CN115016260A - Energy consumption modeling and process parameter optimization method in laser cleaning process - Google Patents

Energy consumption modeling and process parameter optimization method in laser cleaning process Download PDF

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CN115016260A
CN115016260A CN202210543768.8A CN202210543768A CN115016260A CN 115016260 A CN115016260 A CN 115016260A CN 202210543768 A CN202210543768 A CN 202210543768A CN 115016260 A CN115016260 A CN 115016260A
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energy consumption
laser
cleaning process
laser cleaning
power
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姜兴宇
李家振
王弘玥
索英祁
刘同明
田志强
于沈虹
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Shenyang University of Technology
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Abstract

The invention relates to an energy consumption modeling and process parameter optimization method in a laser cleaning process, and belongs to the technical field of advanced manufacturing and automation. The method of the invention comprises the following steps: establishing an energy consumption model of the laser cleaning process based on energy consumption analysis of the laser system, the robot system, the cooling system and the dust removal system; building a power real-time monitoring platform to obtain energy consumption model test parameters; establishing an energy consumption-oriented laser cleaning process technological parameter optimization model by considering energy efficiency, surface roughness and surface oxygen content factors; solving the model by using an improved lion group optimization algorithm; example analysis. The method is simple and practical, fully considers the energy consumption and the cleaning quality during modeling, and provides good support for optimizing the energy consumption in the laser cleaning process.

Description

Energy consumption modeling and process parameter optimization method in laser cleaning process
Technical Field
The invention relates to an energy consumption modeling and process parameter optimization method in a laser cleaning process, and belongs to the technical field of advanced manufacturing and automation.
Background
In recent years, carbon peaking and carbon neutralization have become the central importance of the world to meet global climate change and realize sustainable development. As the first major manufacturing country in the world at present, industrial energy consumption is a main source of carbon emission, and how to save energy and reduce carbon emission in manufacturing industry is the key of sustainable economic development in China. The laser cleaning technology is taken as an important direction in the laser processing technology in the current industrial field, and the acting force between pollutants and the surface of a part is broken by utilizing the high-energy pulse shock wave of laser, so that the pollutants are removed [1], and the laser cleaning technology is widely applied to the fields of aerospace, maritime work equipment and rail transit. However, the laser cleaning process consumes a long time, and the incomplete conversion of electric energy and the incomplete utilization of laser energy cause a large amount of energy consumption in the laser cleaning process. Therefore, how to establish an energy consumption model and process optimization in the laser cleaning process is the key for realizing the green and high-quality development of the laser cleaning technology.
Disclosure of Invention
Aiming at the problems, the invention develops an energy consumption modeling and process parameter optimization method in the laser cleaning process, analyzes the energy consumption characteristics and rules of each system of the laser cleaning equipment, and establishes an energy consumption model in the cleaning process of the laser cleaning equipment. On the basis, a laser cleaning process technological parameter multi-objective optimization model with energy consumption, energy efficiency, surface roughness and surface oxygen content as targets is established, an improved lion group optimization algorithm is provided for solving, the optimal technological parameters are obtained, and effectiveness and feasibility of the model are verified through an experiment case for removing the anodic oxidation film of the aluminum material through laser.
The invention relates to a method for energy consumption modeling and technological parameter optimization in a laser cleaning process, which comprises the following steps:
s1, establishing an energy consumption model of the laser cleaning process based on energy consumption analysis of the laser system, the robot system, the cooling system and the dust removal system;
s2, building a power real-time monitoring platform to obtain energy consumption model test parameters;
s3, establishing an energy consumption-oriented laser cleaning process parameter optimization model by considering energy efficiency, surface roughness and surface oxygen content factors;
s4, solving a laser cleaning process parameter optimization model based on an improved lion group optimization algorithm;
and S5, example analysis.
The step S1 includes the following sub-steps:
s6, constructing a total equipment energy consumption model: e T ＝E l +E r +E a In the formula: e T Total energy consumption for laser cleaning equipment; e l Energy consumption of the laser system; e r Energy consumption for the robot system; e a To assist in the energy consumption of the system.
S7, constructing a laser system energy consumption model:
Figure BDA0003651290770000021
in the formula: p lw Standby power for the laser system; e l Energy consumption of the laser system; p lc Is the operating power of the laser; p l Is the output power of the laser; t is t l Is the laser system runtime.
S8, constructing a robot system energy consumption model: 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 Standby power for the robot; p rwo Operating power for the robot; t is t r The robot runs time; v. of r Is the robot travel speed; l is the cleaning length.
