Selective laser melting forming optimization method based on response surface method

Abstract

The invention discloses a selective laser melting forming optimization method based on a response surface method. The selective laser melting forming optimization method comprises the steps that S1, material parameters are determined through a calibration object printing experiment and simulation combined method; s2, finite element simulation software is utilized, a response surface experiment design method is adopted to establish a simulation experiment with the deformation amount and the equivalent stress as responses and with the laser power, the scanning speed and the powder laying thickness as factors, and a regression model among the laser power, the scanning speed, the powder laying thickness, the deformation amount and the equivalent stress is fitted through the response surface method; s3, obtaining an optimal experiment combination under various factor conditions according to the regression model, and determining optimal process parameters of laser power, scanning speed and powder laying thickness; and S4, performing actual printing by using the optimal process parameters, scanning a printing result, comparing the printing result with the model, and verifying the feasibility of the method. According to the method, the optimal forming process parameters of the material can be screened out more simply and more efficiently, and therefore the precision and quality of a 3D printing forming part are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to a laser selective melting forming optimization method based on response surface methodology. Background Art

[0002] Selective Laser Forming (SLM) technology is an additive manufacturing technology based on the principle of layer-by-layer accumulation. It uses infrared laser as a heat source to sinter powder materials and form three-dimensional parts in a layer-by-layer manner. Products manufactured by selective laser melting (SLM) have the advantages of complex formable shapes, wide application, and good mechanical properties of molded parts. The manufactured products have high design freedom, short production cycle, and can ensure structural and functional integration. The parts formed by selective laser melting have high density, good precision, and stable and excellent overall comprehensive performance. Based on the above advantages, selective laser melting has been widely used in aerospace, automotive industry, biomedicine, and national defense.

[0003] The process parameters in the laser selective melting process have a direct impact on the forming quality. Therefore, domestic and foreign scholars have done a lot of research on the influence of SLM process parameters on forming quality performance. The process parameters that the research focuses on are mostly laser power, powder layer thickness, scanning speed, scanning spacing, scanning method, etc. For example, if the laser power is too large, the scanning speed is too low, the temperature is too high, and the powder melts too quickly, spheroidization will occur; if the laser power is too small, the energy is low, and the powder melts slowly, stratification and porosity will occur. Even different laser power and scanning speed may affect the plasticity and hardness of the molded part.

[0004] Therefore, the quality of the selective laser melting sample is strongly dependent on the laser process parameters, and the effects of the process parameters on the molded parts are not independent of each other. The effects of various process factors on the performance show complex correlations. Only when various process factors cooperate with each other can a product of good quality be formed. Therefore, optimizing the process parameters is of great practical significance for improving the quality of molded parts. The current traditional method for improving the quality of molded parts is to conduct a large number of experimental tests through artificial experiments (existing artificial process parameter optimization methods) to find the optimal process parameter combination (process parameters may include laser power, powder layer thickness, scanning speed). However, this method requires the combination verification of parameters one by one, which has problems such as high material loss and long cycle, and will consume a lot of manpower and material resources in actual engineering. Summary of the invention

[0005] The purpose of the present invention is to propose a laser selective melting forming optimization method based on response surface methodology to solve the problems of high cost and low efficiency faced by the existing process parameter optimization methods, in view of the traditional method of gradually verifying the molding results of various process parameters through a large number of manual experiments to screen out the optimal process parameter combination, which has the problems of high material loss (high cost) and long cycle (low efficiency).

[0006] To achieve the above object, the present invention is implemented by the following technical solutions:

[0007] The present invention provides a laser selective melting forming optimization method based on response surface methodology, the method comprising the following steps:

[0008] S1. Material parameter calibration: Determine the material parameters through a combination of printing experiments and simulations of calibration objects, providing a basis for subsequent calculations;

[0009] S2. Establishment of regression model: Using finite element simulation software, the response surface experimental design method is used to establish a simulation experiment with deformation and equivalent stress as responses and laser power, scanning speed, and powder thickness as factors, and the regression model between laser power, scanning speed, powder thickness, deformation and equivalent stress is fitted by the response surface method;

[0010] S3. Determine the optimal molding conditions: According to the above regression model, the optimal experimental parameter combination under various factors is obtained to determine the optimal process parameters of laser power, scanning speed and powder thickness;

[0011] S4. Feasibility verification: Use the optimal process parameters to conduct actual printing experiments, and compare the printing result scan with the model to verify the feasibility of the method.

[0012] Furthermore, a laser selective melting forming optimization method based on response surface methodology is provided: step S1, printing the material to be calibrated into a calibration object, and then measuring the maximum deformation value of the calibration object and the material simulation data, and importing the measured maximum deformation value into the simulation software to calibrate the material parameters.

