High-power laser directional energy deposition process parameter multi-objective optimization method considering grain size, deposition efficiency and energy consumption

Abstract

The invention relates to a high-power laser directional energy deposition process parameter multi-objective optimization method considering grain size, deposition efficiency and energy consumption, and belongs to the field of laser additive manufacturing. Comprising the following steps that an energy consumption monitoring platform for the high-power laser directional energy deposition process is built; analyzing a time model and an energy consumption model of the deposition process according to the target size of deposition and the deposition strategy; a high-power laser directional energy deposition process parameter multi-objective optimization model is constructed; acquiring model parameters based on the experimental data; solving the optimization model based on an ICMA algorithm; optimal process parameters are obtained on the basis of an IOMV-Topsis evaluation method; tC4 titanium alloy is used as a material, optimal process parameters are adopted for a deposition experiment, the structure and mechanical properties are analyzed, and the effectiveness and feasibility of the model and the method are verified. The average grain size is small, the deposition efficiency is improved, the energy consumption is reduced, and high-performance, high-efficiency and green manufacturing in the laser directional energy deposition process is realized.

Description

Technical Field

[0001] The present invention relates to a multi-objective optimization method for high-power laser directional energy deposition process parameters taking into account grain size, deposition efficiency and energy consumption, and specifically to a mapping model construction method between energy consumption, deposition efficiency, average grain size and process parameters in a high-power laser directional energy deposition process and a multi-objective process parameter optimization method, belonging to the field of laser additive manufacturing. Background Art

[0002] Laser directed energy deposition (LDED) technology has a wide range of design flexibility and process adaptability, and is very suitable for the manufacture of large aerospace structures with rapid strength customization and integrated structure and function. In view of the low forming efficiency of laser directed energy deposition, increasing the laser power and powder feed rate are the main methods to improve the deposition efficiency. However, under high-power conditions, the shape and quality control of the process are more complex and difficult due to high energy input and more drastic evolution of the molten pool. In addition, the long deposition time and the low efficiency of converting electrical energy into laser energy lead to a large amount of energy consumption in the deposition process. This obviously makes it difficult to achieve the manufacturing goals of high performance, high efficiency and low energy consumption, resulting in insufficient performance of the deposited parts, inefficient deposition process and huge energy consumption. Summary of the invention

[0003] In view of the problems of low deposition efficiency, high energy consumption and poor performance of laser directed energy deposition technology, the present invention proposes a multi-objective optimization method for high-power laser directed energy deposition process parameters taking into account grain size, deposition efficiency and energy consumption. Aiming at the high-power laser directed energy deposition process, an energy consumption model of the deposition process is established. A multivariate high-order nonlinear model is used to establish a mapping model between process parameters and average grain size, melt height, melt width and powder utilization rate to solve the problem of low prediction accuracy of the second-order model. On this basis, a multi-objective process parameter optimization model for high-power laser directed energy deposition is established, the ICMA algorithm is selected to solve the model, and the IOMV-TOPSIS method is used to select the optimal solution. Finally, the microstructure and mechanical properties of the deposited state are analyzed through titanium alloy deposition experiments and tensile tests to verify the effectiveness and accuracy of the model.

[0004] The present invention provides a multi-objective optimization method for high-power laser directional energy deposition process parameters taking into account grain size, deposition efficiency, and energy consumption, comprising the following steps:

[0005] S1. Build an energy consumption monitoring platform for high-power laser directional energy deposition process;

[0006] S2. Analyze the time model and energy consumption model of the deposition process according to the target size and deposition strategy;

[0007] S3. Construct a multi-objective optimization model for high-power laser directed energy deposition process parameters;

[0008] S4. Obtain model parameters based on experimental data;

[0009] S5. Solve the optimization model based on ICMA algorithm;

[0010] S6. Obtain the optimal process parameters based on the IOMV-TOPSIS evaluation method;

[0011] S7. Using TC4 titanium alloy as the material, the deposition experiment was carried out with the optimal process parameters, the microstructure and mechanical properties were analyzed, and the effectiveness and feasibility of the proposed model and method were verified.

[0012] Preferably, step S2 includes the following sub-steps:

[0013] Specifically, in step S2, the time model and energy consumption model of the deposition process are modeled as follows:

[0014] S21. Divide the high-power laser directed energy deposition system into five subsystems, namely, laser, robot subsystem, powder feeder, cooling subsystem, and auxiliary subsystem.