S9, constructing an energy consumption model of the auxiliary system: e a ＝E co +E ep In the formula E co Energy consumption for cooling the system; e ep Energy consumption of a dust removal system. The cooling system energy consumption can be expressed as:
Figure BDA0003651290770000022
in the formula P cw Standby power for the cooling system; p cwo Operating power for the cooling system; t is t t Total time for cooling system operation; eta is the efficiency of the cooling system for absorbing heat; rho is the density of the cooling water; v is the cooling water flow rate; c is the specific heat capacity of cooling water, and Delta T is 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 Respectively stand-by power and running power of the dust removal system, t ep The operating time of the dust removal system.
S10, laser cleaning process total energy consumption model:
Figure BDA0003651290770000023
the step S2 includes the following sub-steps:
s11, building a power real-time monitoring platform based on laser cleaning equipment;
s12, obtaining the power of the laser in the running state by data fitting: p lc 683.4+2.685 × n × f, where n is the monopulse energy and f is the pulse frequency;
s13, acquiring power parameters of the robot system;
s14, obtaining a power parameter value of the cooling system;
and S15, obtaining the working power value of the dust removal system.
The step S3 includes the following sub-steps:
s16, establishing an energy consumption objective function based on the energy consumption model
Figure BDA0003651290770000031
S17, establishing an energy efficiency function
Figure BDA0003651290770000032
S18 fitting surface roughness function based on experimental data
R a ＝6.535-0.1292n-0.021f+0.00067n 2 ；
S19 fitting 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, establishing a multi-objective optimization function model
F(n,f,v r )＝{minE,minR a ,minW to ,maxη}
Figure BDA0003651290770000033
The step S4 includes the following sub-steps:
s21, performing multi-objective optimization based on the improved lion group optimization algorithm;
s22, selecting the optimal solution based on the entropy weight-TOPSIS method.
The invention has the beneficial effects that: the energy consumption modeling and process parameter optimization method for the laser cleaning process analyzes the energy consumption characteristics of all subsystems in the laser cleaning process and establishes an energy consumption comprehensive model for the laser cleaning process. On the basis, a laser cleaning process technological parameter multi-objective optimization model with energy consumption, energy efficiency, surface roughness and surface oxygen content as targets is established, an improved lion group optimization algorithm is provided for solving, the optimal technological parameters are obtained, and effectiveness and feasibility of the model are verified through an experimental case for removing the aluminum anodic oxide film by laser. The research on energy consumption modeling and process parameter optimization in the laser cleaning process has important engineering significance for the wide application of the laser cleaning technology in the manufacturing industry.
Drawings
FIG. 1 is a graph of power characteristics for a laser cleaning process according to the present invention.
FIG. 2 is a real-time power monitoring platform for a cleaning process of the laser cleaning device of the present invention.
FIG. 3 is a graph of the power variation of the laser system of the present invention.
FIG. 4 is a graph of the power variation of the cooling system of the present invention.
FIG. 5 is a graph showing the variation of the power of the dust-removing system of the present invention.
FIG. 6 is a power variation diagram of the robot system of the present invention.
FIG. 7 is a flow chart of the improved lion group optimization algorithm of the present invention.
FIG. 8 is a comparison of substrate surface macro topography after laser cleaning in accordance with the present invention.
FIG. 9 is an analysis diagram of the optimization results of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and examples, but it should be understood that the examples are illustrative of the present invention and are not intended to limit the present invention.
The invention discloses a method for modeling energy consumption and optimizing process parameters in a laser cleaning process, and fig. 1 is a power characteristic curve diagram of the laser cleaning process. FIG. 2 is a real-time power monitoring platform for a cleaning process of a laser cleaning device. Fig. 3 is a graph of laser system power variation. Fig. 4 is a graph of cooling system power variation. FIG. 5 is a graph of the power change of the dust removal system. Fig. 6 is a power variation diagram of the robot system. Fig. 7 is a flow chart of an improved lion group optimization algorithm. As shown in fig. 1 to 7, a laser cleaning equipment cleaning process power real-time monitoring platform, a laser cleaning process power characteristic curve, a laser system power variation curve, a cooling system power variation curve, a dust removal system power variation curve, a robot system power variation curve, and an improved lion group optimization algorithm flow in the laser cleaning process energy consumption modeling and process parameter optimization method of the present invention are shown.
