A surface quality prediction and evaluation method for selective laser melting parts

Abstract

The present invention discloses a method for predicting and evaluating the surface quality of parts formed by laser selective melting, comprising the following steps: 1. determining the laser selective melting forming process and typical characteristic structure parameters; 2. using different laser selective melting forming processes to prepare different typical characteristic structures; 3. measuring the original surface quality of the typical characteristic structure with powder sticking; 4. obtaining the actual surface quality of the typical characteristic structure after removing the surface powder sticking; 5. establishing a BP neural network prediction model; 6. predicting the surface quality of parts formed by laser selective melting; and 7. evaluating the surface quality of parts formed by laser selective melting. The present invention introduces a step of obtaining the actual surface quality after removing the surface powder sticking, eliminates the interference of the surface powder sticking on the surface quality evaluation, constructs the relationship between the forming process and the typical characteristic structure parameters - the original surface quality - the actual surface quality, establishes a BP neural network prediction model, and realizes the rapid prediction and accurate evaluation of the surface quality of the formed part.

Description

Technical Field

[0001] The invention belongs to the technical field of laser selective melting, and in particular relates to a surface quality prediction and evaluation method for a laser selective melting formed part. Background Art

[0002] Selective laser melting (SLM) is a high-performance metal additive manufacturing technology used to create complex, precise structures. It holds broad application prospects in high-tech fields such as aerospace. Due to the complexity of the SLM forming process, variations in parameters such as powder properties, process parameters, scanning strategy, structure forming angle, structure scale, and structure type can lead to significant variations in the surface quality of the formed part, which in turn affects the part's service performance in practical engineering applications. However, traditional evaluation of the surface quality of formed parts requires not only the use of complex roughness measurement equipment to characterize the surface quality of the formed part, but also consumes considerable time and testing costs. Furthermore, it is worth noting that the actual surface topography of the formed part, after removing the surface powder adhering to it, has a significant impact on mechanical properties. However, the presence of a large amount of adhering powder on the original surface of the formed part prevents the effective display of the actual surface topography. The raw surface quality of the formed part with adhering powder, as measured by roughness measurement equipment, cannot accurately represent the actual surface quality, severely hindering the evaluation of the formed part's surface quality. This issue significantly affects the analysis of the causes of bearing failure of the formed part and significantly restricts the further application of SLM technology. Therefore, there is an urgent need to construct a surface quality prediction and evaluation method for laser selective melting formed parts, so as to achieve rapid prediction and accurate evaluation of the original and actual surface quality of the formed parts and improve the economic benefits of laser selective melting technology. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a surface quality prediction and evaluation method for laser selective melting formed parts in view of the deficiencies in the above-mentioned prior art.

[0004] To solve the above technical problems, the present invention adopts a technical solution: a method for predicting and evaluating the surface quality of a part formed by selective laser melting, characterized in that the method comprises the following steps:

[0005] Step 1: Determine the selective laser melting forming process and typical characteristic structure parameters: the selective laser melting forming process parameters include powder characteristics, process parameters, and scanning strategy;

[0006] The typical characteristic structural parameters include structural forming angle, structural scale, and structural type;

[0007] Step 2: Prepare different typical characteristic structures using different selective laser melting processes: Based on the selective laser melting process and typical characteristic structure parameters determined in step 1, prepare typical characteristic structures with different structural forming angles, structural scales, and structural types under different powder states, process parameters, and scanning strategy conditions;

[0008] Powder particles are adhered to the original surface of the typical characteristic structure formed by the laser selective melting;

[0009] Step 3. Measure the original surface quality of the typical characteristic structure with sticky powder: Use a roughness measuring device to characterize the original surface morphology of the typical characteristic structure with sticky powder, and obtain the original measured surface roughness of the typical characteristic structure with sticky powder by measurement. The original measured surface roughness includes the original measured maximum height roughness Rz(AP) and the original measured arithmetic average roughness Ra(AP). Rz(AP) and Ra(AP) reflect the original surface quality.

