A method for modeling energy consumption of a laser cleaning process and optimizing process parameters

Abstract

This invention relates to a method for energy consumption modeling and process parameter optimization in laser cleaning processes, belonging to the field of advanced manufacturing and automation technology. The method includes the following steps: establishing an energy consumption model for the laser cleaning process based on energy consumption analysis of the laser system, robot system, cooling system, and dust removal system; building a real-time power monitoring platform to obtain experimental parameters of the energy consumption model; establishing an energy-oriented laser cleaning process parameter optimization model considering energy efficiency, surface roughness, and surface oxygen content; solving the model using an improved lion herd optimization algorithm; and providing case studies. This invention is simple and practical, and fully considers energy consumption and cleaning quality during modeling, providing excellent support for energy consumption optimization in laser cleaning processes.

Description

Technical Field

[0001] This invention relates to a method for energy consumption modeling and process parameter optimization in laser cleaning processes, belonging to the field of advanced manufacturing and automation technology. Background Technology

[0002] In recent years, carbon peaking and carbon neutrality have become the top priorities for countries around the world to cope with global climate change and achieve sustainable development. As the world's largest manufacturing country, my country's industrial energy consumption is the main source of carbon emissions. How to save energy and reduce carbon emissions from manufacturing is the key to my country's sustainable economic development. Laser cleaning technology, as an important direction in laser processing technology in the current industrial field, uses high-energy pulse shock waves of lasers to break the force between pollutants and the surface of parts, thereby removing pollutants[1]. It has been widely used in aerospace, marine engineering equipment and rail transportation. However, laser cleaning is time-consuming, and the incomplete conversion of electrical energy and the incomplete utilization of laser energy lead to a large amount of energy consumption in the laser cleaning process. Therefore, how to establish an energy consumption model and process optimization for the laser cleaning process is the key to achieving green and high-quality development of laser cleaning technology. Summary of the Invention

[0003] To address the aforementioned problems, this invention develops a method for energy consumption modeling and process parameter optimization in laser cleaning processes. It analyzes the energy consumption characteristics and patterns of various systems within the laser cleaning equipment and establishes an energy consumption model for the laser cleaning process. Based on this, a multi-objective optimization model for laser cleaning process parameters is established, targeting energy consumption, energy efficiency, surface roughness, and surface oxygen content. An improved lion herd optimization algorithm is proposed to solve the model and obtain the optimal process parameters. The effectiveness and feasibility of the model are verified through an experimental case study of laser removal of anodized film from aluminum.

[0004] The present invention provides a method for energy consumption modeling and process parameter optimization in a laser cleaning process, comprising the following steps:

[0005] S1. Establish an energy consumption model for the laser cleaning process based on energy consumption analysis of the laser system, robot system, cooling system, and dust removal system;

[0006] S2. Build a real-time power monitoring platform to obtain energy consumption model test parameters;

[0007] S3. Considering energy efficiency, surface roughness, and surface oxygen content, establish an optimization model for the laser cleaning process parameters oriented towards energy consumption.

[0008] S4. Solving the process parameter optimization model for laser cleaning based on the improved lion pack optimization algorithm;

[0009] S5. Case Analysis.

[0010] Step S1 includes the following sub-steps:

[0011] S6. Construct a total energy consumption model for the equipment: E T =E l +E r +E a In the formula: E T E represents the total energy consumption of the laser cleaning equipment. l Energy consumption of the laser system; E r Energy consumption of the robot system; E a This refers to the energy consumption of the auxiliary system.

[0012] S7. Constructing an energy consumption model for the laser system: In the formula: P lw E represents the standby power of the laser system. l P represents the energy consumption of the laser system. lc P represents the operating power of the laser. l t is the output power of the laser; l This refers to the operating time of the laser system.

[0013] S8. Constructing an energy consumption model for the robot system: E r =P rw ×(t t -t r )+P rwo ×t r =P rw ×(t t -L / v r )+P rwo ×L / v r In the formula P rw P represents the robot's standby power. rwo For robot operating power; t r v represents the robot's runtime. r L represents the robot's travel speed; L represents the cleaning length.

[0014] S9. Construct an energy consumption model for the auxiliary system: E a =E co +E ep E in the formula co Energy consumption of the cooling system; E ep This refers to the energy consumption of the dust removal system. The energy consumption of the cooling system can be expressed as: In the formula P cw P is the standby power of the cooling system. cwo The operating power of the cooling system; t t Let be the total operating time of the cooling system; η be the efficiency of the cooling system in absorbing heat; ρ be the density of the cooling water; v be the flow rate of the cooling water; C be the specific heat capacity of the cooling water; and ΔT be the temperature difference of the cooling water. The energy consumption of the dust removal system can be expressed as: Eep =P epw ×(t t -t ep )+P epwo ×t ep In the formula P epw and P epwo These are the standby power and operating power of the dust removal system, respectively, in tons. ep This refers to the operating time of the dust removal system.