The overall technical scheme of the invention is a method for modeling energy consumption and optimizing process parameters in a laser cleaning process, which comprises the following steps:
s1, establishing an energy consumption model of the laser cleaning process based on energy consumption analysis of the laser system, the robot system, the cooling system and the dust removal system;
s2, building a power real-time monitoring platform to obtain energy consumption model test parameters;
s3, establishing an energy consumption-oriented laser cleaning process technological parameter optimization model by considering energy efficiency, surface roughness and surface oxygen content factors;
s4, solving a laser cleaning process parameter optimization model based on an improved lion group optimization algorithm;
and S5, example analysis.
The step S1 includes the following sub-steps:
s6, constructing a total equipment energy consumption model: e T ＝E l +E r +E a In the formula: e T Total energy consumption for laser cleaning equipment; e l Energy consumption of the laser system; e r Energy consumption for the robot system; e a To assist in the energy consumption of the system.
S7, constructing a laser system energy consumption model according to the graph in FIG. 3:
Figure BDA0003651290770000041
in the formula: p is lw Standby power for the laser system; e l Energy consumption of the laser system; p lc Is the operating power of the laser; p l Is the output power of the laser; t is t l The laser system run time.
S8, constructing the robot system energy consumption model according to the figure 6: 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 Standby power for the robot; p rwo Operating power for the robot; t is t r The robot runs time; v. of r Is the robot travel speed; l is the cleaning length.
S9, constructing an energy consumption model of the auxiliary system according to the figures 4 and 5: e a ＝E co +E ep In the formula E co Energy consumption for cooling the system; e ep Energy consumption of a dust removal system. The cooling system energy consumption can be expressed as:
Figure BDA0003651290770000051
in the formula P cw Standby power for the cooling system; p cwo Operating power for the cooling system; t is t t Total time for cooling system operation; eta is the efficiency of the cooling system for absorbing heat; rho is the density of the cooling water; v is the cooling water flow rate; c is the specific heat capacity of cooling water, and Delta T is the temperature difference of the cooling water. Removing deviceThe dust system energy consumption can be expressed as: e ep ＝P epw ×(t t -t ep )+P epwo ×t ep In the formula P epw And P epwo Respectively stand-by power and running power of the dust removal system, t ep The operating time of the dust removal system.
S10, laser cleaning process total energy consumption model:
Figure BDA0003651290770000052
the step S2 includes the following sub-steps:
s11, building a real-time power monitoring platform of the cleaning process based on the laser cleaning equipment as shown in figure 2;
s12, fitting the data in the table 1 to obtain the power of the laser in the operating state as follows: p lc 683.4+2.685 × n × f, where n is the single pulse energy and f is the pulse frequency;
TABLE 1 laser system power variation table
Figure BDA0003651290770000053
S13, acquiring power parameters of the robot system;
s14, obtaining a power parameter value of the cooling system;
and S15, obtaining the working power value of the dust removal system.
S16, constructing energy consumption mathematical model of laser cleaning process based on table 2 and table 3
Figure BDA0003651290770000061
TABLE 2 Power parameter Table
Figure BDA0003651290770000062
TABLE 3 Cooling System parameters Table
Figure BDA0003651290770000063
The step S3 includes the following sub-steps:
s17, establishing an energy consumption objective function based on the energy consumption model
Figure BDA0003651290770000064
S18, establishing an energy efficiency function
Figure BDA0003651290770000065
S19 fitting surface roughness function R based on experimental data table 4 a ＝6.535-0.1292n-0.021f+0.00067n 2 ；
TABLE 4 surface roughness notes table
Figure BDA0003651290770000071
S20 fitting surface oxygen content function W based on experimental data 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
TABLE 5 recording table of surface oxygen content
Figure BDA0003651290770000081
S21, establishing a multi-objective optimization function model
F(n,f,v r )＝{minE,minR a ,minW to ,maxη}
Figure BDA0003651290770000082
The step S4 includes the following sub-steps:
s22, performing multi-objective optimization based on the improved lion group optimization algorithm as shown in the figure 7;
s23, selecting the optimal solution based on the entropy weight-TOPSIS method as shown in the table 6.
TABLE 6 Multi-objective decision analysis Table
Figure BDA0003651290770000091
And S5, example analysis.
The laser cleaning 5052 aluminum alloy anode oxide film experiment was performed at 20mm x 300mm, and the laser cleaning experiment was performed according to the process parameters optimized in table 6. From Table 7, it is understood that the energy consumption is less than 21.84% before optimization, the surface roughness is less than 13.57% before optimization, and the oxygen content of the surface is less than 10.89% before optimization.
