Multi-objective optimization method for technological parameters of low-carbon and high-efficiency high-power laser directional energy deposition process

Abstract

The invention relates to a multi-objective optimization method for technological parameters of a low-carbon and efficient high-power laser directional energy deposition process, and belongs to the field of green intelligent manufacturing and laser additive manufacturing. The design method comprises the following steps: constructing a high-power laser directional energy deposition process system carbon emission comprehensive model; building a high-power laser directional energy deposition process energy consumption real-time monitoring platform; carrying out a high-power laser directional energy deposition test to obtain related test parameters of the carbon emission model; with carbon emission, the powder utilization rate and the deposition efficiency as targets, a multi-target optimization model of the technological parameters of the high-power laser directional energy deposition process is established; solving the model based on an improved NSGA-III algorithm of a self-adaptive dynamic adjustment variation crossover and reference point selection mechanism to obtain an optimal process parameter combination; the invention aims to realize the manufacturing targets of high quality, high efficiency and low carbon, and provides powerful support for quality improvement, cost reduction and efficiency improvement of high-end equipment large-scale structural members.

Description

Technical Field

[0001] The present invention relates to a multi-objective optimization method for process parameters of a high-power laser directional energy deposition process for low carbon and high efficiency. The present invention relates to a multi-objective optimization method for process parameters of a high-power laser directional energy deposition process for low carbon emissions and high deposition efficiency. The present invention specifically relates to a multi-objective optimization method for process parameters of a high-power laser directional energy deposition process for large-scale key and complex structural parts such as aerospace for low carbon and high efficiency. The method belongs to the technical field of green intelligent manufacturing and laser additive manufacturing. Background Art

[0002] Laser directed energy deposition has been widely used due to its high forming efficiency and degree of freedom, and strong feasibility of multi-process composite. In particular, in recent years, breakthroughs have been made in large-scale key and complex structural parts of high-end equipment such as aviation and aerospace. Under the dual carbon strategy, facing the process requirements of structural function integration and rapid customization, high-performance and high-efficiency laser directed energy deposition technology has become a key technology that countries around the world are competing to develop. At present, high laser power, high scanning speed, and high powder feeding are one of the main ways to improve the efficiency of laser additive manufacturing, but the shape control of its process is more complex and difficult, the efficiency of converting electrical energy into laser energy is low, and the energy utilization rate is low, which requires more electrical energy, resulting in more carbon emissions. Therefore, exploring the carbon emission mechanism and process optimization method of high-power laser directed energy deposition process is the key to achieving low carbon and high efficiency of large-scale key and complex structural parts of high-end equipment in my country. Summary of the invention

[0003] Due to the complex process of high-power laser directed energy deposition, many carbon emission sources, unclear carbon emission mechanism and characteristics under the coordinated work of multiple systems and the coupling of multiple process parameters, and the lack of adaptability of existing carbon emission models in low-power laser directed energy deposition processes, the present invention designs a process parameter optimization method for high-power laser directed energy deposition processes with low carbon and high efficiency, which effectively improves manufacturing efficiency, reduces carbon emissions, and reduces production costs.

[0004] In response to the above problems, the present invention deeply analyzes the carbon emission mechanism and characteristics of the high-power laser directed energy deposition process, and establishes a carbon emission model for high-power laser directed energy deposition. Taking carbon emissions, powder utilization and deposition efficiency as optimization targets, and aspect ratio and powder utilization as constraints, a multi-objective optimization model for high-power laser directed energy deposition process parameters is established. An improved NSGA-Ⅲ model solving algorithm is proposed to obtain the optimal combination of process parameters. Finally, a high-power laser additive experiment of TC4 titanium alloy material is carried out using the KUKA robot laser directed energy deposition equipment to verify the effectiveness and accuracy of the model and algorithm.

[0005] The present invention provides a multi-objective optimization method for process parameters of a high-power laser directional energy deposition process for low carbon and high efficiency, and the specific method comprises the following steps:

[0006] S1. Construct a comprehensive model of carbon emissions from high-power laser directed energy deposition process systems.

[0007] S2. Build a real-time monitoring platform for energy consumption in the high-power laser directional energy deposition process.

[0008] S3. Conduct high-power laser directed energy deposition experiments and apply linear regression fitting methods to obtain relevant experimental parameters of the carbon emission model.