[0013] Further, a laser selective melting forming optimization method based on response surface methodology: the material used in step S1 is AlSi 10 Mg aluminum alloy powder.

[0014] Furthermore, a laser selective melting forming optimization method based on response surface methodology is provided: the laser power in step S2 is 200-500 W, the scanning speed is 400-1000 mm / s, and the powder thickness is 0.02-0.1 mm.

[0015] Furthermore, a laser selective melting forming optimization method based on response surface methodology: the finite element simulation software used in step S2 is Simufact Additive.

[0016] Furthermore, a laser selective melting forming optimization method based on response surface methodology: the regression model in step S2 adopts a second-order polynomial model.

[0017] Specifically, for the powder molding materials actually used, the material manufacturers generally provide a process parameter package applicable to the material's molding process (including parameters such as laser power, scanning speed, and powder thickness applicable to the material), and the quality of the selective laser melting sample strongly depends on the various process parameters in the molding process. The process parameters in the SLM forming process have a direct impact on the molding quality. Only when various process factors are coordinated with each other can a high-quality product be formed. Therefore, in order to obtain the best molding quality, it is necessary to screen out the optimal process parameter combination within the process parameter range given by the manufacturer to meet the optimal molding process conditions of the material and obtain the best molding quality.

[0018] At present, the traditional way to obtain the optimal molding process parameters of materials is to gradually adopt different parameter combinations to conduct actual manual experimental tests within a given material parameter range, that is, to find the optimal process parameter combination through manual one-by-one testing. This method has problems such as high material consumption (high cost) and long cycle (low efficiency), so it will consume a lot of manpower and material resources in actual engineering.

[0019] In response to the problems existing in the above-mentioned method of manually screening the optimal process parameter combination of materials, the present invention has developed a laser selective melting forming optimization method based on the response surface method. With the widespread application of computer simulation technology in additive manufacturing and component optimization, the use of computer simulation technology can better guide the optimization of process parameters in forming technology. The present invention simulates the influence of forming process parameters through numerical calculation methods, thereby achieving the purpose of determining the optimal process parameters of SLM technology. The method provided by the present invention does not require manual step-by-step experimental verification of various parameter combinations, thereby avoiding the problems of high material loss and long cycle in the manual experimental verification method (i.e., the manual process parameter optimization method), and solves the problems of high cost and low efficiency of the existing manual process parameter optimization method.

[0020] The laser selective melting forming optimization method based on the response surface methodology of the present invention is equivalent to a laser selective melting forming precision control method, and its purpose is to screen out the process parameter combination that is most suitable for the forming material, so as to obtain the best quality molded parts.

[0021] Beneficial effects of the present invention:

[0022] (1) The laser selective melting forming optimization method based on the response surface method provided by the present invention does not require manual experimental testing under various process parameter combinations, so the material loss is low, the trial and error cost is reduced, and the efficiency is higher. For a given material, the method of the present invention can more simply and efficiently screen out the optimal molding process parameters of the material, thereby significantly improving the accuracy and quality of the 3D printed parts.

[0023] (2) The laser selective melting forming optimization method based on the response surface method provided by the present invention is based on numerical calculation, which can greatly reduce the trial and error cost in engineering and improve work efficiency and economic benefits. The present invention simulates the influence of the forming process parameters of the material by numerical calculation, thereby achieving the purpose of determining the optimal process parameters of the SLM technology. The method of the present invention does not require manual experimental verification of various process parameter combinations, thereby avoiding the problems of high material loss and long cycle in traditional process parameter optimization methods, and solves the problems of high cost and low efficiency of traditional manual parameter optimization methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 The calibration object model used in the material parameter calibration of Example 1 of the present invention;

[0026] Figure 2 This is a schematic diagram of the model structure of the heat sink of a drone printed in Example 1 of the present invention;

[0027] Figure 3 This is a comparison diagram of the three-dimensional scanning images of the heat sink model printed in Example 1 of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Example 1

[0030] This embodiment 1 provides a laser selective melting forming optimization method based on response surface methodology, which includes the following specific steps:

[0031] S1. Material parameter calibration: Determine the material parameters through a combination of printing experiments and simulations of calibration objects, providing a basis for subsequent calculations;