[0015] S22. Determine the laser working time T according to the overlap rate ɑ between tracks and the overlap rate β between layers, as well as the deposition strategy. L , as follows:

[0016]

[0017] Wherein, n1 and n2 are the number of deposited passes and the number of deposited layers, respectively; L, W, and H are the target dimensions of the length, width, and height of the deposited layer, in mm; w and h are the melt width and melt height, in mm; V is the scanning speed, in mm / s; α is the overlap rate between passes, and β is the overlap rate between layers.

[0018] S23, according to the robot's return speed V r , the robot's return time T r It can be expressed as:

[0019]

[0020] S24, the total running time of the robot T R for:

[0021] T R =T L +T r +T0

[0022] Among them, T0 is the time it takes for the robot to run from the safety point to the deposition starting point.

[0023] S25, considering the powder feeder delay powder feeding time T pw After that, the running time of the powder feeder is T P for:

[0024] T P =T L +T r +T Pw

[0025] S26, according to the laser input power P and output power P L , calculate the cooling system operating time T C for:

[0026]

[0027] Among them, ρ is the coolant density, v is the coolant flow rate, c is the coolant specific heat capacity, and △T is the cooling system operation time.

[0028] S27, in the clear interlayer cooling time T LC Based on this, the total deposition time T is:

[0029] T=T L +T r +T0+T Pw +T LC

[0030] S28. Establish an energy consumption model E for high-power laser directional energy deposition process, as follows:

[0031]

[0032] Among them, P L , P Ls Respectively represent the output power of the laser in the running and waiting states, unit: W; P R , P Rs Respectively represent the output power of the robot in running and waiting state, unit: W; P P , P Ps Respectively represent the output power of the powder feeder in the powder feeding and waiting states, unit: W; P C , P Cs Respectively represent the output power of the cooling system in operation and waiting state, unit: W; P AUX Represents the output power of the auxiliary system in operation, unit: W.

[0033] Preferably, step S3 includes the following sub-steps:

[0034] Specifically, in step S3, sub-step S31 of step S3 specifies the optimization target, and includes the following sub-steps:

[0035] S311, average grain size D, unit: mm, calculated as follows:

[0036]

[0037] Where n is the number of grains counted, d i is the grain size, i is the grain number;

[0038] S312, deposition efficiency ηD, unit: mm3 / s, calculated as follows;

[0039]

[0040] S313, energy consumption E.

[0041] Specifically, the step S32 specifies the constraint conditions, including the following sub-steps:

[0042] S321, aspect ratio The calculation is as follows:

[0043] φ=w / h

[0044] S322, powder utilization rate η P , calculated as follows:

[0045]

[0046] Wherein, M1 is the mass of the substrate before deposition, M2 is the mass of the substrate after deposition, and M is the powder feeding amount during the deposition process, unit: g;

[0047] Specifically, the step S33 establishes a multi-objective process parameter optimization model, which is as follows:

[0048] F(P,V,F)=(min{D},{E},max{η D})

[0049]

[0050] Wherein, P is the laser power, unit: W; V is the scanning speed, unit: mm / s; F is the powder disk speed, unit: r / min.

[0051] Preferably, step S4 includes the following sub-steps:

[0052] Specifically, the step S41 of acquiring specific parameters of the energy consumption model includes the following sub-steps:

[0053] S411 uses a single-factor experiment to clarify the power data of the laser, robot system, powder feeder, and cooling system in operation and standby states;

[0054] S412 performs linear fitting on the power characteristic data to obtain the relationship between process parameters and laser and robot system

[0055] The mapping model between the system and the powder feeder operating power;

[0056] S413 determines the power characteristics of each subsystem in the high-power laser directional energy deposition process.

[0057] Specifically, the step S42 obtains specific parameters of the objective function model, including the following sub-steps:

[0058] S421 uses response surface experiments to obtain the melt height, melt width, average grain size, and powder utilization rate of different process parameters;

[0059] S422 uses a multivariate high-order nonlinear model to perform fitting regression on melt height, melt width, average grain size, and powder utilization to determine the mapping model of the objective function.

