CN117408143A - Carbon emission monitoring platform and process optimization method for laser shock peening process - Google Patents
Carbon emission monitoring platform and process optimization method for laser shock peening process Download PDFInfo
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Abstract
The invention relates to a carbon emission monitoring platform and a process optimization method in a laser shock peening process, and belongs to the technical field of advanced manufacturing and automation. Which comprises the following steps: on the basis of defining a carbon emission boundary in the laser shock peening process, constructing a laser cleaning carbon emission model based on a shock peening mechanism of laser shock peening equipment by constructing a carbon emission monitoring platform; constructing a laser shock peening process parameter multi-objective optimization model facing to a low-carbon process; solving a laser shock reinforcement process parameter optimization model based on a cube chaos and crisscross strategy multi-objective white whale optimization algorithm; example analysis. The method is practical and simple, and can obviously reduce carbon emission in the laser shock strengthening process on the basis of ensuring shock strengthening quality.
Description
Technical Field
The invention relates to a carbon emission monitoring platform and a process optimization method in a laser shock peening process, and belongs to the technical field of advanced manufacturing and automation.
Background
The laser shock peening technology is widely applied to high-end industries such as aerospace, military processing, ships, medical treatment and the like as a high-new technology to prolong the service life of the target material and improve the surface strength of the target material. The laser shock reinforcement utilizes plasma shock waves generated by strong laser beams, and the mechanical effect of the plasma shock waves is represented by the fact that the surface of the material obtains higher residual compressive stress due to the reaction of materials around an impact area, so that the fatigue resistance, wear resistance and corrosion resistance of the metal material are improved. In the processing process, the incomplete conversion of electric energy used by the laser shock peening equipment and the incomplete absorption of laser heat by the target material can greatly increase the carbon emission. Therefore, the reduction of carbon emission in the laser shock peening process has important significance for reducing carbon emission in the manufacturing industry in China.
Disclosure of Invention
Aiming at the problems that carbon footprint is difficult to analyze, carbon emission evaluation is difficult and the like caused by complex carbon emission sources in the laser shock strengthening process, on the basis of defining the carbon emission boundary in the laser shock strengthening process, key process parameters in the processing process are obtained by constructing a carbon emission monitoring platform in the laser shock strengthening process, and then a carbon emission comprehensive model in the laser shock strengthening process is constructed, a multi-objective white whale optimization algorithm based on cube chaos and crisscross strategy is designed to solve the established model; the effectiveness and feasibility of the proposed model are verified through laser shock peening experimental cases. By optimizing the process parameters, the carbon emission in the laser shock strengthening process is reduced, the greenhouse gas emission is reduced, the laser shock strengthening quality is improved, and the development targets of high quality and low carbon in the laser shock strengthening process are further realized.
The invention discloses a carbon emission process optimization method in a laser shock peening process, which comprises the following steps of:
s1: establishing a laser shock strengthening carbon emission model based on a laser shock strengthening mechanism;
s2: setting up a carbon emission monitoring platform in the laser shock strengthening process, carrying out a carbon emission model parameter acquisition test of each subsystem on the laser shock strengthening equipment, and analyzing test results;
s3: constructing a low-carbon-oriented multi-objective optimization model of carbon emission and process parameters in the laser shock peening process;
s4: solving a laser shock reinforcement process parameter model based on a cube chaos and crisscross strategy multi-objective white whale optimization algorithm;
s5: and (5) verifying experiments.
According to the invention: step S1 comprises the following sub-steps:
s11: construction of a laser cleaning Process time model T total ＝T d +T w ，
Wherein: t (T) total Indicating the total laser shock peening time; t (T) d Indicating the standby time of the laser shock equipment, T w The working time of the laser shock peening equipment is shown.