[0015] S10, Total Energy Consumption Model for Laser Cleaning Process:

[0016]

[0017] Step S2 includes the following sub-steps:

[0018] S11. Establish a real-time power monitoring platform based on laser cleaning equipment;

[0019] S12. The power of the laser under operating conditions obtained from data fitting is: P lc =683.4+2.685×n×f, where n is the single pulse energy and f is the pulse frequency;

[0020] S13. Obtain the robot system power parameters;

[0021] S14. Obtain the power parameter values ​​of the cooling system;

[0022] S15. Obtain the working power value of the dust removal system.

[0023] Step S3 includes the following sub-steps:

[0024] S16. Establish an energy consumption objective function based on the energy consumption model.

[0025]

[0026] S17. Establish the energy efficiency function

[0027] S18. Fitting surface roughness function based on experimental data

[0028] R a =6.535-0.1292n-0.021f+0.00067n 2 ;

[0029] S19. Fitting the surface oxygen content function based on experimental data

[0030] W to =392.943+0.03399n 2 -0.0535v 2 -0.259f2 -7.6515n

[0031] +0.7805v-1.607f+0.0245nv+0.0845nf-0.2465vf

[0032] S20. Establish a multi-objective optimization function model.

[0033] F(n,f,v r )={minE,minR a ,minW to ,maxη}

[0034]

[0035] Step S4 includes the following sub-steps:

[0036] S21. Multi-objective optimization based on the improved lion pride optimization algorithm;

[0037] S22. Select the optimal solution based on the entropy weight-TOPSIS method.

[0038] The beneficial effects of this invention are: a method for energy consumption modeling and process parameter optimization in laser cleaning processes, analyzing the energy consumption characteristics of each subsystem in the laser cleaning process, and establishing a comprehensive energy consumption model for the laser cleaning process. Based on this, a multi-objective optimization model for laser cleaning process parameters is established, with energy consumption, energy efficiency, surface roughness, and surface oxygen content as objectives. An improved lion herd optimization algorithm is proposed for solving the model to obtain the optimal process parameters. The effectiveness and feasibility of the model are verified through an experimental case study of laser removal of anodized film from aluminum. Research on energy consumption modeling and process parameter optimization in laser cleaning processes has significant engineering implications for the widespread application of laser cleaning technology in manufacturing. Attached Figure Description

[0039] Figure 1 This is a power characteristic curve of the laser cleaning process of the present invention.

[0040] Figure 2 This invention relates to a real-time power monitoring platform for the laser cleaning equipment during the cleaning process.

[0041] Figure 3 This is a power variation diagram of the laser system of the present invention.

[0042] Figure 4 This is a diagram showing the power variation of the cooling system of the present invention.

[0043] Figure 5 This is a power variation diagram of the dust removal system of the present invention.

[0044] Figure 6 This is a diagram showing the power variation of the robot system of the present invention.

[0045] Figure 7 The flowchart shows the improved lion pride optimization algorithm of this invention.

[0046] Figure 8 These are comparison images of the macroscopic morphology of the substrate surface after laser cleaning according to the present invention.

[0047] Figure 9 This is a diagram illustrating the optimization results of the present invention. Detailed Implementation

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the embodiments are used to explain the present invention and are not intended to limit the present invention.

[0049] This invention develops a method for energy consumption modeling and process parameter optimization in laser cleaning processes. Figure 1 This is a power characteristic curve of the laser cleaning process. Figure 2 A real-time power monitoring platform for laser cleaning equipment during the cleaning process. Figure 3 This is a graph showing the power variation of the laser system. Figure 4 This is a diagram showing the power variation of the cooling system. Figure 5 This is a graph showing the power variation of the dust removal system. Figure 6 This is a diagram showing the power variation of the robot system. Figure 7 To improve the lion pride optimization algorithm flowchart. (For example...) Figure 1-7 The diagram illustrates a laser cleaning equipment power real-time monitoring platform, laser cleaning process power characteristic curve, laser system power change curve, cooling system power change curve, dust removal system power change curve, robot system power change curve, and improved lion pack optimization algorithm flow, all part of a laser cleaning process energy consumption modeling and process parameter optimization method according to the present invention.

