CN113059186A - Low-carbon modeling and process parameter optimization method in laser additive manufacturing process - Google Patents
Low-carbon modeling and process parameter optimization method in laser additive manufacturing process Download PDFInfo
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- CN113059186A CN113059186A CN202110295074.2A CN202110295074A CN113059186A CN 113059186 A CN113059186 A CN 113059186A CN 202110295074 A CN202110295074 A CN 202110295074A CN 113059186 A CN113059186 A CN 113059186A
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Abstract
The invention relates to a low-carbon modeling and process parameter optimization method in a laser additive manufacturing process, and belongs to the technical field of advanced manufacturing and automation. The method of the invention comprises the following steps: establishing a laser additive manufacturing carbon footprint model based on the carbon footprint analysis of the laser generator, the cooling subsystem, the powder feeding subsystem, the feeding subsystem and the auxiliary subsystem; building a power real-time monitoring platform to obtain carbon footprint model test parameters; constructing a carbon emission-oriented laser additive manufacturing process parameter optimization model by considering the elements of cladding quality and cladding cost; solving a laser additive manufacturing process parameter optimization model based on an artificial fish swarm algorithm; example analysis. The method is simple and practical, fully considers the powder utilization rate and the cladding quality during the mold building, and provides good support for the optimization of carbon emission in the laser additive manufacturing process.
Description
Technical Field
The invention relates to a low-carbon modeling and process parameter optimization method in a laser additive manufacturing process, and belongs to the technical field of advanced manufacturing and automation.
Background
With the increasing global demand for climate change, carbon peaking, and carbon neutralization have become a global focus of attention. China ranks carbon peak-reaching and carbon neutral as one of eight key tasks of 2021 central economic work. The fourteen-five period is the key period for realizing carbon emission peak reaching in China, and the manufacturing industry is the main field of carbon emission in China and accounts for about 80% of the total emission in China, so the manufacturing industry is bound to become a main battlefield for carbon peak reaching and carbon neutralization. Laser additive manufacturing is used as a key technology for competitive development of intelligent manufacturing in various countries in the world, and is widely applied to the field of manufacturing of high-end equipment such as aerospace, ships and the like in China at present. The principle is that the metal powder which is coaxially conveyed is rapidly melted/solidified by using high-energy laser beams, so that the material is directly formed by stacking layer by layer. The process is long in time, and the electric energy is not completely converted into laser beams to generate a large amount of carbon emission, so that the method becomes one of main carbon emission sources in the manufacturing process of high-end equipment in China. Therefore, research on modeling and optimization of laser additive manufacturing carbon emission has important engineering significance for realizing carbon peak reaching and carbon neutralization in the laser additive manufacturing equipment industry in China.
Disclosure of Invention
Aiming at the problems, the invention develops a low-carbon modeling and process parameter optimization method in the laser additive manufacturing process, analyzes the carbon emission mechanism and characteristics of each subsystem in the laser additive manufacturing process, and establishes a carbon emission comprehensive model in the laser additive manufacturing process. On the basis, a laser additive manufacturing process parameter multi-objective optimization model with carbon emission, powder utilization rate and cladding quality as targets is established, an artificial fish swarm algorithm is proposed to solve, the optimal process parameters are obtained, and effectiveness and feasibility of the model are verified through laser additive manufacturing experimental cases.
The invention discloses a low-carbon modeling and process parameter optimization method in a laser additive manufacturing process, which comprises the following steps of:
s1, establishing a laser additive manufacturing carbon footprint model based on the carbon footprint analysis of the laser generator, the cooling subsystem, the powder feeding subsystem, the feeding subsystem and the auxiliary subsystem; building a power real-time monitoring platform to obtain carbon footprint model test parameters;
s2, building a power real-time monitoring platform to obtain carbon footprint model test parameters;
s3, constructing a carbon emission-oriented laser additive manufacturing process parameter optimization model by considering the elements of cladding quality and cladding cost;
s4, solving a laser additive manufacturing process parameter optimization model based on an artificial fish school algorithm;
and S5, example analysis.