[0009] S4. Construct a multi-objective optimization model for process parameters of high-power laser directed energy deposition process with carbon emission, powder utilization and deposition efficiency as the targets.

[0010] S5. An improved NSGA-Ⅲ algorithm based on an adaptive dynamic adjustment of variation crossover and reference point selection mechanism is used to solve the process parameter optimization model of high-power laser directional energy deposition process.

[0011] S6. Use the optimized process parameters to carry out high-power laser directed energy deposition experiments on TC4 titanium alloy materials to verify the effectiveness and accuracy of the model and algorithm.

[0012] Preferably, step S1 in the present invention specifically comprises the following sub-steps:

[0013] S11. Establish the time function relationship under the working state of high-power laser directional energy deposition process.

[0014] S111, the total time function of high power laser directional energy deposition process is:

[0015] T=T rt +T rm +T st +T pt

[0016] Where, T is the total deposition time; T rt is the deposition auxiliary time; T rm Working time for the robot; T st is the laser interlayer interval time; T pt Delay the powder feeding time for the powder feeder.

[0017] S112, the robot working time function is:

[0018] T rm =T dm +T b +(K1+K2)×N0

[0019]

[0020]

[0021] Where, T rm is the robot feeding time; T dm is the deposition process time; T b is the single-pass return time; L is the length of the sedimentary layer; V o is the single-channel return speed; V f is the scanning speed; N1 is the number of deposition paths; N2 is the number of deposition layers; N0 is the number of times the robot rises and falls; K1 and K2 are the fixed time constants consumed by the robot's descent and ascent. The working time function of the water cooler in operation is:

[0022]

[0023] Where P lin is the laser input power; P lm is the laser working power; v c is the coolant flow rate; ρ is the coolant density; c is the coolant specific heat capacity, and ΔT is the coolant temperature difference.

[0024] S12. Establish a total carbon emission model for the high-power laser directed energy deposition process system.

[0025] S121. Establish a carbon emission model based on the operating status of the laser during the deposition process:

[0026] C L =(P lt ×(TT dm )+P lm ×T dm +P ls ×T ds )×C e

[0027] In the formula, C L is the carbon emission of the laser; P lt , P ls They are respectively the laser subsystem standby power and the inter-layer stop power.

[0028] S122. As a separate working system, the working state of the water chiller has a certain period and has no obvious relationship with the working state of the laser. Therefore, its carbon emission model is:

[0029] C C =(P ct ×(TT cm )+P cm ×T cm )×C e

[0030] In the formula, C C is the carbon emission of the water cooler; P ct , P cm They are the standby power and working power of the cooling subsystem respectively.

[0031] S123. As an important subsystem of the deposition process, the robot feeding system can establish a carbon emission model based on the changes in working status:

[0032] C R =(P Rt ×(TT rm )+P Rm ×(T rm -T b )+P Rb ×T b )×C e

[0033] In the formula, C R Feeding carbon emissions to robots; R-st , P Rm , P Rb They are the standby power, working power and return power of the robot feeding subsystem respectively.

[0034] S124. Construct a carbon emission model based on the working principle of the powder feeder and to ensure the accuracy of the carbon emission model and reduce the complexity of the model:

[0035] C p =(P p-st ×(T-(T dm +T Pt ))+P pm ×(T dm +T Pt ))×C e

[0036] In the formula, C P is the carbon emission of powder feeder; P p-st , P pm They are the standby power and working power of the powder feeding subsystem respectively.

[0037] S125. Constructing carbon emission model of auxiliary system:

[0038] C Aux =C e ×P A ×T

[0039] In the formula, C Aux is the comprehensive carbon emission of auxiliary systems; P A It is the comprehensive working power of the auxiliary system.

[0040] S126. The carbon emission models of metal powder and protective gas materials during the sealing and deposition process are:

[0041] C M =M m ×C m

[0042] C Ar =C g ×Q×(T dm +T pt )

[0043] In the formula, C M Carbon emission of metal powder; C Ar To protect gas carbon emissions; M m is the mass of metal powder not used in the cladding process; Q is the flow rate of protective gas; T dm The time the shielding gas is turned on.

[0044] S127. Through the analysis of carbon emissions of each system under different working conditions, it can be seen that the total carbon emission model of the high-power laser directed energy deposition process system is:

[0045]

[0046] Specifically, according to the step S2 of the present invention, the energy consumption of the high-power laser directional energy deposition process is monitored in real time using a Topray TP600 power analyzer.