In the laser shock strengthening process, the laser generator subsystem, the feeding subsystem and the water delivery subsystem are simultaneously opened and closed, so that the working time and the standby time of the three subsystems are the same. In the laser shock strengthening process, in order to reduce energy consumption and carbon emission, a serpentine track is adopted to shock strengthen the target material in the test process, and the lap ratio is set as alpha, so that the laser shock strengthening time function is obtained as follows:
wherein T is laser shock peening time; l is the length of the substitute machining workpiece; d is the width of the workpiece to be machined, r is the diameter of the light spot, alpha is the overlap ratio, and v is the moving speed of the light spot.
S12: the cooling subsystem is used as an independent subsystem, the temperature is adjusted according to the actual test condition, and the necessary connection and interaction between the cooling subsystem and other subsystems are not needed, so that the time function of the cooling subsystem needs to be calculated independently. The working time of the cooling subsystem is as follows
Wherein: t (T) C The working time of the cooling subsystem; tw is the working time of the laser equipment; p (P) i Inputting power for laser; p (P) O For laser output power ρ l Is the density of the cooling liquid; t (T) h The highest temperature of the cooling liquid; t (T) l C, at the minimum temperature of the cooling liquid l Specific heat capacity of the cooling liquid; v is the coolant flow rate.
S13: construction of laser Generator subsystem carbon emission model C L ＝(P w ·T w +P d ·T d )·F e ，
Wherein: c (C) L Carbon emission for the laser generator subsystem; p (P) w Working power for the laser generator; t (T) w The working time of the laser generator is as follows; p (P) d Standby power for the laser generator subsystem; t (T) d Standby time for the laser generator subsystem; f (F) e Is the carbon emission factor of the power grid;
construction of feed subsystem carbon emission model
Wherein C is R Carbon emissions for the feed subsystem; p (P) sms Power for the working state of the feeding subsystem; d is the feeding distance; t is the working time of the feeding subsystem; p (P) hs Is power in standby state; t is t a Total operating time for the feed subsystem; t is t w Working time for the feeding subsystem; f (F) e Is the carbon emission factor of the power grid;
s15: construction of Cooling subsystem carbon emission model C c ＝(P w ·T w +P d ·T d )·F e
Wherein C is c Carbon emissions for the cooling subsystem; p (P) W Power for the working state of the cooling subsystem; t (T) w The working time of the cooling subsystem; p (P) d Standby state power for cooling subsystem; t (T) d Standby time for the cooling subsystem; f (F) e Is the carbon emission factor of the power grid;
s16: construction of Water delivery subsystem carbon emission model C s ＝(P w ·T w +P m ·T m )·F e ，
Wherein C is s Carbon emission for the water cooling subsystem; p (P) w Working power of the water cooling subsystem; t (T) w The working time of the water cooling subsystem is as follows; p (P) m Standby power for the water cooling subsystem; t (T) m The standby time of the water cooling subsystem is; f (F) e Is the carbon emission factor of the power grid;
s17: construction of auxiliary subsystem carbon emission model
Wherein: c (C) a Carbon emissions for auxiliary subsystems; n (N) i Starting the number of auxiliary subsystems; p (P) i Working power for the auxiliary subsystem; t (T) m The working time of the auxiliary subsystem is as follows; f (F) e Is the carbon emission factor of the power grid;
s18: in summary, the total carbon emission model of the laser cleaning process obtained by the arrangement is as follows:
according to the invention: step S2 comprises the following sub-steps:
s21: building a carbon emission monitoring platform in the laser shock peening process;
s22: after analysis based on the test data, fitting the data to obtain a functional relation P in =347n+267.5; wherein P is in The output power of the laser generator is given, and n is the laser energy value;
s23: acquiring a power parameter value of a feeding subsystem;
s24: acquiring a power parameter value of a cooling subsystem;
s25: acquiring a power parameter value of a water supply subsystem;
s26: and acquiring the power parameter value of the auxiliary subsystem.