[0050] The overall technical solution of this invention is a method for energy consumption modeling and process parameter optimization in laser cleaning processes, comprising the following steps:

[0051] S1. Establish an energy consumption model for the laser cleaning process based on energy consumption analysis of the laser system, robot system, cooling system, and dust removal system;

[0052] S2. Build a real-time power monitoring platform to obtain energy consumption model test parameters;

[0053] S3. Considering energy efficiency, surface roughness, and surface oxygen content, establish an optimization model for the laser cleaning process parameters oriented towards energy consumption.

[0054] S4. Solving the process parameter optimization model for laser cleaning based on the improved lion pack optimization algorithm;

[0055] S5. Case Analysis.

[0056] Step S1 includes the following sub-steps:

[0057] S6. Construct a total energy consumption model for the equipment: E T =E l +E r +E a In the formula: E T E represents the total energy consumption of the laser cleaning equipment. l Energy consumption of the laser system; E r Energy consumption of the robot system; E a This refers to the energy consumption of the auxiliary system.

[0058] S7, by Figure 3 Constructing an energy consumption model for the laser system: In the formula: P lw E represents the standby power of the laser system. l P represents the energy consumption of the laser system. lc P represents the operating power of the laser. l t is the output power of the laser; l This refers to the operating time of the laser system.

[0059] S8, by Figure 6 Constructing an energy consumption model for a robot system: E r =P rw ×(t t -t r )+P rwo ×t r =P rw ×(t t -L / v r )+P rwo ×L / v r In the formula P rw P represents the robot's standby power. rwo For robot operating power; t r v represents the robot's runtime. r L represents the robot's travel speed; L represents the cleaning length.

[0060] S9, by Figure 4 , Figure 5 Construct an energy consumption model for the auxiliary system: E a =E co +E ep E in the formula co Energy consumption of the cooling system; E ep This refers to the energy consumption of the dust removal system. The energy consumption of the cooling system can be expressed as: In the formula P cw P is the standby power of the cooling system. cwo The operating power of the cooling system; t tLet be the total operating time of the cooling system; η be the efficiency of the cooling system in absorbing heat; ρ be the density of the cooling water; v be the flow rate of the cooling water; C be the specific heat capacity of the cooling water; and ΔT be the temperature difference of the cooling water. The energy consumption of the dust removal system can be expressed as: E ep =P epw ×(t t -t ep )+P epwo ×t ep In the formula P epw and P epwo These are the standby power and operating power of the dust removal system, respectively, in tons. ep This refers to the operating time of the dust removal system.

[0061] S10, Total Energy Consumption Model for Laser Cleaning Process:

[0062]

[0063] Step S2 includes the following sub-steps:

[0064] S11. Establish a real-time power monitoring platform for the cleaning process based on laser cleaning equipment, such as... Figure 2 ;

[0065] S12. The power of the laser under operating conditions obtained by fitting the data in Table 1 is: P lc =683.4+2.685×n×f, where n is the single pulse energy and f is the pulse frequency;

[0066] Table 1. Power Variation of Laser System

[0067]

[0068] S13. Obtain the robot system power parameters;

[0069] S14. Obtain the power parameter values ​​of the cooling system;

[0070] S15. Obtain the working power value of the dust removal system.

[0071] S16. Construct a mathematical model of energy consumption for the laser cleaning process based on Tables 2 and 3.

[0072] Table 2 Power Parameter Table

[0073]

[0074] Table 3 Cooling System Parameters

[0075]

[0076] Step S3 includes the following sub-steps:

[0077] S17. Establish an energy consumption objective function based on the energy consumption model.

[0078]

[0079] S18. Establish the energy efficiency function

[0080] S19. Fitting the surface roughness function R based on experimental data in Table 4. a =6.535-0.1292n-0.021f+0.00067n 2 ;

[0081] Table 4 Surface Roughness Record Table

[0082]

[0083] S20. Fitting the surface oxygen content function W based on experimental data in Table 5. to =392.943+0.03399n 2 -0.0535v 2 -0.259f 2 -7.6515n+0.7805v-1.607f+0.0245nv+0.0845nf-0.2465vf

[0084] Table 5 Surface Oxygen Content Record Table

[0085]

[0086] S21. Establish a multi-objective optimization function model.

[0087] F(n,f,v r )={minE,minR a ,minW to ,maxη}

[0088]

[0089] Step S4 includes the following sub-steps:

[0090] S22. Multi-objective optimization based on the improved lion pride optimization algorithm, such as... Figure 7 ;

[0091] S23. The optimal solution is selected based on the entropy weight-TOPSIS method, as shown in Table 6.