[0047] Preferably, according to the present invention, step S3 specifically comprises the following sub-steps:

[0048] S31. The data of the laser, robot and powder feeder are fitted by the data linear regression fitting method, and the mathematical function relationship between the laser input power and the output power is obtained as follows: lm =515.9+3.087P lin ; The mathematical function relationship between the robot scanning speed and output power is: P Rm =448.4+0.8479V f ; The mathematical function relationship between the powder feeding speed and output power of the powder feeder is: P pm =25.58+9.246V p .

[0049] S32. Obtain output power values ​​of the water cooler and auxiliary equipment under working conditions through multiple actual measurements.

[0050] S33. Obtaining the carbon emission model parameters of TC4 titanium alloy metal powder during deposition:

[0051]

[0052] In the formula, C M is the carbon emission of metal powder; η is the powder utilization rate; V P is the powder feeding speed.

[0053] Preferably, step S4 in the present invention specifically comprises the following sub-steps:

[0054] S41. Considering that the processing cost is directly related to the powder utilization rate, the ratio of the cladding layer mass per unit time to the powder delivery mass per unit time is expressed as:

[0055]

[0056] Where, η is the powder utilization rate; V P is the powder feeding speed; M1 and M2 are the substrate masses before and after deposition.

[0057] S42. Since the single-pass deposition efficiency directly determines the multi-pass and multi-layer deposition efficiency, the deposition layer volume per unit time is used as the evaluation standard of deposition efficiency:

[0058]

[0059] In the formula, is the deposition efficiency; H is the height of a single-channel deposition layer; W is the width of a single-channel deposition layer.

[0060] S43. The quality evaluation standard of the sediment layer can be tested by the aspect ratio to see whether it meets the ideal requirements:

[0061]

[0062] Wherein, γ represents the aspect ratio; W represents the width of a single deposition track; and H represents the height of a single deposition track.

[0063] S44. Powder utilization rate η and deposition efficiency are obtained by data fitting The fitting mathematical function relationship is as follows: The powder uses the mathematical function relationship:

[0064]

[0065] Mathematical function relationship of deposition efficiency:

[0066]

[0067] S45. The aspect ratio γ is obtained by data fitting as a mathematical function relationship, and is used as a constraint condition:

[0068]

[0069] S46. Construct a multi-objective optimization model for process parameters:

[0070]

[0071] Preferably, step S5 in the present invention specifically comprises the following sub-steps:

[0072] S51. Solution of improved NSGA-Ⅲ algorithm model based on adaptive dynamic adjustment of mutation crossover and reference point selection mechanism.

[0073] S52. Select the Pareto optimal solution based on the CRITIC-TOPSIS analysis method.

[0074] The present invention mainly aims at the problems of large part size, low deposition efficiency, high carbon emission and insufficient performance faced by laser directed energy deposition in the processing of large key and complex structural parts in the aerospace field. By deeply analyzing the carbon emission mechanism and characteristics of each subsystem during the deposition process, a comprehensive carbon emission model of the high-power laser directed energy deposition process system is constructed, and a multi-objective optimization model of high-power laser directed energy deposition process parameters with carbon emission, powder utilization and deposition efficiency as optimization targets is established. An improved NSGA-Ⅲ algorithm is proposed to solve the model and obtain the optimal process parameter combination. Secondly, experiments were carried out on TC4 titanium alloy materials to verify the effectiveness and accuracy of the model and algorithm. The study of low-carbon modeling and process parameter optimization of high-power laser directed energy deposition process is the key to realizing the low-carbon and efficient manufacturing of large key and complex structural parts of high-end equipment in my country using high-power laser directed energy deposition technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a power characteristic curve diagram of the high-power laser directional energy deposition process of the present invention.

[0076] Figure 2 This is a system boundary diagram of the high-power laser directional energy deposition process of the present invention.

[0077] Figure 3 This is an energy consumption monitoring diagram of the high-power laser directional energy deposition process of the present invention.

[0078] Figure 4 This is a power variation diagram of the laser working state of the present invention.

[0079] Figure 5 The power variation diagram of the water-cooling machine of the present invention at laser powers of 3700W, 3900W and 4100W is shown.

[0080] Figure 6 This is a diagram showing the power variation during the feeding process of the robot of the present invention.