Preferably, the step S1 includes the following sub-steps:
s11, constructing a standby time function Ts＝Ti+Tp+Tg，
In the formula, TiThe laser interval time; t ispPreparing time for the early stage; t isgThe powder feeder delay time.
S12, constructing a time mathematical function of the cladding process as
In the formula, l is cladding length; d is the diameter of the light spot; s is the cladding width of the matrix; alpha is the lap joint rate; n is the number of cladding layers; vsIs the scanning speed.
S13, constructing the working time function of the cooling subsystem as
In the formula, vkThe cooling water flow rate; c is the specific heat capacity of cooling water; rho is the density of the cooling water; delta T is the cooling water temperature difference; plmWorking power of a laser generator subsystem is provided; plinThe power is input for the laser.
S14, establishing a carbon footprint model C of the laser generator systeml＝(Pls*(Ts-Ti)+Plm*Tm+Pli*Ti)*CeIn the formula, PlsSpacing power for the laser generator subsystem; pliThe power is the power of the laser generator subsystem in a standby state.
S15 carbon footprint model C of powder feeding subsystemp＝(Pps*Ts+Ppm*Tm)*CeIn the formula, PpsIs the power of the powder feeding subsystem in a standby state; ppmIs the working state power of the powder feeding subsystem.
S16 feed subsystem carbon footprint model Cm＝(Pms*Ts+Pmm*Tm)*Ce
In the formula, PmsFor machine standby power, PmmAnd the power of the machine tool in the working state.
S17, the cooling subsystem is used as an independent subsystem and is not influenced by laser input power, and the cooling subsystem carbon footprint model Cc＝(Pcs*(Ttotal-Ti)+Pcm*Tc)*Ce，
In the formula, PpsPower for cooling subsystem standby state; ppmAnd cooling the subsystem working state power.
S18 auxiliary subsystem carbon footprint model
In the formula, n is the number of auxiliary systems; piAuxiliary system working power; n is a radical ofiIn order to assist the switching function of the system,
s19 laser additive manufacturing process total carbon emission model
Preferably, the step S2 includes the following sub-steps:
s21, building a real-time monitoring platform;
s22, fitting data to obtain a mathematical relation P between the laser input power and the laser output powerlm＝3.189*Plin+22.86；
S23 fitting data to obtain a power variation function of scanning speed as Pmm＝0.008*Vs+52.78；
S24, obtaining a power parameter value of the powder feeding subsystem;
s25, obtaining the working power value of the cooling subsystem;
preferably, the step S3 includes the following sub-steps:
s31, establishing a powder utilization function
In the formula, M1The quality after cladding; m2The quality before cladding.
S32 quality objective function
In the formula, W1Single pass cladding width; h1The height of single cladding.
S33 fitting powder utilization function based on experimental data
S34 fitting aspect ratio function based on experimental data
S35, establishing a multi-objective optimization function model
In the formula, PminlinInputting minimum power P for laser equipmentmaxlinInputting a maximum power for the laser device; vsminFor the minimum allowable scanning speed, V, of the lasersmaxThe highest scanning speed of the laser is set; vfminIs the lowest powder feeding speed V of the powder feederfmaxThe maximum powder feeding speed of the powder feeder.
Preferably, the step S4 includes the following sub-steps:
s41, performing multi-objective optimization based on an artificial fish school algorithm;
and S42, selecting an optimal solution based on entropy weight-gray correlation analysis.
The invention has the beneficial effects that: the low-carbon modeling and process parameter optimization method in the laser additive manufacturing process analyzes the carbon emission mechanism and characteristics of each subsystem in the laser additive manufacturing process and establishes a carbon emission comprehensive model in the laser additive manufacturing process. On the basis, a laser additive manufacturing process parameter multi-objective optimization model with carbon emission, powder utilization rate and cladding quality as targets is established, an artificial fish swarm algorithm is proposed to solve, the optimal process parameters are obtained, and effectiveness and feasibility of the model are verified through laser additive manufacturing experimental cases. The research on low-carbon modeling and process parameter optimization in the laser additive manufacturing process has important engineering significance for the wide application of the laser additive technology in the manufacturing industry.