[0010] Step 4: Obtain the actual surface quality of the typical characteristic structure for removing surface powder. The process is as follows:

[0011] Step 401: Remove original surface powder from the typical characteristic structure by sandblasting or ultrasonic cleaning, then characterize the actual surface morphology of the typical characteristic structure after the surface powder is removed using a roughness measurement device, and obtain the actual measured surface roughness of the typical characteristic structure after the surface powder is removed by measurement. The actual measured surface roughness includes the actual measured maximum height roughness Rz1(NP) and the actual measured arithmetic average roughness Ra1(NP);

[0012] Step 402: using a metallographic cross-section method or a CT method to obtain an actual surface profile curve of a typical characteristic structure for removing surface powder, wherein the surface profile curve ensures that there is no influence of surface powder;

[0013] Step 403: According to the formula Rz2(NP)=max(Z(x))-min(Z(x)) and The actual calculated surface roughness of the typical characteristic structure for removing surface powder is calculated respectively. The actual calculated surface roughness includes the actual calculated maximum height roughness Rz2(NP) and the actual calculated arithmetic average roughness Ra2(NP). Wherein, λ is the actual surface profile sampling length of the typical characteristic structure for removing surface powder, x is the abscissa of the actual surface profile sampling point, and Z(x) is the ordinate of the actual surface profile sampling point.

[0014] Step 404: The combination of the actual measured surface roughness and the actual calculated surface roughness reflects the actual surface quality of the typical characteristic structure after removing the surface powder;

[0015] Step 5. Establish a BP neural network prediction model: Use the laser selective melting forming process and typical characteristic structure parameters as the input layer nodes of the BP neural network prediction model, and use the original measured surface roughness of the typical characteristic structure, the actual measured surface roughness, and the actual calculated surface roughness as the output layer nodes of the BP neural network prediction model. Construct the relationship between the forming process and typical characteristic structure parameters - original surface quality - actual surface quality, and establish a BP neural network prediction model to realize the prediction of the surface quality of the formed part;

[0016] Step 6: Prediction of surface quality of laser selective melting parts. The process is as follows:

[0017] Step 601: Decompose and sort out several typical characteristic structures according to the structural characteristics of the selective laser melting formed part, extract the corresponding typical characteristic structure parameters, and then retrieve the selective laser melting forming process parameters of the formed part;

[0018] Step 602: Inputting the laser selective melting forming process parameters and the typical characteristic structure parameters into the BP neural network prediction model in step 5 to obtain the original measured surface roughness prediction value, the actual measured surface roughness prediction value, and the actual calculated surface roughness prediction value of each typical characteristic structure;

[0019] Step 603: Characterize the original surface morphology of the typical characteristic structures corresponding to the selective laser melting formed part using a roughness measurement device to obtain the original surface roughness measurement value of each typical characteristic structure;

[0020] Step 604: Compare the original measured surface roughness prediction value and the measured value of each typical characteristic structure to verify the accuracy of the BP neural network prediction model. Then, the surface quality of the laser selective melting formed part is predicted by using the actual measured surface roughness prediction value and the actual calculated surface roughness prediction value of each typical characteristic structure obtained in step 602.

[0021] Step 7. Surface quality evaluation of the selective laser melting formed part: The surface quality of the selective laser melting formed part predicted in step 604 is compared with the actual engineering application requirements of the formed part. When the actual surface quality of the formed part is not lower than the surface quality specified by the engineering application requirements, the surface quality of the selective laser melting formed part is qualified; otherwise, the surface quality of the selective laser melting formed part is unqualified.

[0022] The above-mentioned method for predicting and evaluating the surface quality of a part formed by selective laser melting is characterized in that the powder properties include powder particle size and powder morphology.

[0023] The above-mentioned method for predicting and evaluating the surface quality of laser selective melting formed parts is characterized in that the process parameters include laser mode, laser spot diameter, substrate preheating temperature, forming atmosphere, laser power, scanning speed, scanning spacing and powder layer thickness.

[0024] The above-mentioned surface quality prediction and evaluation method for laser selective melting formed parts is characterized in that: the scanning strategy includes internal filling scanning and contour scanning, and the internal filling scanning includes inter-layer rotation scanning based on intra-layer unidirectional scanning, reciprocating scanning, chessboard scanning, and remelting scanning.

[0025] The above-mentioned method for predicting and evaluating the surface quality of a selective laser melting formed part is characterized in that the structural forming angle range is 0° to 90°.

[0026] The above-mentioned method for predicting and evaluating the surface quality of a selective laser melting formed part is characterized in that the structural scale range is 0.05mm to 200mm.