[0081] Figure 7 The power variation diagram of the powder feeder in the present invention in the working state

[0082] Figure 8 It is a production flow chart of the present invention.

[0083] Fig. 9 This is a comparison chart before and after the improvement of the NSGA-Ⅲ algorithm of the present invention. DETAILED DESCRIPTION

[0084] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The embodiments are used to explain and illustrate the present invention and are not used to limit the scope of the present invention.

[0085] The present invention proposes a multi-objective optimization design method for process parameters of high-power laser directional energy deposition process for low carbon and high efficiency. In order to reduce carbon emissions in the deposition process, reduce processing and manufacturing costs, and improve the efficiency of the deposition process, a multi-objective optimization model for process parameters of high-power laser directional energy deposition is constructed, and the improved NSGA-Ⅲ algorithm is used to solve the optimization model and select the optimal process parameter combination, in order to achieve low carbon and high efficiency manufacturing of high-power laser directional energy deposition process of large key complex structural parts.

[0086] The overall technical implementation scheme of the present invention is a multi-objective optimization design method for process parameters of high-power laser directional energy deposition for low carbon and high efficiency. The specific implementation process includes the following steps:

[0087] S1. Construct a comprehensive model of carbon emissions from high-power laser directed energy deposition process systems.

[0088] S2. Build a real-time monitoring platform for energy consumption in the high-power laser directional energy deposition process.

[0089] S3. Conduct high-power laser directed energy deposition experiments and apply linear regression fitting methods to obtain relevant experimental parameters of the carbon emission model.

[0090] S4. Construct a multi-objective optimization model for process parameters of high-power laser directed energy deposition process with carbon emission, powder utilization and deposition efficiency as the targets while ensuring the quality of the deposited layer.

[0091] S5. An improved NSGA-Ⅲ algorithm based on an adaptive dynamic adjustment of variation crossover and reference point selection mechanism is used to solve the process parameter optimization model of high-power laser directional energy deposition process.

[0092] S6. Use the optimized process parameters to carry out high-power laser directed energy deposition experiments on TC4 titanium alloy materials to verify the effectiveness and accuracy of the model and algorithm.

[0093] Specifically, the specific process in step S1 of the present invention includes the following sub-steps:

[0094] Figure 1 This is a power characteristic curve diagram of the high-power laser directional energy deposition process of the present invention, Figure 2 This is the system boundary diagram of the high-power laser directional energy deposition process of the present invention, which determines the composition and operating status of each subsystem involved in the deposition process.

[0095] S11. Establish the time function relationship under the working state of high-power laser directional energy deposition process.

[0096] S111, the total time function of high power laser directional energy deposition process is:

[0097] T=T rt +T rm +T st +T pt

[0098] Where, T is the total time of the deposition process, unit: S; T rt is the deposition auxiliary time; T rm Working time for the robot; T st is the laser interlayer interval time; T pt Delay the powder feeding time for the powder feeder.

[0099] S112, the robot working time function is:

[0100] T rm =T dm +T b +(K1+K2)×N0

[0101]

[0102]

[0103] Where, T rm is the robot feeding time; T dm is the deposition process time; T b is the single-pass return time; L is the length of the sedimentary layer, unit: mm; V o is the single-channel return speed, unit: mm / s; V f is the scanning speed, unit: mm / s; N1 is the number of deposition passes; N2 is the number of deposition layers; N0 is the number of times the robot rises and falls; K1 and K2 are the fixed time constants consumed by the robot's descent and ascent.

[0104] The working time function of the water cooler in operation is:

[0105]

[0106] Where, T cm P is the working time of the water cooler; linis the laser input power; P lm is the laser operating power; T lm is the deposition process time; in this case, water is selected as the coolant, and the flow rate v c is 100L1 / min; density ρ is 1×10 3 kg / m 3 ; specific heat capacity c is 4.2×10 3 J·kg -1 ℃ -1 , ΔT is the coolant temperature difference (ΔT=2°C).

[0107] S12. Establish a total carbon emission model for the high-power laser directed energy deposition process system.

[0108] S121, such as Figure 4 As shown, the relevant carbon emission model is established according to the working status of the laser:

[0109] C L =(P lt ×(TT dm )+P lm ×T dm +P ls ×T ds )×C e

[0110] In the formula, C L Carbon emissions from the laser operation process; C e To select the carbon emission factor for the Northeast region, unit: kgCO2 / kWh; P lt is the laser subsystem standby power; P ls is the inter-layer stop power of the laser subsystem.

