclined surface and the horizontal base plate are 20-55°.

[0016] Preferably, in step 3, the inclined workpiece is separated from the substrate by wire cutting.

[0017] Preferably, in step 3, the upper inclined surface is an inclined surface with an acute angle between the surface normal vector and the positive forming direction; and the lower inclined surface is an inclined surface with an obtuse angle between the surface normal vector and the positive forming direction.

[0018] Preferably, in step 3, the roughness of the upper inclined surface and the lower inclined surface is measured by a laser confocal optical microscope, and the microscope magnification is 1000 times.

[0019] Preferably, in step 3, for the same plane, an average value of several times of measured transverse roughness and several times of measured longitudinal roughness is calculated as the roughness of the measured inclined surface.

[0020] Preferably, in step 4, the mathematical fitting process is to perform mathematical fitting on the upper inclined surface roughness and the lower inclined surface roughness obtained in step 3 for the same group of inclined workpieces by the least squares method to obtain a polynomial, which is the roughness prediction model for the same group of inclined workpieces.

[0021] Preferably, the highest degree of the polynomial is 2.

[0022] Preferably, in step 5, the printing direction and the scraper powder spreading direction are determined based on the roughness prediction result of the inclined workpiece to be printed and the roughness requirement of the inclined workpiece to be printed.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention discloses a method for predicting the surface roughness of parts manufactured by selective laser melting additive manufacturing. This method first discovers that the inclination direction and inclination angle of the inclined surface of the printed inclined workpiece, as well as the direction of the scraper spreading powder, are related to the surface roughness of the printed inclined workpiece during SLM printing. Two surface roughness prediction models for the forward and reverse scraper directions are established, respectively. Compared with existing prediction methods, the accuracy of SLM surface roughness prediction is improved. In addition, the present invention establishes a nonlinear mapping relationship between surface inclination angle and surface roughness based on a polynomial model and proposes an explicit prediction model. On the one hand, compared with traditional linear models, the model enhances the fitting of nonlinear relationships; on the other hand, compared with black box nonlinear prediction models such as artificial neural networks, the model can provide explicit and interpretable expressions to guide engineers in the application of the present invention. The method has a certain degree of universality. For different SLM printing process parameters, printing shapes and printing materials, the corresponding prediction model can be determined through preliminary printing setting experiments, making the model more targeted and highly accurate when applied. Therefore, the method of the present invention has unique advantages in the application of surface roughness prediction in selective laser melting additive manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flowchart of the method for rapid and accurate prediction of surface roughness of SLM parts.

[0026] Figure 2 CAD models of inclined plates at different inclination angles and in different directions along or against the scraper blade. (a) Example of a 55° inclined plate CAD model and its upper inclined surface; (b) 55° inclined plate CAD model with lower inclined surface; (c) CAD models of inclined plates at different inclination angles along or against the scraper blade direction.

[0027] Figure 3This is the forming condition of the inclined plate part obtained by cutting from the substrate after SLM printing contact; among them: (a) the inclined plate is placed in the direction of the scraper spreading powder; (b) the inclined plate is placed in the opposite direction of the scraper spreading powder.

[0028] Figure 4 The morphology and roughness of the inclined surface were obtained using a laser confocal microscope. (a) The upper inclined surface of a 25° forward inclined plate; (b) The lower inclined surface of a 25° forward inclined plate; (c) The inclined surface of a 25° reverse inclined plate; (d) The lower inclined surface of a 25° reverse inclined plate.

[0029] Figure 5 The roughness curves of the upper and lower inclined surfaces vary with angle for different forward and reverse scraper directions. The dotted line represents the lower inclined surface, the solid line represents the upper inclined surface, the red color represents the forward scraper spreading direction, and the blue color represents the reverse scraper spreading direction.

[0030] Figure 6 The two curves are the surface roughness scatter points in the forward and reverse directions of the scraper and their quadratic polynomial fitting in the θ space formed by the inclined surface and the horizontal surface. The red color represents the forward direction of the scraper, and the blue color represents the reverse direction of the scraper. DETAILED DESCRIPTION

[0031] The present invention is described in further detail below with reference to the accompanying drawings:

[0032] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention; the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; in addition, unless otherwise expressly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0033] The present invention discloses a method for quickly and accurately predicting the surface roughness of a part manufactured by selective laser melting additive manufacturing, comprising the following steps:

[0034] Step 1: Design two groups of inclined plate models with different inclination angles in the simulation software. One group is placed in the direction of the scraper spreading powder (referred to as the forward scraper placement), and the other group is placed in the reverse direction of the scraper spreading powder (referred to as the reverse scraper placement). Two groups of inclined plate models are obtained, and each group of inclined plate models includes several inclined plates with the same inclination direction.