[0027] The above-mentioned surface quality prediction and evaluation method for laser selective melting formed parts is characterized in that the structure types include cylindrical structures, cone structures, spherical structures, torus structures and prism structures.

[0028] The above-mentioned surface quality prediction and evaluation method for laser selective melting formed parts is characterized in that the roughness measurement equipment includes a contact roughness meter, an atomic force microscope, a laser confocal microscope, a laser interferometer and an optical profiler.

[0029] The above-mentioned method for predicting and evaluating the surface quality of a part formed by selective laser melting is characterized in that the actual surface profile sampling length λ is 0.1 mm to 50 mm.

[0030] The beneficial effect of the present invention is to propose a surface quality prediction and evaluation method for laser selective melting formed parts. The method introduces a step of obtaining the actual surface quality after removing the surface powder, eliminating the interference of the surface powder on the surface quality evaluation. At the same time, the method constructs the relationship between the laser selective melting forming process and the typical characteristic structural parameters-original surface quality-actual surface quality, establishes a BP neural network prediction model, and realizes the rapid prediction and accurate evaluation of the surface quality of the formed part without destroying the structure of the formed part. In addition, the method improves the accuracy and convenience of the surface quality prediction and evaluation of the laser selective melting formed parts, provides important guidance for the optimized design and high-quality manufacturing of the laser selective melting formed parts, and further promotes the development and application of laser selective melting technology in high-precision fields such as aerospace; at the same time, the method does not require additional complex surface quality testing links for formed parts, significantly improves the economic benefits of laser selective melting technology, simplifies the surface quality evaluation process of formed parts, shortens the evaluation cycle, and saves a lot of time costs and testing fees.

[0031] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of the method of the present invention.

[0033] Figure 2 This is a diagram showing the effect of the structural scale on the original surface quality of the cylindrical structure formed by laser selective melting in this embodiment.

[0034] Figure 3 This is a rendering of the effect of the structural scale on the actual surface quality of the cylindrical structure formed by laser selective melting in this embodiment. DETAILED DESCRIPTION

[0035] like Figure 1 As shown, a surface quality prediction and evaluation method for a selective laser melting formed part of the present invention comprises the following steps:

[0036] Step 1: Determine the selective laser melting forming process and typical characteristic structure parameters: the selective laser melting forming process parameters include powder characteristics, process parameters, and scanning strategy;

[0037] The typical characteristic structural parameters include structural forming angle, structural scale, and structural type;

[0038] Step 2: Prepare different typical characteristic structures using different selective laser melting processes: Based on the selective laser melting process and typical characteristic structure parameters determined in step 1, prepare typical characteristic structures with different structural forming angles, structural scales, and structural types under different powder states, process parameters, and scanning strategy conditions;

[0039] Powder particles are adhered to the original surface of the typical characteristic structure formed by the laser selective melting;

[0040] Step 3. Measure the original surface quality of the typical characteristic structure with sticky powder: Use a roughness measuring device to characterize the original surface morphology of the typical characteristic structure with sticky powder, and obtain the original measured surface roughness of the typical characteristic structure with sticky powder by measurement. The original measured surface roughness includes the original measured maximum height roughness Rz(AP) and the original measured arithmetic average roughness Ra(AP). Rz(AP) and Ra(AP) reflect the original surface quality.

[0041] Step 4: Obtain the actual surface quality of the typical characteristic structure for removing surface powder. The process is as follows:

[0042] Step 401: Remove original surface powder from the typical characteristic structure by sandblasting or ultrasonic cleaning, then characterize the actual surface morphology of the typical characteristic structure after the surface powder is removed using a roughness measurement device, and obtain the actual measured surface roughness of the typical characteristic structure after the surface powder is removed by measurement. The actual measured surface roughness includes the actual measured maximum height roughness Rz1(NP) and the actual measured arithmetic average roughness Ra1(NP);

[0043] Step 402: using a metallographic cross-section method or a CT method to obtain an actual surface profile curve of a typical characteristic structure for removing surface powder, wherein the surface profile curve ensures that there is no influence of surface powder;

[0044] Step 403: According to the formula Rz2(NP)=max(Z(x))-min(Z(x)) and The actual calculated surface roughness of the typical characteristic structure for removing surface powder is calculated respectively. The actual calculated surface roughness includes the actual calculated maximum height roughness Rz2(NP) and the actual calculated arithmetic average roughness Ra2(NP). Wherein, λ is the actual surface profile sampling length of the typical characteristic structure for removing surface powder, x is the abscissa of the actual surface profile sampling point, and Z(x) is the ordinate of the actual surface profile sampling point.