[0111] S122, such as Figure 5 As shown in the figure, since the water chiller is a separate subsystem and is not affected by the laser input power, the carbon emission model of the cooling subsystem is established:

[0112] C C =(P ct ×(TT cm )+P cm ×T cm )×C e

[0113] In the formula, C L Carbon emissions from the working process of the water cooler; C e To select the carbon emission factor for Northeast China; P ct is the cooling subsystem standby power; P cm are the working power of cooling subsystem respectively.

[0114] S123, such as Figure 6 As shown in the figure, the carbon emissions of the robot feeding system are mainly directly determined by the power consumption during the movement. The carbon emission model of the robot feeding subsystem is established according to its different working states:

[0115] C R =(P Rt ×(TT rm )+P Rm ×(T rm -T b )+P Rb ×T b )×C e

[0116] In the formula, C R Carbon emissions from the robot feeding process; C e To select the carbon emission factor for Northeast China; P R-st is the standby power of the robot feeding subsystem, P Rm is the working power of the robot feeding subsystem, P Rb are the return power of the robot feeding subsystem respectively.

[0117] S124, such as Figure 7 As shown in the figure, according to the working principle of the powder feeder, the power consumption in the start-stop state will produce instantaneous power changes, but the change value is small and the time is short. The power in the start-stop state is classified as standby power to establish the carbon emission model of the powder feeding subsystem:

[0118] C p =(P p-st ×(T-(T dm +T Pt ))+P pm ×(T dm +T Pt ))×C e

[0119] In the formula, C P Carbon emissions from the powder feeder working process; C e To select the carbon emission factor for Northeast China; P p-st Powder feeding subsystem standby power; P pm are the working power of the powder feeding subsystem respectively.

[0120] S125. Since the power change of the auxiliary subsystem is basically in a constant value state from the start of working state to the end of working state, a carbon emission model is established:

[0121] C Aux =C e ×P A ×T

[0122] In the formula, CAux Carbon emissions from the auxiliary system working process; C e To select the carbon emission factor for Northeast China; P A is the comprehensive working power of the auxiliary system; T is the total time of the deposition process.

[0123] S126. During the sealing and deposition process, metal powder will produce a certain amount of loss. The protective gas is used to maintain the deposition process in an inert atmosphere. The material carbon emission models generated by the consumption of the two are:

[0124] C M =M m ×C m

[0125] C Ar =C g ×Q×(T dm +T pt )

[0126] In the formula, C M Carbon emission of metal powder; C Ar To protect gas carbon emissions; M m is the mass of metal powder not used in the cladding process, unit: g; Q is the flow rate of protective gas, unit: L / min; T dm The time when the protective gas is turned on; T pt It is the powder feeding time.

[0127] S127. Through the analysis of carbon emissions of each system under different working conditions, it can be seen that the total carbon emission model of the high-power laser directed energy deposition process system is:

[0128]

[0129] Specifically, according to the step S2 of the present invention, the energy consumption of the high-power laser directional energy deposition process is monitored in real time using a Topray TP600 power analyzer.

[0130] Specifically, the specific process in step S3 according to the present invention includes the following sub-steps:

[0131] S31. The linear regression fitting method is used to fit the data of the laser, robot and powder feeder respectively, see Table 1. The mathematical function relationship between the laser input power and output power is obtained through data fitting: P lm =515.9+3.087P lin , its standby power is 220W; the mathematical function relationship between the robot scanning speed and output power is: P Rm =448.4+0.8479V f, its standby power is 150W; the mathematical function relationship between the powder feeder powder feeding speed and output power is: P pm =25.58+9.246V p , its standby power is 38W.

[0132] S32. Through multiple actual measurements, the working power of the water cooler is 5850W, the standby power is 4913W, and the output power of the auxiliary equipment in the working state is 565W.

[0133] S33, conduct a powder calibration experiment and determine the actual powder delivery amount at different powder disc speeds. Based on the powder utilization rate and 80% of the recycled powder, the carbon emission model parameters of TC4 metal powder materials during the deposition process are obtained:

[0134]

[0135] In the formula, C M is the carbon emission of metal powder; η is the powder utilization rate; V P is the powder feeding speed.