According to the invention: step S3 comprises the following sub-steps:
s31: based on Matlab 2020 software, data fitting is carried out on main technological parameters in test data, and a microhardness function is constructed:
wherein H is v Is vickers hardness; e (E) L Is the laser energy, P w Power for the laser device; t is the working time of the laser equipment;
s32: based on Matlab 2020 software, data fitting is carried out on main process parameters in test data, and a residual stress function is constructed:
wherein F is r Is the residual stress value; e (E) L Is the laser energy, P w Power for the laser device; t is the working time of the laser equipment;
s33: establishing a multi-objective optimization model function
F(Q out ,t ws )＝min{C E },max{Hv c },{F R }
In which Q out Output energy for laser, t ws For the laser operating time, C E Is the total carbon emission in the laser shock strengthening process, hv c Is of Vickers hardness number, F R For residual stress value, Q max For the maximum single output value of the laser, Q min Is the minimum single laser output value of the laser, t wmax For maximum working time of laser, t wmin Hv is the minimum operating time of the laser MAX The maximum Vickers hardness value of the target material is shown, and f (x) is the laser shock strengthening frequency.
According to the invention: step S4 comprises the following sub-steps:
s41: performing multi-objective optimization based on a cube chaos and crisscross strategy multi-objective white whale optimization algorithm;
s42: and analyzing and selecting an optimal solution based on an entropy weight method-gray correlation analysis and evaluation method.
The invention has the beneficial effects that: according to the invention, the laser shock strengthening carbon emission model is constructed on the basis of defining the laser shock strengthening carbon emission mechanism, a certain data support and supplement can be provided for research of laser shock strengthening carbon footprint analysis, carbon emission in the laser shock strengthening process is reduced, greenhouse gas emission is reduced, laser shock strengthening quality is improved, and further the high-quality and low-carbon targets in the laser shock strengthening process are realized, so that a certain thought and reference are provided for future students to research the laser shock strengthening carbon footprint analysis.
Drawings
FIG. 1 is a graph showing the power curve of the laser shock peening process of the present invention.
FIG. 2 is a graph of carbon emission boundaries during laser shock peening according to the present invention.
Fig. 3 is a diagram of a carbon emission monitoring platform in the laser shock peening process of the present invention.
Fig. 4a-4d are graphs of cooling subsystem power variations of the present invention.
Fig. 5 is a graph of feed subsystem power variation of the present invention.
Fig. 6 is a flowchart of a multi-objective beluga optimization algorithm based on the cube chaos and crisscross strategy of the present invention.
Fig. 7a-7c are graphs comparing before and after optimization, wherein fig. 7a is unreinforced, fig. 7b is an empirical value, and fig. 7c is an optimized value.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, but it should be understood that the examples are intended to illustrate the invention and not to limit the invention.
The invention develops a carbon emission monitoring platform and a process optimization method in a laser shock peening process. Fig. 1 is a graph showing a power curve of a laser shock peening process. Fig. 2 is a graph of carbon emission boundaries during laser shock peening. Fig. 3 is a diagram of a carbon emission monitoring platform structure in the laser shock peening process. Fig. 4a-4d are graphs of cooling subsystem power variations. Fig. 5 is a graph of feed subsystem power variation. Fig. 6 is a flowchart of a multi-objective beluga optimization algorithm based on the cube chaos and crisscross strategies. Fig. 7a-7c are graphs comparing before and after optimization.
1-7c, a laser shock strengthening process carbon emission monitoring platform and a process optimization method of the invention are shown, wherein the laser shock strengthening process carbon emission monitoring platform, the laser shock strengthening process carbon emission boundary, the laser shock strengthening process carbon emission monitoring platform, the cooling system power variation, the feeding system power variation, the multi-objective white whale optimization algorithm flow chart based on the cube chaos and the crisscross strategy and the comparison chart before and after the laser shock strengthening optimization are shown.