[0060] Preferably, step S5 includes the following sub-steps:

[0061] Specifically, in step S5, solving the optimization model based on the ICMA algorithm includes the following sub-steps:

[0062] S51, using different algorithms to try to solve the optimization model;

[0063] S52, using Runtime, CPF, GD, HV, IGD, Spacing, Spread indicators, analyze the algorithm performance from three perspectives: solution speed, convergence speed, and population diversity;

[0064] S53, construct an evaluation matrix with the characteristic values ​​of performance indicators of different algorithms, rank the algorithm performance based on the TOPSIS method, and clearly identify the ICMA algorithm as the optimal algorithm;

[0065] S54, performing signal-to-noise ratio analysis on algorithm parameters of the ICMA algorithm to identify optimal algorithm parameters;

[0066] S55. Use optimal algorithm parameters to solve the model and output the optimal solution set.

[0067] Preferably, step S6 includes the following sub-steps:

[0068] Specifically, step S6 obtains the optimal process parameters based on the IOMV-TOPSIS evaluation method, including the following sub-steps:

[0069] S61. According to the optimization goal, establish a secondary goal evaluation system, as follows:

[0070] Table 1. Index evaluation system

[0071]

[0072] S62, scoring is performed using a percentage system to obtain an evaluation matrix;

[0073] S63, calculating the independent weight coefficient of each optimization target;

[0074] S64, normalizing the optimal solution set to obtain a normalized matrix, and weighting each indicator to obtain a TOPSIS evaluation matrix;

[0075] S65. Calculate the positive and negative ideal solutions of the TOPSIS evaluation matrix;

[0076] S66. Perform TOPSIS sorting on the TOPSIS evaluation matrix to obtain the optimal solution.

[0077] Specifically, the step S63 calculates the independent weight coefficient of each optimization target, including the following sub-steps:

[0078] S631, evaluation matrix normalization

[0079] S632. Calculate the multiple correlation coefficient R of each secondary indicator j ;

[0080] S633. Calculate the weight r of each secondary indicator j ; The calculation method is as follows:

[0081]

[0082] Among them, m is the number of secondary indicators of the indicator, and j is the indicator serial number.

[0083] Preferably, the step S7 is characterized in that step S7 uses TC4 titanium alloy as material, conducts deposition experiments with the optimal process parameters obtained in step S6, analyzes the structure and mechanical properties, and calculates deposition efficiency and energy consumption to verify the effectiveness and feasibility of the method proposed in this case.

[0084] In view of the problems of low efficiency, high energy consumption and poor performance of traditional laser directional energy deposition, the present invention adopts a high-power laser directional energy deposition process, constructs a multi-objective process parameter optimization model that takes into account grain size, deposition efficiency and energy consumption, and solves the model based on the ICMA algorithm to achieve the optimization goals of high performance, high efficiency and low energy consumption. Through verification experiments, it was verified that the prediction error of the model was within 4%. By analyzing the signal-to-noise ratio between process parameters and grain size, energy consumption and deposition efficiency, the influence of process parameters on different targets was clarified. The optimal process parameters were obtained based on the IOMV-Topsis evaluation method, and its structure and mechanical properties were analyzed through deposition experiments to verify the feasibility and effectiveness of the method in this case. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1(a)-1(c) The SEM image and particle size distribution of TC4 powder used in the present invention.

[0086] Figure 2 An energy consumption monitoring platform for the high-power laser directional energy deposition process built for the present invention.

[0087] Figure 3(a)-3(e) It is a schematic diagram of the power state of the high-power laser directional energy deposition process in the present invention.

[0088] Figure 4(1)-4(3) It is the fitting result of the process parameters and the power characteristics of each subsystem in the present invention.

[0089] Figure 5 Comparison between the actual value and the predicted value of the energy consumption model of the present invention.

[0090] Figure 6a-6d The actual values ​​and predicted values ​​of the process parameters of the present invention and the melt height, melt width, average grain size, and powder utilization model are compared.

[0091] Figure 7(a) and 7(b) The Pareto front of the present invention is compared with the Pareto solutions of different algorithms.

[0092] Figures 8(a)-8(f) The figure is a comparison of performance curves of different algorithms of the present invention.

[0093] Figure 9(a)-9(h) This is a performance comparison and consistency analysis of different algorithms of the present invention.

[0094] Figure 10(a)-10(f) It is the signal-to-noise ratio analysis result of the ICMA algorithm parameters of the present invention.

[0095] Fig.11 The Pareto solution set obtained by solving the ICMA algorithm of the present invention.