[0032] Specifically, the material used in step S1 is AlSi 10 Mg aluminum alloy powder, the calibration parameter is the linear expansion coefficient, AlSi 10 Mg aluminum alloy powder is printed into a jagged calibration object (such as Figure 1 After printing, perform wire cutting on the calibration object to cut off all small saw teeth, and then measure the maximum deformation value of the actual printed calibration object and the calibration object simulated with the material; in order to improve the accuracy and reduce the influence of measurement errors, three calibration objects can be printed at the same time, and the average value is taken after the measurement is completed, and then imported into Simufact Additive software for material parameter calibration; in order to reduce the influence of creep deformation, the measurement should be completed as soon as possible after the cutting is completed; after the calibration is completed, the material parameters are saved in the software and called in the next calculation;

[0033] S2. Establishment of regression model: Using finite element simulation software Simufact Additive, response surface experimental design method was used to establish simulation experiments with deformation and equivalent stress as responses, and laser power, scanning speed, and powder thickness as factors (laser power was 200-500W, scanning speed was 400-1000mm / s, and powder thickness was 0.02-0.1mm), and the regression model between laser power, scanning speed, powder thickness and deformation and equivalent stress was fitted by response surface method.

[0034] Specifically, in step S2, according to the given AlSi 10 The process parameter range applicable to Mg aluminum alloy powder sets 21 groups of parameter combinations, of which 5 groups of process parameters are the same, to show whether the prediction lack of fit is significant. The deformation and equivalent stress data are obtained by simulating the above 21 groups of parameters using the finite element simulation software Simufact Additive, as shown in Table 1 below:

[0035] ① First, analyze the deformation. After response surface analysis, the binary regression equation of deformation, laser power, scanning speed, and powder thickness is obtained, as shown in the following formula:

[0036] Deformation = -0.113947 + 0.0018624 * A + -0.00027174 * B + 6.05201 * C + 1.39733e-06 * A * B + 0.00671966 * A * C + -0.00448047 * B * C + -3.91718e-06 * A 2 +-1.76559e-08*B 2 +-43.125*C 2 ; In the formula, A represents laser power, B represents scanning speed, and C represents powder thickness;

[0037] The variance analysis of the binary regression equation of surface deformation is shown in Table 2. From Table 2, it can be seen that the lack of fit term P = 0.4960> 0.05, indicating that it is not significant, indicating that the binary regression equation has a good fit, and the correlation coefficient R 2 =0.9124, which means that the actual value and the predicted value are highly correlated. Therefore, the model can clearly reflect the relationship between laser power, scanning speed, powder layer thickness and surface deformation and optimize the best process conditions. According to the P value, A laser power, interaction term AB, A 2 and B 2 The effect on deformation is extremely significant;

[0038] ② The binary regression equation of equivalent stress, laser power, scanning speed and powder layer thickness was obtained through response surface analysis, as shown in the following formula:

[0039] Equivalent stress = 382.122 + -0.109233*A + 0.0469119*B + 210.674*C + 0.000718595*A*B + 5.30337*A*C + -2.70527*B*C + -0.000704634*A 2 +-0.000169832*B 2 +-8196.16*C 2 ; In the formula, A represents laser power, B represents scanning speed, and C represents powder thickness;

[0040] The variance analysis of the equivalent stress binary regression equation is shown in Table 3. From Table 3, we can see that the model fit R 2 and corrected goodness of fit R 2 ADj All of them are close to 1, indicating that the model is accurate and can be used to predict the effects of laser power, scanning speed, and powder layer thickness on equivalent stress. The lack of fit term P = 0.4537 > 0.05, indicating that it is not significant, indicating that the binary regression equation has a good fit.

[0041] S3. Determine the optimal molding conditions: According to the above regression model, the best experimental combination under various factors is obtained to determine the optimal process parameters of laser power, scanning speed and powder thickness;

[0042] Specifically, the two response targets are optimized centrally, and the targets of deformation and equivalent stress are set to be minimum in the optimization options. The numerical optimization algorithm is used. The specific process is as follows:

[0043] (1) Let X be a vector, where X i (i=1, 2, ..., n) represents the design variables in the optimization space (a subset of the design space);

[0044] (2) Let Y j , U j , L j are the 1st to mth responses, respectively, which contain upper and / or lower bounds as constraints;

[0045] (3) Let Y(X) be the target to be responded to, and the purpose is to optimize; then F(X) = Y(X) represents minimization, and F(X) = -Y(X) represents maximization, and the constraints are defined as a series of discontinuous functions; make

[0046] This results in a system of m constraints, which can be solved as an unconstrained problem using the penalty function method: Minimize[f(X)+p∑jGj(x)]

[0047] Where p is the penalty parameter when j=1~m>0; j ranges from 1 to m and increases by 100 times with each iteration. The number of iterations is limited to 15, so the penalty coefficient is 1030 maximum. The goal of the penalty function is to optimize and provide a reachable vertex when starting from an unwanted position;