The whole technical scheme of the invention is a carbon emission process optimization method in a laser shock peening process, which comprises the following steps:
s1: constructing a laser shock strengthening model based on a laser shock strengthening process and carbon emission mechanism analysis;
s2: building a carbon emission monitoring platform in the laser shock peening process, and obtaining carbon emission model parameters;
s3: constructing a low-carbon-oriented laser shock peening process parameter and carbon emission mapping relation model;
s4: model solving based on a cube chaos and crisscross strategy multi-objective white whale optimization algorithm;
s5: and (3) performing laser shock peening low-carbon process experiment verification.
The step S1 comprises the following sub-steps:
s11: construction of a laser cleaning Process time model T total ＝T d +T w ，
Wherein: t (T) total Indicating the total laser shock peening time; t (T) d Indicating the standby time of the laser shock equipment, T w The working time of the laser shock peening equipment is shown.
S12: the cooling subsystem acts as a stand-alone subsystem, the time function of which needs to be calculated separately. The working time of the cooling subsystem is as follows
Wherein: t (T) C The working time of the cooling subsystem; p (P) i Inputting power for laser; p (P) O For laser output power ρ l Is the density of the cooling liquid; t (T) h The highest temperature of the cooling liquid; t (T) l C, at the minimum temperature of the cooling liquid l Specific heat capacity of the cooling liquid; v is the coolant flow rate.
S13: construction of laser Generator subsystem carbon emission model C L ＝(P w ·T w +P d ·T d )·F e ，
Wherein: c (C) L Carbon emission for the laser generator subsystem; p (P) d Standby power for the laser generator subsystem; p (P) w Is the working power of the laser generator.
S14: construction of feed subsystem carbon emission model
Wherein C is R Carbon emissions for the feed subsystem; p (P) t Power for the working state of the feeding subsystem; p (P) s Is power in standby state.
S15: construction of Cooling subsystem carbon emission model C c ＝(P w ·T w +P d ·T d )·F e ，
Wherein C is c To cool subsystem carbon emissions, P w To cool the subsystem operating state power, P d Is standby state power.
S16: construction of Water delivery subsystem carbon emission model C s ＝(P w ·T w +P m ·T m )·F e ，
Wherein C is s Carbon emission for the water cooling subsystem; p (P) w Working power of the water cooling subsystem; p (P) m Standby power for the water cooling subsystem.
S17: construction of auxiliary subsystem carbon emission model
Wherein: c (C) a Carbon emissions for auxiliary subsystems; p (P) i Operating power for the auxiliary subsystem.
S18: in summary, the total carbon emission model of the laser cleaning process obtained by the arrangement is as follows:
the step S2 comprises the following sub-steps:
s21: building a carbon emission monitoring platform in the laser shock peening process;
s22: after analysis based on the test data, a linear relationship between the single laser intensification energy and the laser generator is obtained from table 1, and a fitted functional relationship formula P is obtained in ＝347n+267.5；
Table 1 laser subsystem operating state power variation
S23: acquiring a power parameter value of a feeding subsystem;
s24: acquiring a power parameter value of a cooling subsystem;
s25: acquiring a power parameter value of a water supply subsystem;
s26: and acquiring the power parameter value of the auxiliary subsystem.
S27: based on tables 3 and 4, a mathematical model of carbon emission in the laser shock peening process is constructed:
TABLE 3 electric energy carbon emission factor meter for each area
Table 4 power parameter table
The step S3 comprises the following sub-steps:
s31: based on Matlab 2020 software, data fitting is carried out on main technological parameters in test data, and a microhardness function is constructed:
wherein H is v Is vickers hardness; e (E) L Is the laser energy, P w Power for the laser device; t is the working time of the laser equipment;
s32: based on Matlab 2020 software, data fitting is carried out on main process parameters in test data, and a residual stress function is constructed:
wherein F is r Is the residual stress value; e (E) L Is the laser energy, P w Power for the laser device; t is the working time of the laser equipment;
table 5 microhardness measurements
TABLE 6 residual stress measurement
S33: establishing a multi-objective optimization function
F(Q out ,t ws )＝min{C E },max{Hv c },{F R }
In which Q out Output energy for laser, t ws For the laser operating time, C E Is the total carbon emission in the laser shock strengthening process, hv c Is of Vickers hardness number, F R For residual stress value, Q max For the maximum single output value of the laser, Q min Is the minimum single laser output value of the laser, t wmax For maximum working time of laser, t wmin Hv is the minimum operating time of the laser MAX The maximum Vickers hardness value of the target material is shown, and f (x) is the laser shock strengthening frequency.