Drawings
Fig. 1 is a power characteristic graph of a laser additive manufacturing process.
Fig. 2 is a graph of a laser additive process carbon footprint boundary.
Fig. 3 is a laser additive manufacturing process power monitoring platform.
Fig. 4 is a graph of laser interlayer spacing power.
Fig. 5 is a graph of feed system power change.
FIG. 6 is a power state change curve diagram of the powder feeding subsystem.
FIG. 7 is a graph of cooling subsystem power variation at 500 power.
FIG. 8 is a graph of cooling subsystem power change at 600 power.
FIG. 9 is a flow chart of an artificial fish school algorithm.
FIG. 10 is a comparison graph of the appearance of a molded part of an article (a is an empirical graph, and b is an optimization graph).
Fig. 11 is an analysis diagram of the optimization results.
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 low-carbon modeling and process parameter optimization method in a laser additive manufacturing process, and a power characteristic curve diagram in the laser additive manufacturing process is shown in figure 1. Fig. 2 is a graph of a laser additive process carbon footprint boundary. Fig. 3 is a laser additive manufacturing process power monitoring platform. Fig. 4 is a graph of laser interlayer spacing power. Fig. 5 is a graph of feed system power change. FIG. 6 is a power state change curve diagram of the powder feeding subsystem. FIG. 7 is a graph of cooling subsystem power variation at 500 power. FIG. 8 is a graph of cooling subsystem power change at 600 power. FIG. 9 is a flow chart of an artificial fish school algorithm. As shown in fig. 1 to 9, a laser additive manufacturing process power monitoring platform, a laser additive manufacturing process carbon footprint boundary diagram, an artificial fish swarm algorithm flow diagram, a laser additive manufacturing process power characteristic curve, laser interlayer spacing power, a feeding system power change curve, a powder feeding subsystem power state change curve, a cooling subsystem power change at 500 powers, and a cooling subsystem power change at 600 powers in a laser additive manufacturing process low-carbon modeling and process parameter optimization method according to the present invention are shown.
The overall technical scheme of the invention is that the low-carbon modeling and process parameter optimization method in the laser additive manufacturing process comprises the following steps:
s1, establishing a laser additive manufacturing carbon footprint model based on the carbon footprint analysis of the laser generator, the cooling subsystem, the powder feeding subsystem, the feeding subsystem and the auxiliary subsystem; building a power real-time monitoring platform to obtain carbon footprint model test parameters;
s2, building a power real-time monitoring platform to obtain carbon footprint model test parameters;
s3, constructing a carbon emission-oriented laser additive manufacturing process parameter optimization model by considering the elements of cladding quality and cladding cost;
s4, solving a laser additive manufacturing process parameter optimization model based on an artificial fish school algorithm;
and S5, example analysis.
The step S1 includes the following sub-steps:
s11, constructing a standby time function Ts＝Ti+Tp+Tg，
In the formula, TiThe laser interval time; t ispPreparing time for the early stage; t isgThe powder feeder delay time.
S12, constructing a time mathematical function of the cladding process as
In the formula, l is cladding length; d is the diameter of the light spot; s is the cladding width of the matrix; alpha is the lap joint rate; n is the number of cladding layers; vsIs the scanning speed.
S13, constructing the working time function of the cooling subsystem as
In the formula, vkThe cooling water flow rate; c is the specific heat capacity of cooling water; rho is the density of the cooling water; delta T is the cooling water temperature difference; plmWorking power of a laser generator subsystem is provided; plinThe power is input for the laser.
S14, establishing a carbon footprint model C of the laser generator systeml＝(Pls*(Ts-Ti)+Plm*Tm+Pli*Ti)*CeIn the formula, PlsSpacing power for the laser generator subsystem; pliThe power is the power of the laser generator subsystem in a standby state.
S15 carbon footprint model C of powder feeding subsystem shown in FIG. 6p＝(Pps*Ts+Ppm*Tm)*Ce，
In the formula, PpsIs the power of the powder feeding subsystem in a standby state; ppmIs the working state power of the powder feeding subsystem.