[0035] Step 2: Select the printing powder material, determine the printing equipment and process parameters, and perform SLM printing; obtain two groups of inclined workpieces, namely the forward scraper inclined workpiece group and the reverse scraper inclined workpiece group.

[0036] Step 3: Using the wire cutting method, the printed inclined plates with different inclination angles are cut off from the substrate, and the roughness of the upper and lower inclined surfaces of all inclined workpieces is measured respectively, and the data are collected and organized;

[0037] Step 4. Based on the roughness of the upper inclined surface and the roughness of the lower inclined surface in the group of workpieces tilted with the scraper, a mathematical relationship between the inclination angle of the inclined surface and the surface roughness in the group of workpieces tilted with the scraper is constructed; based on the roughness of the upper inclined surface and the roughness of the lower inclined surface in the group of workpieces tilted with the reverse scraper, a mathematical relationship between the inclination angle of the inclined surface and the surface roughness in the group of workpieces tilted with the reverse scraper is constructed. The fitting method is the least squares method, and two sets of polynomials are obtained, which are the mapping relationship between the inclination angle of the inclined surface and the surface roughness. The inclined surface roughness prediction model of the workpiece tilted with the scraper and the inclined surface roughness prediction model of the workpiece tilted with the reverse scraper are obtained.

[0038] In step 5, when printing parts made of the same material using the printing equipment and process parameters in step 2, the roughness of each surface unit of the part can be calculated based on the relative relationship between the scraper movement direction (forward or reverse) and the tilt direction of the surface unit during printing. The roughness prediction model in step 4 can be used to calculate the roughness of each surface unit. The average roughness of the entire part can be obtained by taking the average. Alternatively, the surface roughness of the key surfaces in the part that affect the target performance can be calculated separately.

[0039] In some embodiments of the present invention, in step 1, each inclined plate in each set of inclined plate models is defined as a cube with a certain inclination angle, with the top surface being a horizontal plane, and the model height varying with the inclination angle. The inclined plate is placed in the direction of the scraper when the inclination direction is aligned with the direction of the scraper spreading the powder, and placed in the opposite direction when the inclination direction is aligned with the direction of the scraper spreading the powder.

[0040] It should be understood that in step 1, the inclination values ​​of multiple inclined plates in the same set of inclined plate models can be different, and the inclination angles in each set of inclined plate models can be set with denser step sizes to make the data volume richer and enhance the accuracy of the entire model.

[0041] For the same inclined plate, the inclined surface with an acute angle between the surface normal vector and the positive angle of the forming direction is defined as the upper inclined surface, and the inclined surface with an obtuse angle between the surface normal vector and the positive angle of the forming direction is defined as the lower inclined surface.

[0042] It should be noted that, for the same inclined plate, the angle between the upper inclined surface and the substrate and the angle between the lower inclined surface and the substrate can be the same or different, and can be set specifically according to the target forming shape of the final printed workpiece; however, the results can be used to construct the final roughness prediction model.

[0043] As a preferred solution, in the same set of inclined plate models, the angles between the upper inclined surface and the horizontal substrate, as well as the angles between the lower inclined surface and the horizontal substrate, are 20-55°, which are mostly the actual workpiece inclination angles. Furthermore, these angles can be adjusted according to the actual printing situation.

[0044] The above-mentioned setting method of the printing model enables the method to be applicable to constructing roughness prediction models for various workpieces to be printed.

[0045] In some embodiments of the present invention, in step 2, the powder material, printing equipment and process parameters are not restricted, as long as the material composition and each SLM printing parameter in the same batch used for modeling remain consistent; in the actual modeling process, the powder material, printing equipment and process parameters are consistent with the workpiece of the subsequent surface roughness process that needs to be predicted.