[0096] Figure 12(a) and 12(b) It is a metallographic structure comparison diagram of the present invention.

[0097] Figure 13(a) and 13(b) This is a comparison diagram of the grain size distribution of the present invention.

[0098] Figure 14(a) and 14(b) It is a microhardness comparison diagram of the present invention.

[0099] Figure 15(a)-15(i) This is a fracture morphology diagram of the tensile specimen of the present invention. DETAILED DESCRIPTION

[0100] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0101] The present invention proposes a multi-objective process parameter optimization model for high-power laser directional energy deposition process. By building an energy consumption monitoring platform for high-power laser directional deposition process, the energy consumption characteristics of each subsystem are obtained, and an energy consumption model of the deposition process is established. Using TC4 titanium alloy as the material, a response surface experiment is adopted to establish a mapping relationship between process parameters and melt height, melt width, average grain size, and powder utilization rate, and then a process parameter optimization model is established with grain size, deposition efficiency, and energy consumption as considerations, and with aspect ratio and powder utilization rate as constraints. By comparing the solution performance of different algorithms, the ICMA algorithm is selected to solve the model, and reasonable parameters are selected for the algorithm through signal-to-noise ratio analysis. Finally, the optimal process parameters are used to carry out deposition experiments, analyze the deposition efficiency, energy consumption, and the organization and performance of the deposited state, and verify the effectiveness and feasibility of the model.

[0102] The present invention provides a multi-objective optimization method for high-power laser directional energy deposition process parameters taking into account grain size, deposition efficiency, and energy consumption, comprising the following steps:

[0103] S1. Build an energy consumption monitoring platform for high-power laser directional energy deposition process;

[0104] S2. Analyze the time model and energy consumption model of the deposition process according to the target size and deposition strategy;

[0105] S3. Construct a multi-objective optimization model for high-power laser directed energy deposition process parameters;

[0106] S4. Obtain model parameters based on experimental data;

[0107] S5. Selection and performance comparison of optimization algorithms;

[0108] S6. Obtain the optimal process parameters based on the IOMV-Topsis evaluation method;

[0109] S7. Carry out deposition experiments using optimal process parameters, analyze the microstructure and mechanical properties, and verify the effectiveness and feasibility of the proposed model and method.

[0110] The step S2 analyzes the time model and energy consumption model of the deposition process according to the target size of the deposition and the deposition strategy, and includes the following sub-steps:

[0111] S21. Divide the high-power laser directed energy deposition system into five subsystems, namely, laser, robot subsystem, powder feeder, cooling subsystem, and auxiliary subsystem.

[0112] S22. Determine the laser working time T according to the overlap rate ɑ between tracks and the overlap rate β between layers, as well as the deposition strategy. L , as follows:

[0113]

[0114] Wherein, n1 and n2 are the number of passes and the number of deposition layers for each layer, respectively; L, W, and H are the target dimensions of the length, width, and height of the deposition layer, in mm; w and h are the melt width and melt height, in mm, respectively; and V is the scanning speed, in mm / s.

[0115] S23, according to the robot's return speed V r , the robot's return time T r It can be expressed as:

[0116]

[0117] S24, the total running time of the robot T R for:

[0118] T R =T L +T r +T0

[0119] Among them, T0 is the time it takes for the robot to run from the safety point to the deposition starting point.

[0120] S25, considering the powder feeder delay powder feeding time T pw After that, the running time of the powder feeder is T P for:

[0121] T P =T L +T r +T Pw

[0122] S26, according to the laser input power P and output power PL , calculate the cooling system operating time T C for:

[0123]

[0124] S27, in the clear interlayer cooling time T LC Based on this, the total deposition time T is:

[0125] T=T L +T r +T0+T Pw +T LC

[0126] S28. Establish the energy consumption model E of high-power laser directional energy deposition process. The model error is as follows: Figure 5 As shown, the details are as follows:

[0127]

[0128] Among them, P L , P Ls Respectively represent the output power of the laser in the running and waiting states, unit: W; P R , P Rs Respectively represent the output power of the robot in running and waiting state, unit: W; P P , P Ps Respectively represent the output power of the powder feeder in the powder feeding and waiting states, unit: W; P C , P Cs Respectively represent the output power of the cooling system in operation and waiting state, unit: W; P AUX Represents the output power of the auxiliary system in operation, unit: W.