S16, constructing a feed subsystem carbon footprint model C from FIG. 5m＝(Pms*Ts+Pmm*Tm)*Ce
In the formula, PmsFor machine standby power, PmmAnd the power of the machine tool in the working state.
S17, constructing the cooling subsystem from the graph of FIG. 7 and the graph of FIG. 8 as an independent subsystem without being influenced by the laser input power, and cooling the carbon footprint model C of the subsystemc＝(Pcs*(Ttotal-Ti)+Pcm*Tc)*Ce，
In the formula, PpsPower for cooling subsystem standby state; ppmAnd cooling the subsystem working state power.
S18 auxiliary subsystem carbon footprint model
In the formula, n is the number of auxiliary systems; piAuxiliary system working power; n is a radical ofiIn order to assist the switching function of the system,
s19 laser additive manufacturing process total carbon emission model
The step S2 includes the following sub-steps:
s21, building a real-time monitoring platform as shown in the figure 3;
s22, obtaining a mathematical relation P between the laser input power and the laser output power by fitting the data in the table 1lm＝3.189*Plin+22.86；
TABLE 1 laser generator subsystem power variation Table
S23, fitting the power variation function of the scanning speed into P from the data in the table 2mm＝0.008*Vs+52.78；
TABLE 2 feed system Power Change Table
S24, obtaining a power parameter value of the powder feeding subsystem;
s25, obtaining the power value of the cooling subsystem;
s26, acquiring the power value of the auxiliary subsystem;
s27, constructing carbon emission mathematical model in laser cladding process based on table 3, table 4 and table 5
Table 3 electric energy carbon emission factor table corresponding to each regional electric network
TABLE 4 Power parameter Table
TABLE 5 other parameters table of device
The step S3 includes the following sub-steps:
s31, establishing a powder utilization function
In the formula, M1The quality after cladding; m2The quality before cladding.
S32 quality objective function
In the formula, W1Single pass cladding width; h1The height of single cladding.
S33 fitting powder utilization function based on experimental data table 6
S34 fitting aspect ratio function based on experimental data table 6
TABLE 6 recording table of the weld height, weld width and gram of powder used for the cladding layer
S35, establishing a multi-objective optimization function model
In the formula, PminlinInput minimum power 300w PmaxlinInputting a maximum power of 1000w for the laser equipment; vsminThe minimum allowable scanning speed of the laser is 300mm/min, VsmaxThe maximum scanning speed of the laser is 450 mm/min; vfminThe minimum scanning speed of the powder feeder is 3.7g/min, VfmaxThe step S4 for the maximum scanning speed of the powder feeder of 15.7g/min includes the following substeps:
s41, performing multi-objective optimization based on the artificial fish school algorithm as shown in figure 9;
s42, selecting an optimal solution table 7 based on entropy weight-gray correlation analysis.
TABLE 7 selection of optimal solution for entropy weight-Grey correlation analysis
And S5, example analysis.
The 20mm 15mm 10 formed part cladding experiment is carried out, the process parameters after the optimization of the table 7 are carried out, the formed part cladding experiment is carried out, and the carbon emission is lower than the empirical value of 16 percent, the powder utilization rate is higher than the empirical value of 11 percent, and the width-to-height ratio is optimized to be 2 percent, and the best cladding quality is achieved (when the width-to-height ratio is close to 4.5).
It can be seen from fig. 10 that the appearance quality of the optimized formed part b is significantly better than that of the empirical formed part a, and based on the above analysis, the accuracy of the established model and the optimized process parameters are verified again, so that the carbon emission in the cladding process can be effectively reduced, the powder utilization rate is improved, and the cladding quality is improved. Fig. 11 is an analysis diagram of the optimization results.
Table 8 comparison table before and after optimization of cladding experiment
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 low-carbon modeling and process parameter optimization method in a laser additive manufacturing process is characterized by comprising the following steps:
s1, establishing a laser additive manufacturing carbon footprint model based on the carbon footprint analysis of the laser generator, the cooling subsystem, the powder feeding subsystem, the feeding subsystem and the auxiliary subsystem; building a power real-time monitoring platform to obtain carbon footprint model test parameters;
s2, building a power real-time monitoring platform to obtain carbon footprint model test parameters;
s3, constructing a carbon emission-oriented laser additive manufacturing process parameter optimization model by considering the elements of cladding quality and cladding cost;
s4, solving a laser additive manufacturing process parameter optimization model based on an artificial fish school algorithm;
and S5, example analysis.