[0046] In some embodiments of the present invention, in step 3, the surface roughness Ra is measured using a laser confocal optical microscope at 1000x magnification, and the average of five transverse Ra measurements and five longitudinal Ra measurements is calculated as the surface roughness Ra value for that surface. Specifically, transverse Ra refers to the degree of microscopic unevenness on the part surface in the transverse direction (i.e., perpendicular to the cutting direction of the tool). Longitudinal Ra refers to the degree of microscopic unevenness on the part surface in the longitudinal direction (i.e., parallel to the cutting direction of the tool). Simultaneously measuring both transverse Ra and longitudinal Ra, and taking into account multiple measurement values, can provide a more representative roughness profile for each inclined surface.

[0047] In some embodiments of the present invention, in step 4, the maximum order of polynomial model fitting is selected as 2, and the roughness values ​​of the upper / lower inclined surfaces in the forward / reverse scraper direction are respectively brought into the model to fit the coefficients and intercept sizes, so that the prediction model is expressed in the form of a display equation.

[0048] The range of the inclined plate angle discussed in step 1 is 0°~90°, and the experiment needs to be divided into two cases: forward and reverse scraping. Each case corresponds to four cases of upper and lower inclined surfaces, which is not convenient for the actual application of SLM additive manufacturing. In order to achieve a unified analysis and quantitative prediction of the roughness of all upper and lower inclined surfaces, the angle between the surface vector of the upper and lower inclined surfaces and the horizontal vector is defined as θ, and the relationship between the surface roughness and θ is quantitatively discussed, as shown in the figure. Figure 6 As shown in the figure. The value range of θ is 0°~180°, where 90°<θ<180° and 0°<θ<90° represent the angles between the upper / lower inclined surface and the horizontal substrate, respectively. The blue points are in the reverse direction of the scraper, and the red points are in the same direction as the scraper.

[0049] Furthermore, based on the least squares method, the quadratic polynomial calculation formula for surface roughness was fitted to the forward scraping / reverse scraping roughness results respectively.

[0050] By fitting, the surface roughness of the scraper The calculation formula is:

[0051]

[0052] in, 、 are the coefficients of the quadratic and linear terms, respectively. is a constant term.

[0053] Surface roughness of reverse scraper The calculation formula is:

[0054]

[0055] in, 、 are the coefficients of the quadratic and linear terms, respectively. is a constant term.

[0056] It should be understood that the two roughness prediction models constructed in the above process correspond to the model for forward and reverse squeegee placement, respectively. Using the roughness measurement data from these different models, models corresponding to different printing directions are constructed for prediction. The model corresponding to the same printing direction can simultaneously predict the roughness of both the upper and lower inclined surfaces of a printed workpiece.

[0057] Furthermore, this method can be used in actual SLM applications. After determining the part placement and forming method, the inclination angle θ of each surface in the part model can be used to predict the roughness of the corresponding surface by applying it to different prediction models based on the forward and reverse scraper directions. This allows for a quantitative and accurate prediction of the roughness of all surfaces of the entire part. The roughness prediction results, combined with the roughness of the target part to be printed, can also be used to determine the printing direction, ensuring that the final roughness of the produced part meets the required quality.

[0058] The following is further described with reference to specific embodiments.

[0059] Example 1

[0060] Step 1: Design and place the three-dimensional drawing of the inclined plate parts.

[0061] Based on UG 3D software, design the inclined plate part model with different inclination angles. Here, "inclination angle" is defined as the angle between the inclined surface of the inclined plate and the horizontal plane (take the positive acute angle). Design the inclined plate model as an inclined cube, and define the inclined surface with an acute angle between the surface normal vector and the positive angle of the forming direction as the upper inclined surface, and the inclined surface with an obtuse angle between the surface normal vector and the positive angle of the forming direction as the lower inclined surface, as shown in the attached figure. Figure 2 As shown in (a) and (b) in the figure, the bottom and top surfaces remain horizontal, with a length and width of 30 mm and 10 mm, respectively, and a slope length of 30 mm. The two side surfaces remain vertical, while the two slopes are inclined, with an angle range of 20° to 55° in 5° steps. The slope length is fixed at 30 mm, and the height of the inclined plate decreases as the angle decreases. In summary, a total of eight inclined plate models with different inclination angles were designed.

[0062] Based on Magcis software, the printing position and direction of the inclined plate model with different inclination angles are placed on the virtual SLM printing platform, as shown in the attached figure. Figure 2 As shown in (c) in the figure, the orientation in which the inclined surface is aligned with the scraper's powder spreading direction is defined as forward-direction placement, while the reverse direction is defined as reverse-direction placement. To explore the effect of different forward and reverse scraper directions on surface roughness, eight inclined plate models were placed once in the forward direction and once in the reverse direction on the Magcis software virtual platform. In total, 16 inclined plate models were subsequently subjected to SLM additive manufacturing.