The step S4 comprises the following sub-steps:
s41: multi-objective optimization is performed based on the cube chaos and the crisscross strategy multi-objective white whale optimization algorithm as shown in fig. 6;
s42: the optimal solution is analyzed and selected based on the entropy weight method-gray correlation analysis and evaluation method as shown in table 7.
Table 7 pareto solution set selection
The step S5 comprises the following sub-steps:
and (3) carrying out experimental verification on the obtained optimized result by adopting a 20mm multiplied by 4mmTa15 titanium alloy target, detecting by adopting a TP1606 multichannel power detector in the experimental process, and outputting the data after the experiment to a computer.
Table 8 laser shock peening optimization front-to-back contrast
The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention, and those skilled in the art may still make modifications to the above technical solutions or make equivalent substitutions of some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. 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 carbon emission monitoring platform and a process optimization method for a laser shock peening process are characterized by comprising the following steps:
s1: establishing a laser shock strengthening carbon emission model based on a laser shock strengthening mechanism;
s2: setting up a carbon emission monitoring platform in the laser shock strengthening process, carrying out a carbon emission model parameter acquisition test of each subsystem on the laser shock strengthening equipment, and analyzing test results;
s3: constructing a low-carbon-oriented multi-objective optimization model of carbon emission and process parameters in the laser shock peening process;
s4: solving a laser shock reinforcement process parameter model based on a cube chaos and crisscross strategy multi-objective white whale optimization algorithm;
s5: and (5) verifying experiments.
2. The carbon emission monitoring platform and process optimization method for laser shock peening process according to claim 1, wherein step S1 comprises the sub-steps of:
s11: construction of a laser cleaning Process time model T total ＝T d +T w ，
Wherein: t (T) total Indicating the total laser shock peening time; t (T) d Indicating the standby time of the laser shock equipment, T w Indicating the working time of the laser shock peening equipment;
in the laser shock strengthening process, the laser generator subsystem, the feeding subsystem and the water delivery subsystem are simultaneously opened and closed, so that the working time and the standby time of the three subsystems are the same. In the laser shock strengthening process, in order to reduce energy consumption and carbon emission, a serpentine track is adopted to shock strengthen the target material in the test process, and the lap ratio is set as alpha, so that the laser shock strengthening time function is obtained as follows:
wherein T is laser shock peening time; l is the length of the substitute machining workpiece; d is the width of the workpiece to be machined, r is the diameter of the light spot, alpha is the lap joint rate, and v is the moving speed of the light spot;
s12: the cooling subsystem is used as an independent subsystem, the time function of the cooling subsystem needs to be calculated independently, and the working time of the cooling subsystem is as follows
Wherein: t (T) C The working time of the cooling subsystem; tw is the working time of the laser equipment; p (P) i Inputting power for laser; p (P) O For laser output power ρ l Is the density of the cooling liquid; t (T) h The highest temperature of the cooling liquid; t (T) l C, at the minimum temperature of the cooling liquid l Specific heat capacity of the cooling liquid; v is the cooling liquid flow rate;
s13: construction of laser Generator subsystem carbon emission model C L ＝(P w ·T w +P d ·T d )·F e ，
Wherein: c (C) L Carbon emission for the laser generator subsystem; p (P) w Working power for the laser generator; t (T) w The working time of the laser generator is as follows; p (P) d Standby power for the laser generator subsystem; t (T) d Standby time for the laser generator subsystem; f (F) e Is the carbon emission factor of the power grid;
s14: construction of feed subsystem carbon emission model