2. The method for low carbon modeling and process parameter optimization in a laser additive manufacturing process of claim 1, wherein step S1 includes the sub-steps of:
s11, constructing a standby time function Ts＝Ti+Tp+Tg，
In the formula, TiThe laser interval time; t ispPreparing time for the early stage; t isgIs the powder feeder delay time;
s12, constructing a time mathematical function of the cladding process as
In the formula, l is cladding length; d is the diameter of the light spot; s is the cladding width of the matrix; alpha is the lap joint rate; n is the number of cladding layers; vsIs the scanning speed;
s13, constructing the working time function of the cooling subsystem as
In the formula, vkThe cooling water flow rate; c is the specific heat capacity of cooling water; rho is the density of the cooling water; delta T is the cooling water temperature difference; plmWorking power of a laser generator subsystem is provided; plinInputting power for the laser;
s14, establishing a carbon footprint model C of the laser generator systeml＝(Pls*(Ts-Ti)+Plm*Tm+Pli*Ti)*Ce，
In the formula, PlsSpacing power for the laser generator subsystem; pliThe power is the power of the laser generator subsystem in a standby state;
s15 carbon footprint model C of powder feeding subsystemp＝(Pps*Ts+Ppm*Tm)*Ce，
In the formula, PpsIs the power of the powder feeding subsystem in a standby state; ppmIs the working state power of the powder feeding subsystem;
s16 feed subsystem carbon footprint model Cm＝(Pms*Ts+Pmm*Tm)*Ce，
In the formula, PmsFor machine standby power, PmmThe power of the machine tool in the working state;
s17, the cooling subsystem is used as an independent subsystem and is not influenced by laser input power, and the cooling subsystem carbon footprint model Cc＝(Pcs*(Ttotal-Ti)+Pcm*Tc)*Ce，
In the formula, PpsPower for cooling subsystem standby state; ppmPower for cooling subsystem operating state;
s18 auxiliary subsystem carbon footprint model
In the formula, n is the number of auxiliary systems; piAuxiliary system working power; n is a radical ofiIn order to assist the switching function of the system,
s19 laser additive manufacturing process total carbon emission model
3. The method for low carbon modeling and process parameter optimization in a laser additive manufacturing process according to claim 1 or 2, wherein step S2 includes the sub-steps of:
s21, building a power real-time monitoring platform;
s22, fitting data to obtain a mathematical relation P between the laser input power and the laser output powerlm＝3.189*Plin+22.86；
S23 fitting data to obtain a power variation function of scanning speed as Pmm＝0.008*Vs+52.78；
S24, obtaining a power parameter value of the powder feeding subsystem;
and S25, obtaining the working power value of the cooling subsystem.
4. The method for low carbon modeling and process parameter optimization in a laser additive manufacturing process of claim 3, wherein step S3 includes the sub-steps of:
s31, establishing a powder utilization function
In the formula, M1The quality after cladding; m2The mass before cladding;
s32 quality objective function
In the formula, W1Single pass cladding width; h1The single-pass cladding height;
s33 fitting powder utilization function based on experimental data
S34 fitting aspect ratio function based on experimental data
S35, establishing a multi-objective optimization function model
In the formula, PminlinInputting minimum power P for laser equipmentmaxlinInputting a maximum power for the laser device; vsminFor the minimum allowable scanning speed, V, of the lasersmaxThe highest scanning speed of the laser is set; vfminIs the lowest powder feeding speed V of the powder feederfmaxThe maximum powder feeding speed of the powder feeder.
5. The method for low carbon modeling and process parameter optimization in a laser additive manufacturing process of claim 4, wherein step S4 includes the sub-steps of:
s41, performing multi-objective optimization based on an artificial fish school algorithm;
and S42, selecting an optimal solution based on entropy weight-gray correlation analysis.
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