CN108319223A - A kind of thread turning process parameter optimizing method of Oriented Green manufacture - Google Patents
A kind of thread turning process parameter optimizing method of Oriented Green manufacture Download PDFInfo
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Abstract
The invention discloses a kind of thread turning process parameter optimizing methods of Oriented Green manufacture, include the following steps：1) optimized variable is determined：Cutting speed Vc, amount of feeding f；2) multi-goal optimizing function is established；3) constraints is determined；4) object function is optimized, obtains its Pareto optimal solution set, 5) by determining that method obtains the weight of each objective attribute target attribute based on the combining weights of AHP and RS, the optimal solution for meeting actual processing demand is obtained from the optimal solution set of step 4.The method disclosed in the present in application process, can the first parameter in model determines according to actual conditions, then model is solved using the above method, so obtain carbon emission amount, noise emission, dust emission synthesis it is optimal when cutting speed value Vc, amount of feeding f, eventually by control machinery process equipment come realize reduce carbon emission, noise emission, dust emission purpose.
Description
Technical Field
The invention relates to the technical field of machining, in particular to a thread turning process parameter optimization method for green manufacturing.
Background
In the production process of machining, the selection of cutting amount directly influences the product quality, the production efficiency and the like of a processed product. The reasonable selection of the cutting amount has important significance for improving the production efficiency and reducing the production cost. At present, the selection of cutting amount mainly depends on experience or relevant data consulted, but the cutting amount selected according to the method is usually not optimal, which causes low production efficiency, resource waste, huge environmental discharge and the like.
At present, a plurality of new technologies relating to the optimization of thread cutting parameters exist at home and abroad. However, most of the prior art techniques are optimized for cost, time, etc. to select appropriate machining parameters. Moreover, the prior art is rarely concerned with methods for optimizing process parameters that integrate the process environment and environmental effects such as carbon emissions, dust emissions and noise emissions during the process.
Disclosure of Invention
The invention aims to make up for the defects of the prior art and provides a thread turning process parameter optimization method for green manufacturing.
The invention is realized by the following technical scheme:
a thread turning process parameter optimization method facing green manufacturing comprises the following steps: (1) determining an optimization variable: cutting speed V in thread turningcAnd a feed amount f;
(2) establishing a multi-objective optimization function;
(3) determining a constraint condition;
(4) optimizing the objective function to obtain a Pareto optimal solution set of the objective function;
(5) and (4) obtaining the weight of each objective function by a combined weight determination method based on the analytic hierarchy process AHP and the rough set theory RS, and obtaining the optimal solution from the optimal solution set in the step (4).
The establishment of the multi-objective optimization function in the step (2) specifically comprises the following steps:
1) establishing a multi-objective optimization function containing carbon emission, machine tool noise and dust emission;
wherein the carbon emission function model is as follows:
Cp＝Ce+Ct+Cc，
wherein, CpRepresenting thread turning carbon emissions, contains three parts: ceIndicating the carbon emission of the turning energy consumption, Ce＝Pe·EeIn the formula PeRepresents the carbon emission factor of the electric energy with the unit of kgCO2/kWh，EeRepresenting the electrical energy consumed by the turning process, in whichIn the formula tpIndicates the preparation time tctIndicating the time of single tool change, tmDenotes the cutting time, TtIndicating tool life, PuDenotes the no-load power, PeIndicating tool change power, PcRepresenting the load power, PaRepresenting the load loss power; ctIndicating that the tool is consuming carbon emissions,in the formula ftDenotes the carbon emission factor, m, of the tooltRepresenting the mass of the tool; ccIndicating the consumption of carbon emissions by the cutting fluid,in the formula TcShowing the cutting fluid replacement period, VfIndicates the amount of cutting fluid used,Indicating the concentration of the cutting fluid (letters are corrected in the formula);
the machine noise function model is as follows:
representing a machine tool noise function by a black box function VE (x) VE (n, f), and analyzing and predicting by using a radial basis function GRNN, wherein x is a set of cutting speed and feed amount in an actual machining process, f represents feed amount, and n is the number of thread lines;
the dust particle function model is as follows:
wherein D isuExpressed as mass of PM2.5 dust particles to mass of swarf,
wherein A represents a scale factor, βmaxrepresenting the maximum value of the division coefficient, β representing the division coefficient, βcRepresenting a segmentation critical value; rarepresents the roughness [. eta. ]sRepresenting a segmentation density; v0Is a reference cutting speed; vc represents the cutting speed; eARepresenting the energy state of the dust particles;wherein alpha represents a tilt angle, phi represents a shear angle, and FcRepresents the cutting force; k represents the cutting edge angle of the cutter; a ispIndicating the depth of cut; f represents the feed amount in turning; delta denotes a parameter related to the type of material, as shown below,
3) determining a constraint condition:
wherein,for cutting speed constraint, where nmin，nmaxThe lowest and highest rotating speeds of the main shaft of the machine tool are respectively;
0.2r/min≤f1the feed amount range during the external circle turning is not less than 0.4r/min, and f is not less than 0.1r/min2The feed amount range when the thread is turned is not more than 0.2 r/min;for the constraint of processing quality, where rεThe radius of the arc of the tool nose of the tool; rmaxThe maximum allowable value of the surface roughness of the part;for the purpose of power constraint, where η represents the effective coefficient of machine tool power, PmaxMaximum effective cutting power for the machine tool;for cutting force restraint, wherein FmaxIndicating the maximum feed force.