[0092] Table 6 Multi-objective Decision Analysis Table

[0093]

[0094] S5. Case Analysis.

[0095] A laser cleaning experiment was conducted on a 20mm*300mm section of 5052 aluminum alloy anodized film. The laser cleaning experiment was performed using the optimized process parameters obtained in Table 6. Table 7 shows that the energy consumption was 21.84% lower than before optimization, the surface roughness was 13.57% lower than before optimization, and the surface oxygen content was 10.89% lower than before optimization.

[0096] Depend on Figure 8 It can be seen that the morphology quality after optimization is slightly better than that before optimization. Most areas show metallic luster, and only a few black spots are areas where the anodic oxide film was not completely removed. Based on the above analysis, the accuracy of the established model and the optimized process parameters can effectively reduce the energy consumption of the laser cleaning process and improve the cleaning quality are verified again. Figure 9 To optimize the results analysis chart.

[0097] Table 7 Comparison of Laser Cleaning Experiment Before and After Optimization

[0098]

[0099] The above description represents preferred embodiments of the present invention and is not intended to limit the invention. Those skilled in the art can still modify the above technical solutions or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for energy consumption modeling and process parameter optimization in laser cleaning processes, characterized in that, Includes the following steps: S1. Establish an energy consumption model for the laser cleaning process based on energy consumption analysis of the laser system, robot system, cooling system, and dust removal system; S2. Build a real-time power monitoring platform to obtain energy consumption model test parameters; S3. Considering energy efficiency, surface roughness, and surface oxygen content, establish an optimization model for the laser cleaning process parameters oriented towards energy consumption. S4. Solving the process parameter optimization model for laser cleaning based on the improved lion pack optimization algorithm; S5, Case Analysis Step S1 includes the following steps: S6. Construct a total energy consumption model for the equipment: E T =E l +E r +E a In the formula: E T E represents the total energy consumption of the laser cleaning equipment. l Energy consumption of the laser system; E r Energy consumption of the robot system; E a For the energy consumption of auxiliary systems; S7. Constructing an energy consumption model for the laser system: In the formula: P lw E represents the standby power of the laser system. l P represents the energy consumption of the laser system. lc P represents the operating power of the laser. l t is the output power of the laser; l This refers to the laser system's operating time. S8. Constructing an energy consumption model for the robot system: E r =P rw ×(t t -t r )+P rwo ×t r =P rw ×(t t -L / v r )+P rwo ×L / v r In the formula P rw P represents the robot's standby power. rwo For robot operating power; t r v represents the robot's runtime. r L represents the robot's travel speed; L represents the cleaning length. S9. Construct an energy consumption model for the auxiliary system: E a =E co +E ep E in the formula co Energy consumption of the cooling system; E ep The energy consumption of the dust removal system and the cooling system can be expressed as follows: In the formula P cw P is the standby power of the cooling system. cwo The operating power of the cooling system; t t The total operating time of the cooling system is given by η, the efficiency of the cooling system in absorbing heat is given by ρ, the density of the cooling water is given by v, the flow rate of the cooling water is given by C, the specific heat capacity of the cooling water is given by ΔT, and the energy consumption of the dust removal system can be expressed as: E ep =P epw ×(t t -t ep )+P epwo ×t ep In the formula P epw and P epwo These are the standby power and operating power of the dust removal system, respectively, in tons. ep This refers to the operating time of the dust removal system; S10, Total Energy Consumption Model for Laser Cleaning Process: Step S2 includes the following steps: S11. Establish a real-time power monitoring platform based on laser cleaning equipment; S12. The power of the laser under operating conditions obtained from data fitting is: P lc =683.4+2.685×n×f, where n is the single pulse energy and f is the pulse frequency; S13. Obtain the robot system power parameters; S14. Obtain the power parameter values ​​of the cooling system; S15. Obtain the operating power value of the dust removal system. Step S3 includes the following steps: S16. Establish an energy consumption objective function based on the energy consumption model. S17. Establish the energy efficiency function S18. Fitting surface roughness function based on experimental data R a =6.535-0.1292n-0.021f+0.00067n 2 ; S19. Fitting the surface oxygen content function based on experimental data W to =392.943+0.03399n 2 -0.0535v 2 -0.259f 2 -7.6515n+0.7805v-1.607f+0.0245nv+0.0845nf-0.2465vf S20. Establish a multi-objective optimization function model. F(n,f,v r )={minE,minR a ,minW to ,maxη} Step S4 includes the following steps: S21. Multi-objective optimization based on the improved lion pride optimization algorithm; S22. Select the optimal solution based on the entropy weight-TOPSIS method.

Images