[0045] Step 404: The combination of the actual measured surface roughness and the actual calculated surface roughness reflects the actual surface quality of the typical characteristic structure after removing the surface powder;

[0046] Step 5. Establish a BP neural network prediction model: Use the laser selective melting forming process and typical characteristic structure parameters as the input layer nodes of the BP neural network prediction model, and use the original measured surface roughness of the typical characteristic structure, the actual measured surface roughness, and the actual calculated surface roughness as the output layer nodes of the BP neural network prediction model. Construct the relationship between the forming process and typical characteristic structure parameters - original surface quality - actual surface quality, and establish a BP neural network prediction model to realize the prediction of the surface quality of the formed part;

[0047] Step 6: Prediction of surface quality of laser selective melting parts. The process is as follows:

[0048] Step 601: Decompose and sort out several typical characteristic structures according to the structural characteristics of the selective laser melting formed part, extract the corresponding typical characteristic structure parameters, and then retrieve the selective laser melting forming process parameters of the formed part;

[0049] Step 602: Inputting the laser selective melting forming process parameters and the typical characteristic structure parameters into the BP neural network prediction model in step 5 to obtain the original measured surface roughness prediction value, the actual measured surface roughness prediction value, and the actual calculated surface roughness prediction value of each typical characteristic structure;

[0050] Step 603: Characterize the original surface morphology of the typical characteristic structures corresponding to the selective laser melting formed part using a roughness measurement device to obtain the original surface roughness measurement value of each typical characteristic structure;

[0051] Step 604: Compare the original measured surface roughness prediction value and the measured value of each typical characteristic structure to verify the accuracy of the BP neural network prediction model. Then, the surface quality of the laser selective melting formed part is predicted by using the actual measured surface roughness prediction value and the actual calculated surface roughness prediction value of each typical characteristic structure obtained in step 602.

[0052] Step 7. Surface quality evaluation of the selective laser melting formed part: The surface quality of the selective laser melting formed part predicted in step 604 is compared with the actual engineering application requirements of the formed part. When the actual surface quality of the formed part is not lower than the surface quality specified by the engineering application requirements, the surface quality of the selective laser melting formed part is qualified; otherwise, the surface quality of the selective laser melting formed part is unqualified.

[0053] In this embodiment, the powder properties include powder particle size and powder morphology.

[0054] In this embodiment, the process parameters include laser mode, laser spot diameter, substrate preheating temperature, forming atmosphere, laser power, scanning speed, scanning spacing and powder layer thickness.

[0055] In this embodiment, the scanning strategy includes inner filling scanning and contour scanning, and the inner filling scanning includes inter-layer rotation scanning based on intra-layer unidirectional scanning, reciprocating scanning, checkerboard scanning, and remelting scanning.

[0056] In this embodiment, the structural forming angle range is 0° to 90°.

[0057] In this embodiment, the structural scale range is 0.05 mm to 200 mm.

[0058] In this embodiment, the structure types include cylindrical structures, cone structures, spherical structures, torus structures and prism structures.

[0059] In this embodiment, the roughness measurement equipment includes a contact roughness meter, an atomic force microscope, a laser confocal microscope, a laser interferometer, and an optical profilometer.

[0060] In this embodiment, the actual surface profile sampling length λ is 0.1 mm to 50 mm.

[0061] When the present invention is used, TiB2 / Al-Si composite material cylindrical structures with different structural scales are prepared under the same laser selective melting forming process parameter conditions, wherein the length of the cylindrical structure is 10 mm, and the diameters of the cylindrical structures are 0.2 mm, 0.6 mm, 1.0 mm, 2.0 mm and 5.0 mm respectively.