[0136] Table 1 Single factor experiment - power characteristics change data table

[0137]

[0138] Specifically, the specific process in step S4 of the present invention includes the following sub-steps:

[0139] S41. Considering that the processing cost is directly related to the powder utilization rate, the ratio of the cladding layer mass per unit time to the powder delivery mass per unit time is expressed as:

[0140]

[0141] Where, η is the powder utilization rate; V P is the powder feeding speed; M1 and M2 are the mass of the substrate before and after deposition, unit: g.

[0142] S42. Since the single-pass deposition efficiency directly determines the multi-pass and multi-layer deposition efficiency, the deposition layer volume per unit time is used as the evaluation standard of deposition efficiency:

[0143]

[0144] In the formula, is the deposition efficiency; H is the height of a single-channel deposition layer, unit: mm; W is the width of a single-channel deposition layer, unit: mm.

[0145] S43. The quality evaluation standard of the sediment layer can be tested by the aspect ratio to see whether it meets the ideal requirements:

[0146]

[0147] Wherein, γ represents the aspect ratio; W represents the width of a single-track deposition, unit: mm; H represents the height of a single-track deposition, unit: mm.

[0148] S44, as shown in Table 2, the powder utilization rate and deposition efficiency fitting mathematical function relationship obtained by data fitting is as follows:

[0149] Powder uses mathematical functions:

[0150]

[0151] Deposition efficiency mathematical function:

[0152]

[0153] S45. The aspect ratio fitting mathematical function relationship is obtained by fitting the data in Table 2, and is used as a constraint condition:

[0154]

[0155] Table 2 Response surface test results statistics

[0156]

[0157] S46. Construct a multi-objective optimization model for process parameters:

[0158]

[0159] Specifically, the specific process in step S5 of the present invention includes the following sub-steps:

[0160] S51, based on the adaptive dynamic adjustment of mutation crossover and reference point selection mechanism, the improved NSGA-Ⅲ algorithm model is solved. The algorithm flow chart is as follows Figure 8 As shown in Figure 2, the Pareto contrast before and after the improvement is Fig. 9 shown.

[0161] This case proposes an adaptive dynamic population crossover and mutation strategy based on population crowding and evolutionary iteration number. The early iteration process uses a larger mutation rate and a smaller crossover rate to ensure that the global solution is quickly obtained; the later iteration process uses a smaller mutation rate and a larger crossover rate to jump out of the local optimal solution. According to the relationship between individual crowding and the average crowding of the population, an iteration factor is introduced to continuously perform crossover and mutation operations so that individuals with high fitness are selected for crossover.

[0162]

[0163]

[0164] In the formula, PC max 、PC max To set the maximum and minimum values ​​of the crossover rate; PM max 、PM max To set the maximum and minimum values ​​of the mutation rate; PC ave is the average value of the current solution of the fitness function; K max is the current maximum number of iterations, T iter is the current iteration number; d(i) is the crowding degree of the i-th individual, d ave (i) is the average congestion degree of the frontier where the i-th individual is located.

[0165] This case proposes an adaptive dynamic reference point selection mechanism and sorting strategy, which adaptively and dynamically identifies and deletes "useless" reference points or adds new reference points based on the current population distribution and evolution stage, and readjusts the sorting according to factors such as the degree of association between the solution and the reference point and the crowding distance of the solution. t The reference line d(s,w) of the nearest reference point w is calculated to be the distance to the nearest reference point π(s) and the corresponding distance d(s).

[0166]

[0167] When k = 1, calculate the association number ρ with the jth reference point j Minimum J min , I j is the individual associated with reference point j.

[0168]

[0169] S52. Select the Pareto optimal solution set based on the CRITIC-TOPSIS analysis method.

[0170] When making multi-objective decisions, a systematic and scientific comprehensive indicator system needs to be established, and the scoring system is based on a percentage system. On this basis, based on the CRITIC-TOPSIS method, the weight coefficients of carbon emissions, powder utilization rate and deposition efficiency are obtained as follows: 0.3362, 0.2996, and 0.3642. The TOPSIS method is used to select the best process parameter combination from the thirty optimal solution sets, and the Pareto solution set decision results are shown in Table 3.

[0171] Table 3 Pareto solution set decision results

[0172]

[0173] Specifically, the specific process in step S6 of the present invention includes the following sub-steps:

[0174] S61. Conduct high-power laser directional energy deposition experiments on 40mm*20mm*15mm TC4 titanium alloy molded parts, and optimize the lowest carbon emission, the highest powder utilization rate, and the highest deposition efficiency based on the optimal Pareto solution set. The optimization results are shown in Table 4. Compared with the empirical solution 6, the deposition efficiency is increased by about 28%, the powder utilization rate is increased by about 22%, and the carbon emission is slightly increased.