[0129] The step S3 includes the following sub-steps

[0130] In the step S3, sub-step S31 specifies the optimization target, including the following sub-steps:

[0131] S311, average grain size D, unit: mm, calculated as follows:

[0132]

[0133] Where n is the number of grains counted, d i is the grain size, i is the grain number;

[0134] S312, deposition efficiency η D , unit: mm 3 / s, calculated as follows;

[0135]

[0136] S313, energy consumption E;

[0137] In step S3, sub-step S32 specifies the constraint conditions, including the following sub-steps:

[0138] S321, aspect ratio The calculation is as follows:

[0139] φ=w / h

[0140] S322, powder utilization rate η P , calculated as follows:

[0141]

[0142] Wherein, M1 is the mass of the substrate before deposition, M2 is the mass of the substrate after deposition, and M is the powder feeding amount during the deposition process, unit: g;

[0143] S33. Establish a multi-objective process parameter optimization model, as follows:

[0144] F(P,V,F)=(min{D},{E},max{η D})

[0145]

[0146] Wherein, P is the laser power, unit: W; V is the scanning speed, unit: mm / s; F is the powder disk speed, unit: r / min.

[0147] The step S4 includes the following sub-steps

[0148] In the step S4, sub-step S41 obtains specific parameters of the energy consumption model, including the following sub-steps:

[0149] S411. Use a single factor experiment to clarify the power data of the laser, robot system, powder feeder, and cooling system in operation and standby states, as shown in Table 2;

[0150] Table 2. Single factor experimental scheme and results of laser, robot system, and powder feeder power characteristics

[0151]

[0152]

[0153] S412, perform linear fitting on the power characteristic data to obtain a mapping model between the process parameters and the operating power of the laser, robot system, and powder feeder, such as Figure 4(1)-4(3) As shown;

[0154] S413, determine the power characteristics of each subsystem in the high-power laser directional energy deposition process, such as Figure 3(a)-3(e) , as shown in Table 3.

[0155] Table 3 Power characteristics of each subsystem

[0156]

[0157] In the step S4, sub-step S42 obtains specific parameters of the objective function model, including the following sub-steps:

[0158] S421, using response surface experiments to obtain the melt height, melt width, and average grain size of different process parameters

[0159] Inch, powder utilization, the experimental results are shown in Table 4;

[0160] Table 4. Response surface test results statistics

[0161]

[0162] S422, using multivariate high-order nonlinear model, fitting regression is performed on melt height, melt width, average grain size, and powder utilization rate to determine the mapping model of the objective function. The model error is as follows: Figure 6a-6d The specific fitting model is as follows:

[0163] (1) Melting height

[0164] h=5512127-5681.714×P-281×V+505×F+2.194707×P 2 +9.5×V 2

[0165] +30.32×F 2 -0.05×P×F-11.88×V×F-3.76348×10 -4 ×P 3

[0166] -0.099×V 3 -2.6×F 3 +0.001×P×V×F+2.41737×10 -8 ×P 4

[0167] (2) Melt width

[0168] w=10530197-10770.5×P-220×V-219×F

[0169] -4.12774×P 2 +9.4×V2 +50×F 2 -0.028×P×VV×F

[0170] -7.01992×10 -4 ×P 3 -0.1×V 3 -3.4×F 3 +4.44706×10 -8 ×P 4

[0171] (3) Average grain size

[0172] D=-974986+1008.296×P+24×V+283×F-0.391309×P 2

[0173] -26×V 2 -0.0071×F 2 -0.040868×P×V+6.75194×10 -5 ×P 3

[0174] -1.4×10 -3 ×V 3 +1.3×F 3 +1.5×10 -4 ×P×V×F-4.368×10 -9 ×P 4

[0175] (4) Powder utilization rate

[0176] η P =458954-472.129×P-14.29×V-55.56×F

[0177] +0.1819705×P 2 +0.369×V 2 +9121×F 2 +9.5×10 -4 ×P×V

[0178] +0.687×V×F-3.11291×10 -5 ×P 3 -4.53×10 -3 ×V 3

[0179] -0.5×F 3 -1.511×10 -4 ×P×V×F+1.99427×10 -9 ×V4

[0180] The step S5 includes the following sub-steps:

[0181] S51. Use different algorithms to try to solve the optimization model and obtain the Pareto solution sets of different algorithms, such as Figure 7(a) and 7(b) As shown;