Wherein C is R Carbon emissions for the feed subsystem; p (P) sms Power for the working state of the feeding subsystem; d is the feeding distance; t is the working time of the feeding subsystem; p (P) hs Is power in standby state; t is t a Total operating time for the feed subsystem; t is t w Working time for the feeding subsystem; f (F) e Is the carbon emission factor of the power grid;
s15: construction of Cooling subsystem carbon emission model C c ＝(P w ·T w +P d ·T d )·F e ，
Wherein C is c Carbon emissions for the cooling subsystem; p (P) W Power for the working state of the cooling subsystem; t (T) w The working time of the cooling subsystem; p (P) d Standby state power for cooling subsystem; t (T) d Standby time for the cooling subsystem; f (F) e Is the carbon emission factor of the power grid;
s16: construction of Water delivery subsystem carbon emission model C s ＝(P w ·T w +P m ·T m )·F e ，
Wherein C is s Carbon emission for the water cooling subsystem; p (P) w Working power of the water cooling subsystem; t (T) w The working time of the water cooling subsystem is as follows; p (P) m Standby power for the water cooling subsystem; t (T) m The standby time of the water cooling subsystem is; f (F) e Is the carbon emission factor of the power grid;
s17: construction of auxiliary subsystem carbon emission model
Wherein: c (C) a Carbon emissions for auxiliary subsystems; n (N) i Starting the number of auxiliary subsystems; p (P) i Working power for the auxiliary subsystem; t (T) m The working time of the auxiliary subsystem is as follows; f (F) e Is the carbon emission factor of the power grid;
s18: in summary, the total carbon emission model of the laser cleaning process obtained by the arrangement is as follows:
3. the carbon emission monitoring platform and the process optimization method for the laser shock peening process according to claim 1, wherein said step S2 comprises the sub-steps of:
s21: building a carbon emission monitoring platform in the laser shock peening process;
s22: after analysis based on the test data, fitting the data to obtain a functional relation P in =347n+267.5; wherein P is in The output power of the laser generator is given, and n is the laser energy value;
s23: acquiring a power parameter value of a feeding subsystem;
s24: acquiring a power parameter value of a cooling subsystem;
s25: acquiring a power parameter value of a water supply subsystem;
s26: and acquiring the power parameter value of the auxiliary subsystem.
4. The carbon emission monitoring platform and the process optimization method for the laser shock peening process according to claim 1, wherein said step S3 comprises the sub-steps of:
s31: based on Matlab 2020 software, data fitting is carried out on main technological parameters in test data, and a microhardness function is constructed:
wherein H is v Is vickers hardness; e (E) L Is the laser energy, P w Power for the laser device; t is the working time of the laser equipment;
s32: based on Matlab 2020 software, data fitting is carried out on main process parameters in test data, and a residual stress function is constructed:
wherein F is r Is the residual stress value; e (E) L Is the laser energy, P w Power for the laser device; t is the working time of the laser equipment;
s33: establishing a multi-objective optimization model function
F(Q out ,t ws )＝min{C E },max{Hv c },{F R }
In which Q out Output energy for laser, t ws For the laser operating time, C E Is the total carbon emission in the laser shock strengthening process, hv c Is of Vickers hardness number, F R For residual stress value, Q max For the maximum single output value of the laser, Q min Is the minimum single laser output value of the laser, t wmax For maximum working time of laser, t wmin Hv is the minimum operating time of the laser MAX The maximum Vickers hardness value of the target material is shown, and f (x) is the laser shock strengthening frequency.
5. The carbon emission monitoring platform and the process optimization method for the laser shock peening process according to claim 1, wherein said step S4 comprises the sub-steps of:
s41: performing multi-objective optimization based on a cube chaos and crisscross strategy multi-objective white whale optimization algorithm;
s42: and analyzing and selecting an optimal solution based on an entropy weight method-gray correlation analysis and evaluation method.
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