[0175] Table 4 Comparison of optimization results

[0176]

[0177] The above content elaborates on the preferred specific embodiments of the present invention, aiming to provide a clear and operable implementation path for technical experts in the same industry through specific technical concepts and feature displays. Any equivalent changes, adaptive adjustments or innovative modifications based on the core concept of the present invention, as long as they maintain the essential characteristics and effects of the present invention, should be deemed to fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should not be limited to the above specific embodiments, but should be comprehensively and objectively defined in accordance with the claims and their equivalent principles.

Claims

1. A multi-objective optimization method for process parameters of high-power laser directional energy deposition process for low carbon and high efficiency, characterized in that: The specific steps are as follows: S1. Construct a comprehensive model of carbon emissions from high-power laser directed energy deposition process systems; S2. Build a real-time monitoring platform for energy consumption in high-power laser directional energy deposition process; S3. Conduct high-power laser directed energy deposition experiments and apply linear regression fitting methods to obtain relevant experimental parameters of the carbon emission model; S4. Construct a multi-objective optimization model for process parameters of high-power laser directed energy deposition process with carbon emission, powder utilization and deposition efficiency as the targets; S5. Solve the process parameter optimization model of high-power laser directional energy deposition process based on an improved NSGA-Ⅲ algorithm with adaptive dynamic adjustment of variation crossover and reference point selection mechanism; S6. Use the optimized process parameters to carry out high-power laser directed energy deposition experiments on TC4 titanium alloy materials to verify the effectiveness and accuracy of the model and algorithm.

2. The multi-objective optimization method for process parameters of a high-power laser directed energy deposition process for low carbon and high efficiency as claimed in claim 1, characterized in that: Step S1 includes the following sub-steps: S11. Establish the time function relationship of the deposition process: S111. Construct the total time function of the deposition process: T=T rt +T rm +T st +T pt Where, T is the total deposition time; T rt is the deposition auxiliary time; T rm Working time for the robot; T st is the laser interlayer interval time; T pt Delay powder feeding time for powder feeder; S112, the robot working time function is: T rm =T dm +T b +(K1+K2)×N0 Where, T rm is the robot feeding time; T dm is the deposition process time; T b is the single-pass return time; L is the length of the sedimentary layer; V o is the single-channel return speed; V f is the scanning speed; N1 is the number of deposition paths; N2 is the number of deposition layers; N0 is the number of times the robot rises and falls; K1 and K2 are the fixed time constants consumed by the robot when it falls and rises. The working time function of the water cooler in operation is: Where P lin is the laser input power; P lm is the laser working power; v c is the coolant flow rate; ρ is the coolant density; c is the coolant specific heat capacity, ΔT is the coolant temperature difference; S12. Establish a total carbon emission model for the sedimentation process system: S121, Laser subsystem carbon emission model: C L =(P lt ×(T-T dm )+P lm ×T dm +P ls ×T ds )×C e In the formula, C L is the carbon emission of the laser; C e is the carbon emission factor of Northeast China; P lt , P ls They are the laser subsystem standby power and inter-layer stop power respectively; S122, Carbon emission model of water chiller subsystem: C C =(P ct ×(T-T cm )+P cm ×T cm )×C e In the formula, C C is the carbon emission of the water cooler; P ct , P cm They are the standby power and working power of the cooling subsystem respectively; S123. Carbon emission model of robot feeding subsystem: C R =(P Rt ×(T-T rm )+P Rm ×(T rm -T b )+P Rb ×T b )×C e In the formula, C R Feeding carbon emissions to robots; R-st , P Rm , P Rb They are the standby power, working power and return power of the robot feeding subsystem; S124, Carbon emission model of powder feeder subsystem: C p =(P p-st ×(T-(T dm +T Pt ))+P pm ×(T dm +T Pt ))×C e In the formula, C P is the carbon emission of powder feeder; P p-st , P pm They are the standby power and working power of the powder delivery subsystem respectively; S125, auxiliary system carbon emission model: C Aux =C e ×P A ×T In the formula, C Aux is the comprehensive carbon emission of auxiliary systems; P A It is the comprehensive working power of the auxiliary system; S126, Material Carbon Emission Model: C M =M m ×C m ;C Ar =C g ×Q×(T dm +T pt ) In the formula, C M Carbon emission of metal powder; C Ar To protect gas carbon emissions; M m (g) is the mass of metal powder not used in the cladding process; Q is the flow rate of shielding gas; T dm The time for the shielding gas to be turned on; S127, Total carbon emission model of high power laser directed energy deposition process system:

3. The multi-objective optimization method for process parameters of a high-power laser directed energy deposition process for low carbon and high efficiency as claimed in claim 1 or 2, characterized in that: Step S2 specifically includes: using the Topray TP600 power analyzer to monitor the energy consumption of the high-power laser directional energy deposition process in real time.

4. The multi-objective optimization method for process parameters of a high-power laser directed energy deposition process for low carbon and high efficiency as claimed in claim 3 is characterized in that: Step S3 includes the following sub-steps: S31. The mathematical function relationship between the laser input power and output power obtained by data fitting is: P lm =515.9+3.087P lin ; The mathematical function relationship between the robot scanning speed and output power is: P Rm =448.4+0.8479V f ; The mathematical function relationship between the powder feeding speed and output power of the powder feeder is: P pm =25.58+9.246V p ; S32, obtaining the output power value of the water cooler and auxiliary equipment in the working state; S33. Obtaining carbon emission model parameters of materials during deposition: In the formula, C M is the carbon emission of metal powder; η is the powder utilization rate; V P is the powder feeding speed.

5. The multi-objective optimization method for process parameters of a high-power laser directed energy deposition process for low carbon and high efficiency as claimed in claim 4, characterized in that: Step S4 includes the following sub-steps: S41. Establish a mathematical relationship for powder utilization: Where, η is the powder utilization rate; V P is the powder feeding speed; M1 and M2 are the substrate masses before and after deposition; S42. Establish a mathematical relationship for deposition efficiency: In the formula, is the deposition efficiency; H is the height of a single-channel deposition layer; W is the width of a single-channel deposition layer; S43, establish a mathematical relationship between the aspect ratio and the height ratio: Where, γ represents the aspect ratio; W represents the width of a single deposition track; H represents the height of a single deposition track; S44. The mathematical function relationship among powder utilization rate, deposition efficiency and aspect ratio is obtained by fitting the experimental data: Mathematical function of powder utilization rate η: Deposition efficiency Mathematical functions: S45, aspect ratio γ mathematical function: S46. Construct a multi-objective optimization model for process parameters:

6. The multi-objective optimization method for process parameters of a high-power laser directed energy deposition process for low carbon and high efficiency as claimed in claim 5, characterized in that: Step S5 includes the following sub-steps: S51. Improved NSGA-Ⅲ algorithm model solution based on adaptive dynamic adjustment of mutation crossover and reference point selection mechanism; This case proposes an adaptive dynamic population crossover and mutation strategy based on population crowding and evolutionary iteration number. In the early iteration process, a larger mutation rate and a smaller crossover rate are used to ensure that the global solution is quickly obtained; in the later iteration process, a smaller mutation rate and a larger crossover rate are used to jump out of the local optimal solution. According to the relationship between individual crowding and the average crowding of the population, an iteration factor is introduced to continuously perform crossover and mutation operations so that individuals with high fitness are selected for crossover. In the formula, PC max 、PC max To set the maximum and minimum values ​​of the crossover rate; PM max 、PM max To set the maximum and minimum values ​​of the mutation rate; PC ave is the average value of the current solution of the fitness function; K max is the current maximum number of iterations, T iter is the current iteration number; d(i) is the crowding degree of the i-th individual, d ave (i) is the average congestion degree of the frontier where the i-th individual is located, This paper proposes a method based on adaptive dynamic reference point selection mechanism and sorting strategy, which adaptively and dynamically identifies and deletes "useless" reference points or adds new reference points according to the current population distribution and evolution stage, and readjusts the sorting results according to factors such as the degree of association between the solution and the reference point and the crowding distance of the solution. t The reference line d(s,w) of the nearest reference point w is calculated to the nearest reference point π(s) and the corresponding distance d(s); When k = 1, calculate the association number ρ with the jth reference point j The minimum value J min , I j is the population individual associated with reference point j, S52. Select the Pareto optimal solution based on the CRITIC-TOPSIS analysis method.

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