[0182] S52, using Runtime, CPF, GD, HV, IGD, Spacing, Spread indicators, analyze the algorithm performance from three perspectives: solution speed, convergence speed, and population diversity. Figures 8(a)-8(f) , as shown in Figures (9a)-9(h);

[0183] S53, the performance index characteristic values ​​of different algorithms are used to construct an evaluation matrix, and the algorithm performance is ranked based on the TOPSIS method. The results are shown in Table 5, which clearly shows that the ICMA algorithm is the optimal algorithm;

[0184] Table 5. Algorithm performance ranking based on the Topsis method

[0185]

[0186] S54, performing signal-to-noise ratio analysis on algorithm parameters of the ICMA algorithm, such as Figure 10(a)-10(f) As shown, the optimal algorithm parameters are clear, that is, the maximum number of iterations is 15,000 and the population size is 300;

[0187] S55, solve the model using the optimal algorithm parameters and output the optimal solution set, such as Fig.11 shown.

[0188] The step S6 includes the following sub-steps:

[0189] S61. According to the optimization goal, establish a secondary goal evaluation system, as follows:

[0190] Table 1. Index evaluation system

[0191]

[0192] S62, scoring is performed using a percentage system, and the results are shown in Table 6 to obtain an evaluation matrix;

[0193] Table 6. Index evaluation system and evaluation results

[0194]

[0195]

[0196] S63, calculating the independent weight coefficient of each optimization target to obtain the weights of different indicators;

[0197] S64. Normalize the optimal solution set to obtain a normalized matrix, and assign weights to each indicator to obtain a TOPSIS evaluation matrix.

[0198] S65. Calculate the positive and negative ideal solutions of the TOPSIS evaluation matrix;

[0199] S66. Perform TOPSIS sorting on the TOPSIS evaluation matrix to obtain the optimal solution. The optimal process parameters are: F(3780,24.05,7)=(245.0987,33.63,0.63).

[0200] The calculation of the independent weight coefficient of each optimization target in step S63 includes the following sub-steps:

[0201] S631, evaluation matrix normalization

[0202] S632. Calculate the multiple correlation coefficient R of each secondary indicator j ;

[0203] S633. Calculate the weight r of each secondary indicator j ; The calculation method is as follows:

[0204]

[0205] Among them, m is the number of secondary indicators of the indicator, and j is the indicator serial number;

[0206] S634, summing up the secondary indicators included in each primary indicator to obtain the primary indicator weight. According to the parameters in Table 6, the weights of the three indicators of average grain size, deposition efficiency, and energy consumption are calculated to be 0.5132, 0.2131, and 0.2738, respectively.

[0207] In step S7, the deposition experiment is carried out using the optimal process parameters to analyze the structure and mechanical properties, and verify the effectiveness and feasibility of the proposed model and method. Under the optimal process parameter conditions, the deposition efficiency is 22.35 mm 3 / s, energy consumption is 0.41kWh, and the average grain size of the deposited state is 242.5μm. Its specific characteristics are as follows:

[0208] (1) TC4 alloy was used as the material for high-power laser directed energy deposition experiments. The material composition is shown in Table 7, and the powder morphology and particle size distribution are shown in Table 7. Figure 1(a)-1(c) shown.

[0209] Table 7. Chemical composition of matrix and powder (wt.%)

[0210]

[0211] (2) Comparison of different optimization objectives.

[0212] By comparing the data in Table 8, it can be seen that compared with the empirical scheme, the average grain size and energy consumption were reduced by 25.08% and 42.73% after the multi-objective process parameter optimization, and the deposition efficiency was increased by 47.24%. Compared with Scheme 1, the energy density of the process parameters obtained by multi-objective optimization is higher, resulting in a certain increase in the average grain size; but since the melt height and melt width of Scheme 4 are significantly greater than those of Scheme 1, the deposition efficiency of Scheme 4 is significantly higher than that of Scheme 1, and Scheme 4 also obtains lower energy consumption. Similarly, Scheme 2 pursues higher deposition efficiency, and its deposition layer has a higher melt height and a wider melt width, but due to the further increase in energy density, the average grain size is significantly increased, and the energy consumption level is also relatively high. Schemes 3 and 4 are relatively close in each target, but from the weights calculated from the evaluation results obtained in Table 6, it can be seen that compared with deposition efficiency and energy consumption, the average grain size index representing performance is more important, and Scheme 4 is slightly better than Scheme 3 in this index. In summary, compared with the single-objective process parameter optimization scheme and the empirical scheme, the multi-objective process parameter optimization scheme can improve the deposition efficiency by up to 39.95% and reduce the energy consumption by up to 36.36% under the condition of a small average grain size, significantly improving the deposition efficiency and reducing the deposition energy consumption.