And (4) optimizing the objective function to obtain a Pareto optimal solution set, specifically, optimizing the objective function by using NSGA-II based on a Pareto method to obtain the Pareto optimal solution set.
The invention optimally controls the feeding amount and the cutting speed of the machining equipment so as to achieve the aim of reducing carbon emission, dust emission and noise emission. Most machining equipment relates to the feeding amount and the cutting speed, so that the method disclosed by the invention can be applied to most machining equipment, such as a milling machine, a lathe and the like.
Carbon emissions from machining processes include carbon emissions resulting from the consumption of energy and materials. The energy consumption is divided into direct consumption and indirect consumption, and the direct energy consumption comprises energy consumption consumed by producing materials and processing products; indirect energy consumption includes energy consumed to maintain stable environmental conditions in the manufacturing system, since indirect consumption is not correlated well with process parameters, material carbon emissions include carbon emissions from material consumption of tools, cutting fluids, and tool materials, where carbon emissions from raw blank consumption and carbon emissions from scrap recycling are not correlated well with variables optimized herein and are therefore not considered.
In conclusion, the carbon emission in the thread cutting process is set as follows: f. simply by solving the above model to obtain CpAnd the corresponding machining parameters are adopted to control the machining equipment at the minimum, so that the aim of reducing the carbon emission can be fulfilled.
Said C ise、CtAnd CcThe composition of (1).
1. Carbon emissions from electrical energy
Said C ise＝Pe·EeIn the formula, PeCarbon emission factor, unit: (kgCO2/kwh), EeRepresents the electric energy consumption of the processing process, and the unit: (kwh).
1) Carbon emission factor P for electrical energye：
Different power grids correspond to different carbon emission factors, China development and improvement committee can publish the data of the carbon emission factors of several power grids in China every year in response to climate change, and Table 1 shows the published carbon emission factors of the several power grids in China 2010-2012, which are weighted and averaged in three years. The carbon emission factor is not a variable to be optimized by the technical idea of the present invention, and for the sake of simplicity, the average value 0.9740 of the several grid emission factors in table 1 is used as the power carbon emission factor PeCan be adjusted according to different regional and official update data.
TABLE 1 electric energy carbon emission factor of each region
2) Electric energy consumption in the machining process EeIs determined
The electric energy consumption of the turning process comprises no-load state energy consumption and load state energy consumption. The power of the machine tool in the idle state is called idle power PuResearch shows that additional load loss power P is generated when one machine tool is in a no-load state to a load stateaAnd load power PcWhen the machine tool changes the tool, the main shaft stops rotating, and the power at the time is called tool changing power Pe. The energy balance equation in the dynamic running of the machine tool can be obtained according to the power balance equation in the dynamic running of the machine tool as follows:
the total input power P and the no-load power P are generated when the same machine tool runs at a fixed speed and under a certain load in a steady state in the machining processuLoad power PcLoad loss power PaThe fluctuation is small and can be regarded as a constant value, so the total energy consumption can be expressed as
2. Carbon emission for cutter use
The carbon emission used by the tool refers to the apportionment of the carbon emission produced by the tool used in the cutting process during its manufacture over each process step, without taking into account the carbon emission directly caused by the use of the tool. The tool life T is determined by the fact that the tool may contain multiple regrinding operations during the actual machining processtIs represented as follows:
Tt＝(N+1)T，
wherein, TtThe tool life is shown, N is the number of regrinding operations, and T is the tool durability. The tool carbon loss emissions are therefore as follows:
wherein, CtRepresents the carbon emission of the tool loss, tmDenotes the cutting time, TtIndicates the life of the tool, mtIndicating the mass of the tool, ftRepresenting the carbon emission factor of the tool.
3. Carbon emission in use of cutting fluid
In the case of dry cutting, this fraction is 0.
The cutting fluid comprises water-based cutting fluid and oil-based cutting fluid, and the most common water-based cutting fluid is adopted in modeling. The water-based cutting fluid is formed by mixing mineral oil and a large amount of water. Research shows that the carbon emission factor of the mineral oil in the cutting fluid is 2.85kgco2L, the content of mineral oil in the waste cutting fluid is very low, and the carbon emission factor treated by waste water can be used for replacing the carbon emission factor treated by the waste cutting fluid, and the carbon emission factor treated by the waste water is 0.2kgco2And L. The cutting fluid carbon loss emissions are as follows:
wherein, TcIndicating the cutting fluid replacement period, VfThe amount of the cutting fluid used is indicated,the cutting fluid concentration is shown.