[0048] Starting with small values, the penalty function pattern search in Nelder-Mead multidimensional (Numerical Recipes in Pascal by William H. Press et al., p. 326) converges to a stationary point or design space boundary; restricting the design space to 10 at the design boundary by evaluating F(X) to +10, restarting the search around the initial convergence point with a larger penalty function; convergence is achieved or the objective function change is less than 10 when the distance is moved -6 ;

[0049] After 100 simulations by Design-Expert, the optimal parameters were finally obtained: laser power 203.497W, scanning speed 952.170mm / s, powder thickness 0.071mm;

[0050] S4. Feasibility verification: Use the optimal process parameters to conduct actual printing experiments, and compare the printing result scan with the model to verify the feasibility of the method;

[0051] Specifically, the optimal process parameters obtained by the above settings are used to print a UAV heat sink with a powder thickness of 0.071 mm, a laser power of 203.497 W, and a scanning speed of 952.170 mm / s. The model structure of the heat sink is as follows: Figure 2 As shown, after printing, the heat sink is removed from the substrate, the support is removed, and the printed heat sink is scanned using a 3D scanner. The 3D model of the heat sink generated by the 3D scanner is compared with the original model. Figure 3 ;Depend on Figure 3 It can be seen that the maximum surface deformation of the heat sink obtained using this process is about 0.3 mm. It is found that the surface variation of the model is within an acceptable range, thus verifying the feasibility of this method.

[0052] Table 1 shows 21 groups of calculation parameters and simulation results

[0053]

[0054]

[0055] Table 2 shows the variance analysis of the deformation regression model.

[0056]

[0057]

[0058] Table 3 shows the variance analysis of the equivalent stress regression model

[0059]

[0060]

[0061] The laser selective melting forming optimization method based on response surface methodology provided by the present invention is a laser selective melting forming precision control method, the purpose of which is to screen out the process parameter combination that is most suitable for the forming material, so as to obtain the best quality formed parts.

[0062] The laser selective melting forming optimization method based on the response surface method provided by the present invention is based on numerical calculation, can greatly reduce the trial and error cost in engineering, and improve work efficiency and economic benefits; the present invention simulates the influence of the forming process parameters of the material by numerical calculation method, so as to achieve the purpose of screening out the best process parameters of SLM technology. The method of the present invention does not require manual experimental verification of various process parameter combinations, thereby avoiding the problems of high material loss and long cycle caused by a large number of experimental verification processes, and solves the problems of high cost and low efficiency of traditional manual parameter optimization methods.

[0063] The above are preferred embodiments of the present invention and are only used to explain the present invention, not to limit the present invention. Any obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A laser selective melting forming optimization method based on response surface methodology, characterized in that: The method comprises the following steps: S1. Material parameter calibration: Determine the material parameters through a combination of printing experiments and simulations of calibration objects, providing a basis for subsequent calculations; S2. Establishment of regression model: Using finite element simulation software, the response surface experimental design method is used to establish a simulation experiment with deformation and equivalent stress as responses and laser power, scanning speed, and powder thickness as factors, and the regression model between laser power, scanning speed, powder thickness, deformation and equivalent stress is fitted by the response surface method; S3. Determine the optimal molding conditions: According to the above regression model, the optimal experimental parameter combination under various factors is obtained to determine the optimal process parameters of laser power, scanning speed and powder thickness; S4. Feasibility verification: Use the optimal process parameters to conduct actual printing experiments, and compare the printing result scan with the model to verify the feasibility of the method.

2. The laser selective melting forming optimization method based on response surface methodology according to claim 1, characterized in that: Step S1, printing the material to be calibrated into a calibration object, then measuring the maximum deformation value of the calibration object and the material simulation data, and importing the measured maximum deformation value into the simulation software to calibrate the material parameters.

3. The laser selective melting forming optimization method based on response surface methodology according to claim 1 or 2, characterized in that: The material used in step S1 is AlSi 10 Mg aluminum alloy powder.

4. The laser selective melting forming optimization method based on response surface methodology according to claim 3 is characterized in that: In step S2, the laser power is 200-500 W, the scanning speed is 400-1000 mm / s, and the powder thickness is 0.02-0.1 mm.

5. The laser selective melting forming optimization method based on response surface methodology according to claim 1, characterized in that: The finite element simulation software used in step S2 is Simufact Additive.

6. The laser selective melting forming optimization method based on response surface methodology according to claim 1, characterized in that: In step S2, the regression model adopts a second-order polynomial model.

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