[0213] Table 8. Comparison of results for different optimization objectives

[0214]

[0215] (3) Organization analysis. By observing the deposited structures of the optimal process parameters and the empirical process parameters, the differences between the two structures are analyzed, the grain size distribution is statistically analyzed, and the average grain size is calculated. Figure 12(a) and 12(b) , Figure 13(a) and 13(b) shown.

[0216] (4) Mechanical properties analysis. Microhardness specimens and tensile specimens were prepared by sampling the deposited state with the optimal process parameters and the empirical process parameters, and the microhardness and tensile properties of the deposited state were tested. Figure 14(a) and 14(b) The tensile strength of the deposited state is shown in Table 9 and Table 10, and the fracture morphology of the tensile specimen is shown in Figure 15(a)-15(i) shown.

[0217] Table 9. Comparison of tensile properties under different process parameters

[0218]

[0219] Table 10 Tensile properties in the scanning direction of the optimal process parameters

[0220]

[0221] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable technicians in this field to understand the content of the present invention and implement it. It cannot be used to limit the protection scope of the present invention. All equivalent changes or modifications made according to the essence of the present invention belong to the protection scope of the present invention.

Claims

1. A multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption, characterized in that: The steps include: S1. Build an energy consumption monitoring platform for high-power laser directional energy deposition process; S2. Analyze the time model and energy consumption model of the deposition process according to the target size and deposition strategy; S3. Construct a multi-objective optimization model for high-power laser directed energy deposition process parameters; S4. Obtain model parameters based on experimental data; S5. Solve the optimization model based on ICMA algorithm; S6. Obtain the optimal process parameters based on the IOMV-TOPSIS evaluation method; S7. Using TC4 titanium alloy as the material, the deposition experiment was carried out with the optimal process parameters, the microstructure and mechanical properties were analyzed, and the effectiveness and feasibility of the proposed model and method were verified.

2. The multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption as claimed in claim 1, characterized in that: Step S2 includes the following sub-steps: S21. Divide the high-power laser directed energy deposition system into five subsystems, namely, laser, robot subsystem, powder feeder, cooling subsystem, and auxiliary subsystem. S22. Determine the laser working time T according to the overlap rate ɑ between tracks and the overlap rate β between layers, as well as the deposition strategy. L , as follows: Wherein, n1 and n2 are the number of deposited passes and the number of deposited layers, respectively; L, W, and H are the target dimensions of the length, width, and height of the deposited layer, in mm; w and h are the melt width and melt height, in mm; V is the scanning speed, in mm / s; α is the overlap rate between passes, and β is the overlap rate between layers. S23, according to the robot's return speed V r , the robot's return time T r It can be expressed as: S24, the total running time of the robot T R for: T R =T L +T r +T0 Among them, T0 is the time it takes for the robot to run from the safety point to the deposition starting point, S25, considering the powder feeder delay powder feeding time T pw After that, the running time of the powder feeder is T P for: T P =T L +T r +T Pw S26, according to the laser input power P and output power P L , calculate the cooling system operating time T C for: Among them, ρ is the coolant density, v is the coolant flow rate, c is the coolant specific heat capacity, and △T is the cooling system operation time. S27, in the clear interlayer cooling time T LC Based on this, the total deposition time T is: T=T L +T r +T0+T Pw +T LC S28. Establish an energy consumption model E for high-power laser directional energy deposition process, as follows: E=(P L +P R +P P +P AUX +P Cs )×T L +(P Ls +P R +P P +P AUX +P Cs )×T r +(P Ls +P R +P Ps +P AUX +P Cs )×T0+(P Ls +P Rs +P P +P AUX +P Cs )×T Pw +(P Ls +P Rs +P Ps +P AUX +P Cs )×T LC +(P C -P Cs )×T C Among them, P L , P Ls Respectively represent the output power of the laser in the running and waiting states, unit: W; P R , P Rs Respectively represent the output power of the robot in running and waiting state, unit: W; P P , P Ps Respectively represent the output power of the powder feeder in the powder feeding and waiting states, unit: W; P C , P Cs Respectively represent the output power of the cooling system in operation and waiting state, unit: W; P AUX Represents the output power of the auxiliary system in operation, unit: W.