[0062] Figure 2 The effect of structure size on the original surface quality of cylindrical structures formed by selective laser melting was investigated. First, a large amount of powder particles adhered to the original surfaces of all cylindrical structures, but the distribution and amount of powder adhered to the original surfaces of samples with different structure sizes did not change significantly. The original surface roughness of all cylindrical structures was then characterized using an optical profilometer. The results showed that the original surface roughness Rz(AP) corresponding to cylindrical structure diameters of 0.2 mm, 0.6 mm, 1.0 mm, 2.0 mm, and 5.0 mm were 114.4 μm, 96.4 μm, 83.4 μm, 77.8 μm, and 76.4 μm, respectively. The original surface roughness Ra(AP) corresponding to cylindrical structure diameters of 0.2 mm, 0.6 mm, 1.0 mm, 2.0 mm, and 5.0 mm were 21.2 μm, 16.4 μm, 13.4 μm, 12.3 μm, and 12.1 μm, respectively.

[0063] Figure 3It shows the influence of structure scale on the actual surface quality of cylindrical structures formed by selective laser melting. The actual surface profiles of all cylindrical structure sections were observed using an optical microscope, and the actual calculated surface roughness of the actual surface profile curves was characterized. The results show that after removing the influence of surface powder sticking, the actual calculated surface roughness of all cylindrical structures is significantly improved, and the difference in actual calculated surface roughness between cylindrical structures of different structural scales is even greater. The actual calculated surface roughness Rz2(AP) corresponding to cylindrical structure diameters of 0.2mm, 0.6mm, 1.0mm, 2.0mm and 5.0mm are 200.0μm, 145.3μm, 112.4μm, 109.8μm and 109.9μm, respectively. The actual calculated surface roughness Ra2(AP) corresponding to cylindrical structure diameters of 0.2mm, 0.6mm, 1.0mm, 2.0mm and 5.0mm are 35.4μm, 25.5μm, 18.2μm, 17.5μm and 17.6μm, respectively.

[0064] The above analysis shows that the original measured surface roughness with powder adhesion cannot accurately represent the actual surface quality, which poses a serious obstacle to the evaluation of the surface quality of laser selective melting parts. In fact, the actual surface quality of the formed part can be effectively characterized by the actual calculated surface roughness after removing the surface powder adhesion. In addition, based on the obtained original measured surface roughness and the actual calculated surface roughness, the relationship between the different structural scales of the laser selective melting cylindrical structure, the original surface quality, and the actual surface quality can be constructed. The corresponding BP neural network prediction model is established. Based on this, the surface quality prediction and evaluation of laser selective melting parts with cylindrical structural characteristics can be realized.