[0063] Step 2: SLM additive manufacturing of the inclined plate model.

[0064] In this example, AlSi10Mg is used as an example material to verify the method of the present invention.

[0065] In step 2.1, AlSi10Mg powder with a particle size of 15-53 μm was selected. The measured element contents were: Si 10.14%, Mg 0.31%, Fe 0.062%, H 0.0017%, O 0.044%, Pb and Sn contents were all <0.02%, Cu, Mn, Ni, Zn, and Ti contents were all <0.01%, N content was less than 0.002%, and the balance was Al.

[0066] In step 2.2, the SLM printing machine used in this case is the BLT-S320. Powder was placed into the SLM printer's powder supply chamber and printing began. The physical laser process parameters were designed as follows: laser power 340 W, scanning speed 1400 mm / s, scanning pitch 0.13 mm, and layer height 0.03 mm. The scanning strategy was strip scanning, with an overlap ratio of 0.02 mm, a rotation angle of 67° between adjacent layers, and substrate preheating to 80°C.

[0067] Step 2.3: After printing, separate the inclined plate sample from the substrate using wire cutting.

[0068] Step 3: Compare the forming conditions of the inclined plates placed in different directions of the scraper

[0069] Step 3.1, arrange the inclined plates in different directions of the scraper spreading the powder, as shown in the attached figure. Figure 3 As shown in (a) and (b) in the figure, it can be found that the forming process can still be carried out when the inclined plate angle is 30° in the direction of the scraper, but fails when the angle is lower than 30°. However, the forming process fails when the inclined plate angle is 30° in the direction of the reverse scraper, which indicates that the forming limit angle of the inclined plate in the direction of the scraper is larger than that in the direction of the reverse scraper.

[0070] In step 3.2, a laser confocal optical microscope was used to capture the roughness Ra of the upper and lower inclined surfaces of 16 inclined plates at a magnification of 1000 times. The average of the transverse Ra and longitudinal Ra values ​​was calculated as the surface roughness Ra value of the surface. Taking an inclined plate with an inclination angle of 25° as an example, the organization diagrams of different forward / reverse scraping directions and different upper and lower inclined surfaces were compared, as well as the surface roughness diagram with better visualization effect, as shown in the attached figure. Figure 4As shown in (a)-(d) in the figure. It can be found that for the same upward inclined surface, the upper inclined surface in the direction of the scraper is similar to the upper inclined surface in the direction of the reverse scraper. Clear transverse molten pool stripes can be seen from the organizational diagram, and the peak values ​​of the surface roughness are similar, but the surface roughness in the direction of the reverse scraper is more volatile than that in the direction of the scraper. For the same downward inclined surface, the surface roughness of the downward inclined surface in the direction of the scraper is significantly lower than that in the direction of the reverse scraper, and the peak value difference of the surface roughness is large. The surface roughness in the direction of the reverse scraper is more volatile, and the roughness peak of the spheroidized protrusion is sharper. In addition, regardless of whether it is in the direction of the scraper or the scraper, the roughness of the upper inclined surface is significantly lower than that of the lower inclined surface, and the forming quality of the lower inclined surface is worse. The statistics of the roughness of the inclined plates in the direction of the scraper / reverse scraper and the upper / lower inclined surfaces of all angles are shown in the attached figure. Figure 5 As shown, this indicates that, despite the same inclination angle, the forming surface quality of the inclined plate placed along the scraper direction is better than that of the inclined plate placed against the scraper direction. Among them, the forming quality of the upper inclined surface of the two is relatively close, while the forming quality of the lower inclined surface is quite different.

[0071] Step 4: Establish a surface roughness prediction model in the forward / reverse scraper direction.

[0072] Step 4.1: The current design of the inclined plate has an inclination angle range of 0°~90°, and needs to be divided into four cases: up / down inclined surface and forward / reverse scraper. This is not convenient for the practical application of SLM additive manufacturing. In order to achieve a unified analysis and quantitative prediction of the roughness of all up / down inclined surfaces, the angle between the surface vector of the up / down surface and the horizontal vector is defined as θ, and the relationship between the surface roughness and θ is quantitatively discussed, as shown in the figure. Figure 6 As shown in the figure, the value of θ ranges from 0° to 180°, where 90°<θ<180° and 0°<θ<90° represent the upward / downward inclined surface conditions, respectively. The blue points are in the reverse direction of the scraper, and the red points are in the forward direction of the scraper.