3. The multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption as claimed in claim 1 or 2, characterized in that: Step S3 includes the following sub-steps: S31. Clarify the optimization goals, as follows: S311, average grain size D, unit: mm, calculated as follows: Where n is the number of grains counted, d i is the grain size, i is the grain number; S312, deposition efficiency η D , unit: mm 3 / s, calculated as follows; S313, energy consumption E; S32. Clarify the constraints, as follows: S321, aspect ratio The calculation is as follows: φ=wh S322, powder utilization rate η P , calculated as follows: Wherein, M1 is the mass of the substrate before deposition, M2 is the mass of the substrate after deposition, and M is the powder feeding amount during the deposition process, unit: g; S33, establish a multi-objective process parameter optimization model, as follows: F(P,V,F)=(min{D},{E},max{η) D }) Wherein, P is the laser power, unit: W; V is the scanning speed, unit: mm / s; F is the powder disk speed, unit: r / min.

4. The multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption as claimed in claim 3, characterized in that: Step S4 includes the following sub-steps: S41, obtaining specific parameters of the energy consumption model, as follows: S411. Use single factor experiments to clarify the power data of the laser, robot system, powder feeder, and cooling system in operation and standby states; S412, performing linear fitting on the power characteristic data to obtain a mapping model between the process parameters and the operating powers of the laser, the robot system, and the powder feeder; S413, determine the power characteristics of each subsystem in the high-power laser directional energy deposition process, S42, obtaining specific parameters of the objective function model, as follows: S421. Using response surface experiments, the melt height, melt width, average grain size, and powder utilization rate of different process parameters were obtained; S422. Use a multivariate high-order nonlinear model to perform fitting regression on melt height, melt width, average grain size, and powder utilization rate to determine the mapping model of the objective function.

5. The multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption as claimed in claim 4, wherein step S5 comprises the following sub-steps: S51, using different algorithms to try to solve the optimization model; S52, using Runtime, CPF, GD, HV, IGD, Spacing, Spread indicators, analyze the algorithm performance from three perspectives: solution speed, convergence speed, and population diversity; S53, construct an evaluation matrix with the characteristic values ​​of performance indicators of different algorithms, rank the algorithm performance based on the TOPSIS method, and clearly identify the ICMA algorithm as the optimal algorithm; S54, performing signal-to-noise ratio analysis on algorithm parameters of the ICMA algorithm to identify optimal algorithm parameters; S55. Use optimal algorithm parameters to solve the model and output the optimal solution set.

6. The multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption as claimed in claim 5, wherein step S6 comprises the following sub-steps: S61. According to the optimization goal, establish a secondary goal evaluation system, as follows: Table 1. Index evaluation system S62, use the percentage system to score and obtain the evaluation matrix. S63. Calculate the independent weight coefficient of each optimization target to obtain the weights of different indicators, as follows: S631, evaluation matrix normalization S632. Calculate the multiple correlation coefficient R of each secondary indicator j ; S633. Calculate the weight r of each secondary indicator j ; The calculation method is as follows: in, m is the number of secondary indicators of the indicator, and j is the indicator serial number; S634. Sum the secondary indicators contained in each primary indicator to obtain the primary indicator weight. S64, normalize the optimal solution set to obtain a normalized matrix, and assign weights to each indicator to obtain a TOPSIS evaluation matrix. S65. Calculate the positive and negative ideal solutions of the TOPSIS evaluation matrix. S66. Perform TOPSIS sorting on the TOPSIS evaluation matrix to obtain the optimal solution.

7. The multi-objective optimization method for high-power laser directed energy deposition process parameters considering grain size, deposition efficiency, and energy consumption as claimed in claim 6, characterized in that: In step S7, TC4 titanium alloy is used as the material, and the optimal process parameters obtained in step S6 are used to conduct deposition experiments, analyze the microstructure and mechanical properties, and calculate the deposition efficiency and energy consumption to verify the effectiveness and feasibility of the method proposed in this case.

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