It can be seen from fig. 8 that the optimized shape quality is slightly better than that before the optimization, most regions leak metal luster, and only individual black spots are regions where the anodic oxide film is not completely removed. Fig. 9 is an analysis diagram of the optimization results.
TABLE 7 comparison table before and after optimization of laser cleaning experiment
Figure BDA0003651290770000092
The above description is a preferred embodiment of the present invention, and is not intended to limit the present invention, and those skilled in the art may modify the above technical solutions or substitute some technical features of the above technical solutions. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A method for modeling energy consumption and optimizing process parameters in a laser cleaning process is characterized by comprising the following steps:
s1, establishing an energy consumption model of the laser cleaning process based on energy consumption analysis of the laser system, the robot system, the cooling system and the dust removal system;
s2, building a power real-time monitoring platform to obtain energy consumption model test parameters;
s3, establishing an energy consumption-oriented laser cleaning process parameter optimization model by considering energy efficiency, surface roughness and surface oxygen content factors;
s4, solving a laser cleaning process parameter optimization model based on an improved lion group optimization algorithm;
and S5, example analysis.
2. The method for modeling energy consumption and optimizing process parameters in a laser cleaning process according to claim 1, wherein the step S1 comprises the steps of:
s6, constructing a total equipment energy consumption model: e T ＝E l +E r +E a In the formula: e T Total energy consumption for laser cleaning equipment; e l Energy consumption of the laser system; e r Energy consumption for the robot system; e a Energy consumption for the auxiliary system;
s7, constructing a laser system energy consumption model:
Figure FDA0003651290760000011
in the formula: p is lw Standby power for the laser system; e l Energy consumption of the laser system; p lc The operating power of the laser; p l Is the output power of the laser; t is t l The laser system is operated;
s8, constructing a robot system energy consumption model:
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 Standby power for the robot; p rwo Operating power for the robot; t is t r The robot runs time; v. of r Is the robot travel speed; l is the cleaning length;
S9、constructing an energy consumption model of an auxiliary system: e a ＝E co +E ep In the formula E co Energy consumption for cooling the system; e ep For dedusting system energy consumption, the cooling system energy consumption can be expressed as:
Figure FDA0003651290760000012
in the formula P cw Standby power for the cooling system; p cwo Operating power for the cooling system; t is t t Total time for cooling system operation; eta is the efficiency of the cooling system for absorbing heat; rho is the density of the cooling water; v is the cooling water flow rate; c is the specific heat capacity of cooling water, Delta T is the temperature difference of the cooling water, and the energy consumption of the dust removal system can be expressed as follows: e ep ＝P epw ×(t t -t ep )+P epwo ×t ep In the formula P epw And P epwo Respectively stand-by power and running power of the dust removal system, t ep The running time of the dust removal system;
s10, laser cleaning process total energy consumption model:
Figure FDA0003651290760000021
3. the method for modeling energy consumption and optimizing process parameters in a laser cleaning process according to claim 1 or 2, wherein the step S2 comprises the steps of:
s11, building a power real-time monitoring platform based on laser cleaning equipment;
s12, obtaining the power of the laser in the running state by data fitting: p lc 683.4+2.685 × n × f, where n is the single pulse energy and f is the pulse frequency;
s13, acquiring power parameters of the robot system;
s14, obtaining a power parameter value of the cooling system;
and S15, obtaining the working power value of the dust removal system.
4. The method for modeling energy consumption and optimizing process parameters in a laser cleaning process according to claim 3, wherein the step S3 comprises the steps of:
s16, establishing an energy consumption objective function based on the energy consumption model
Figure FDA0003651290760000022
S17, establishing an energy efficiency function
Figure FDA0003651290760000023
S18 fitting surface roughness function based on experimental data
R a ＝6.535-0.1292n-0.021f+0.00067n 2 ；
S19 fitting 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, establishing a multi-objective optimization function model
F(n,f,v r )＝{minE,minR a ,minW to ,maxη}
Figure FDA0003651290760000031
5. The method for modeling energy consumption and optimizing process parameters in a laser cleaning process according to claim 4, wherein the step S4 comprises the steps of:
s21, performing multi-objective optimization based on the improved lion group optimization algorithm;
s22, selecting the optimal solution based on the entropy weight-TOPSIS method.
CN202210543768.8A 2022-05-19 2022-05-19 Energy consumption modeling and process parameter optimization method in laser cleaning process Pending CN115016260A (en)

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