[0073] In step 4.2, based on the least squares method, the surface roughness quadratic polynomial calculation formula is fitted to the forward scraping / reverse scraping roughness results respectively.

[0074] By fitting, the surface roughness of the workpiece is adjusted along the scraper tilt The calculation formula is:

[0075]

[0076] Its coefficient of determination R 2 The value is 0.75 and the root mean square error RMSE is 3.56, which shows that the fitted formula has high prediction accuracy.

[0077] Surface roughness of workpiece with reverse scraper tilt The calculation formula is:

[0078]

[0079] Its coefficient of determination R 2 The value is 0.93 and the root mean square error RMSE is 2.33, which shows that the fitted formula has high prediction accuracy.

[0080] Finally, for the target part model, after determining the forward / reverse scraper direction and its inclination angle θ of the patch unit, and substituting them into the above two equations, a quantitative and accurate prediction of the surface roughness of the entire part can be achieved.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting the surface roughness of a part manufactured by selective laser melting additive manufacturing, characterized in that: The following steps are involved: Step 1: Design two sets of inclined plate models in the simulation software. Each set of inclined plate models includes several inclined plates with the same inclination direction. The inclined plates in one set of inclined plate models have the same inclination direction as the scraper spreading direction, while the inclined plates in another set of inclined plate models have the opposite inclination direction to the scraper spreading direction. Step 2: Print materials according to the two sets of inclined plate models through selective laser melting to obtain two sets of inclined workpieces, namely, a forward scraper inclined workpiece group and a reverse scraper inclined workpiece group; Step 3: Separate the inclined workpiece from the substrate, and measure and calculate the roughness of the upper inclined surface and the roughness of the lower inclined surface of all the inclined workpieces; Step 4: Based on the roughness of the upper inclined surface and the roughness of the lower inclined surface in the group of workpieces tilted with the scraper, a mathematical relationship between the inclination angle of the inclined surface and the surface roughness in the group of workpieces tilted with the scraper is constructed; based on the roughness of the upper inclined surface and the roughness of the lower inclined surface in the group of workpieces tilted with the scraper, a mathematical relationship between the inclination angle of the inclined surface and the surface roughness in the group of workpieces tilted with the scraper is constructed, and two sets of roughness prediction models are obtained; Step 5: When performing selective laser melting printing on an inclined workpiece of the same material, the roughness of the printed inclined workpiece is predicted using the corresponding roughness prediction model.

2. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 1, characterized in that: In step 1, in the same set of inclined plate models, the inclination angles of the inclined plates are different.

3. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 1, characterized in that: In the same set of inclined plate models, the angle between the upper inclined surface and the horizontal base plate, and the angle between the lower inclined surface and the horizontal base plate are 20-55°.

4. The surface roughness prediction method for selective laser melting additively manufactured parts according to claim 1, characterized in that: In step 3, the inclined workpiece is separated from the substrate by wire cutting.

5. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 1, characterized in that: In step 3, the upper inclined surface is an inclined surface with an acute angle between the surface normal vector and the positive forming direction; the lower inclined surface is an inclined surface with an obtuse angle between the surface normal vector and the positive forming direction.

6. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 1, characterized in that: In step 3, the roughness of the upper inclined surface and the lower inclined surface is measured by a laser confocal optical microscope, and the microscope magnification is 1000 times.

7. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 1, characterized in that: In step 3, for the same plane, the average value of the transverse roughness measured several times and the longitudinal roughness measured several times is calculated as the roughness of the measured inclined surface.

8. The surface roughness prediction method for selective laser melting additively manufactured parts according to claim 1, characterized in that: In step 4, the mathematical fitting process is to mathematically fit the upper inclined surface roughness and the lower inclined surface roughness obtained in step 3 by the least squares method for the same group of inclined workpieces to obtain a polynomial, which is the roughness prediction model of the same group of inclined workpieces.

9. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 8, characterized in that: The highest degree of the polynomial is 2.

10. The surface roughness prediction method of a selective laser melting additively manufactured part according to claim 8, characterized in that: In step 5, the printing direction and the scraper powder spreading direction are determined according to the roughness prediction result of the inclined workpiece to be printed and the roughness requirement of the inclined workpiece to be printed.

Images