[0065] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for predicting and evaluating the surface quality of a part formed by selective laser melting, characterized in that: The method comprises the following steps: Step 1: Determine the selective laser melting forming process and typical characteristic structure parameters: the selective laser melting forming process parameters include powder characteristics, process parameters, and scanning strategy; The typical characteristic structural parameters include structural forming angle, structural scale, and structural type; Step 2: Prepare different typical characteristic structures using different selective laser melting processes: Based on the selective laser melting process and typical characteristic structure parameters determined in step 1, prepare typical characteristic structures with different structural forming angles, structural scales, and structural types under different powder states, process parameters, and scanning strategy conditions; Powder particles are adhered to the original surface of the typical characteristic structure formed by the laser selective melting; Step 3. Measure the original surface quality of the typical characteristic structure with sticky powder: Use a roughness measuring device to characterize the original surface morphology of the typical characteristic structure with sticky powder, and obtain the original measured surface roughness of the typical characteristic structure with sticky powder by measurement. The original measured surface roughness includes the original measured maximum height roughness Rz(AP) and the original measured arithmetic average roughness Ra(AP). Rz(AP) and Ra(AP) reflect the original surface quality. Step 4: Obtain the actual surface quality of the typical characteristic structure for removing surface powder. The process is as follows: Step 401: Remove original surface powder from the typical characteristic structure by sandblasting or ultrasonic cleaning, then characterize the actual surface morphology of the typical characteristic structure after the surface powder is removed using a roughness measurement device, and obtain the actual measured surface roughness of the typical characteristic structure after the surface powder is removed by measurement. The actual measured surface roughness includes the actual measured maximum height roughness Rz1(NP) and the actual measured arithmetic average roughness Ra1(NP); Step 402: using a metallographic cross-section method or a CT method to obtain an actual surface profile curve of a typical characteristic structure for removing surface powder, wherein the surface profile curve ensures that there is no influence of surface powder; Step 403: According to the formula Rz2(NP)=max(Z(x))-min(Z(x)) and The actual calculated surface roughness of the typical characteristic structure for removing surface powder is calculated respectively. The actual calculated surface roughness includes the actual calculated maximum height roughness Rz2(NP) and the actual calculated arithmetic average roughness Ra2(NP). Wherein, λ is the actual surface profile sampling length of the typical characteristic structure for removing surface powder, x is the abscissa of the actual surface profile sampling point, and Z(x) is the ordinate of the actual surface profile sampling point. Step 404: The combination of the actual measured surface roughness and the actual calculated surface roughness reflects the actual surface quality of the typical characteristic structure after removing the surface powder; Step 5. Establish a BP neural network prediction model: Use the laser selective melting forming process and typical characteristic structure parameters as the input layer nodes of the BP neural network prediction model, and use the original measured surface roughness of the typical characteristic structure, the actual measured surface roughness, and the actual calculated surface roughness as the output layer nodes of the BP neural network prediction model. Construct the relationship between the forming process and typical characteristic structure parameters - original surface quality - actual surface quality, and establish a BP neural network prediction model to realize the prediction of the surface quality of the formed part; Step 6: Prediction of surface quality of laser selective melting parts. The process is as follows: Step 601: Decompose and sort out several typical characteristic structures according to the structural characteristics of the selective laser melting formed part, extract the corresponding typical characteristic structure parameters, and then retrieve the selective laser melting forming process parameters of the formed part; Step 602: Inputting the laser selective melting forming process parameters and the typical characteristic structure parameters into the BP neural network prediction model in step 5 to obtain the original measured surface roughness prediction value, the actual measured surface roughness prediction value, and the actual calculated surface roughness prediction value of each typical characteristic structure; Step 603: Characterize the original surface morphology of the typical characteristic structures corresponding to the selective laser melting formed part using a roughness measurement device to obtain the original surface roughness measurement value of each typical characteristic structure; Step 604: Compare the original measured surface roughness prediction value and the measured value of each typical characteristic structure to verify the accuracy of the BP neural network prediction model. Then, the surface quality of the laser selective melting formed part is predicted by using the actual measured surface roughness prediction value and the actual calculated surface roughness prediction value of each typical characteristic structure obtained in step 602. Step 7. Surface quality evaluation of the selective laser melting formed part: The surface quality of the selective laser melting formed part predicted in step 604 is compared with the actual engineering application requirements of the formed part. When the actual surface quality of the formed part is not lower than the surface quality specified by the engineering application requirements, the surface quality of the selective laser melting formed part is qualified; otherwise, the surface quality of the selective laser melting formed part is unqualified.

2. A method for predicting and evaluating surface quality of a selective laser melting part according to claim 1, characterized in that: The powder characteristics include powder particle size and powder morphology.

3. The surface quality prediction and evaluation method for selective laser melting parts according to claim 1, characterized in that: The process parameters include laser mode, laser spot diameter, substrate preheating temperature, forming atmosphere, laser power, scanning speed, scanning spacing and powder layer thickness.

4. The method for predicting and evaluating surface quality of a selective laser melting part according to claim 1, wherein: The scanning strategies include inner filling scanning and contour scanning, wherein the inner filling scanning includes inter-layer rotation scanning based on intra-layer unidirectional scanning, reciprocating scanning, checkerboard scanning, and remelting scanning.

5. The method for predicting and evaluating surface quality of a selective laser melting formed part according to claim 1, characterized in that: The structural forming angle ranges from 0° to 90°.

6. The method for predicting and evaluating the surface quality of a selective laser melting formed part according to claim 1, characterized in that: The structural scale range is 0.05mm to 200mm.

7. The method for predicting and evaluating the surface quality of a selective laser melting part according to claim 1, characterized in that: The structure types include cylindrical structures, cone structures, spherical structures, torus structures and prism structures.

8. The method for predicting and evaluating surface quality of a selective laser melting part according to claim 1, characterized in that: The roughness measuring equipment includes a contact roughness meter, an atomic force microscope, a laser confocal microscope, a laser interferometer and an optical profiler.

9. The method for predicting and evaluating the surface quality of a selective laser melting part according to claim 1, characterized in that: The actual surface profile sampling length λ is 0.1 mm to 50 mm.

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