Establishing machine tool noise function model
Cutting speed VcThe feed amount f and the noise are in a nonlinear relation, and no accurate functional formula can be clearly expressed, and the black box function is expressed as follows in the invention:
VE (x) is VE (n, f), wherein x is the set of cutting speed and feed in the actual machining process, f represents the feed, n is the number of threads, a highly parallel radial basis function neural network GRNN is adopted for analysis and prediction, the GRNN is a variation form of the highly parallel radial basis function neural network, the nonlinear mapping capability and flexibility are high, the same prediction effect as a BP network can be obtained only by 1.0% of sample size, other parameters except for a smoothing parameter are not required to be adjusted in the training process, and the influence of artificial subjective assumption on the prediction result is reduced.
Establishing a dust particle function model:
wherein D isuExpressed as mass of PM2.5 dust particles to mass of swarf,
wherein A represents a scale factor, βmaxrepresenting the maximum value of the division coefficient, β representing the division coefficient, βcRepresenting a segmentation critical value; rarepresents the roughness [. eta. ]sRepresenting a segmentation density; v0Is a reference cutting speed; vc represents the cutting speed; eARepresenting the energy state of the dust particles;wherein alpha represents a tilt angle, phi represents a shear angle, and FcRepresents the cutting force; k represents the cutting edge angle of the cutter; a ispIndicating the depth of cut; f represents the feed amount in turning; delta represents a parameter related to the material, as shown in table 2,
TABLE 2 Delta parameter Table of materials
In general, the value of the cutting parameter is limited by the conditions of the spindle speed, the feed amount, the maximum cutting force, the maximum cutting power, the machining quality and the like of the selected machine tool equipment, and the value can be obtained only in the range of meeting the limiting conditions. Therefore, the present invention also needs to determine the constraint:
1) cutting speed constraint (i.e. spindle speed constraint)
In actual operation, the cutting speed of the machine tool must be within the range allowed by the machine tool, namely:
in the formula nmin，nmaxThe minimum and maximum rotating speeds of the main shaft of the machine tool are respectively;
2) feed amount constraint
The feed rate must be at the minimum feed rate f allowed by the machine toolminAnd a maximum feed amount fmaxIn the meantime. Namely:
0.2r/min≤f1less than or equal to 0.4r/min, which is the feed amount range during the excircle turning,
0.1r/min≤f2not more than 0.2r/min, which is the feed amount range when the screw thread is turned;
3) constraint of machining quality
In the formula, rεThe radius of the arc of the tool nose of the tool; rmaxThe maximum allowable value of the surface roughness of the part;
4) constraint of machining power
The machine power should be less than the specified maximum available cutting power. Namely:
in the formula, eta represents the effective coefficient of machine tool power, PmaxMaximum effective cutting power for the machine tool;
5) restriction of cutting force
During the turning process, the cutting feed force cannot exceed the maximum feed force allowed by the main shaft of the machine tool, namely:
in the formula, FmaxRepresents the maximum feed force; kFfIndicating cutting force correction
Coefficient, CFf，xFf，yFf，nFfIndicating the coefficients relating to the material of the workpiece and the cutting conditions, to be consulted
The cutting amount manual is obtained.
The mathematical model of the invention is a typical multi-objective optimization problem under multi-constraint conditions, and for the multi-objective optimization problem, because objective functions are often restricted with each other, it is difficult to realize the optimal solution by all the objectives under most conditions. Selecting NSGA-II (second generation non-dominated genetic algorithm with elite strategy) based on a Pareto method to solve to obtain a Pareto optimal solution set of the multi-target multi-constraint problem;
according to the actual processing environment, an optimal solution is obtained from a Pareto optimal solution set, the weights of all objective functions are determined by a comprehensive weighting method based on hierarchical Analysis (AHP) and rough set theory (RS), and therefore the choice of the Pareto optimal solution is completed, and the AHP-RS combined weight determination method is as follows:
1. carrying out vector normalization on the Pareto optimal solution set to obtain a normalized decision matrix B ═ (B)ij)n×mWherein n is the number of Pareto solution sets, and m is the number of evaluation indexes;
2. determining subjective weight of each evaluation index by using analytic hierarchy process (lambda ═ lambda-1,λ2,λ3,…,λm)；
3. Determined according to the theory of rough setObjective weight μ ═ μ (μ) of each evaluation index1,μ2,μ3,…,μm)；
4. Combining subjective weight and objective weight by establishing Lagrange function, and waiting until the combined weight w is equal to (w)1,w2,w3,…,wm). The Lagrange function is as follows:
the invention has the advantages that: in the application process of the method disclosed by the invention, parameters in the model can be determined according to actual conditions, then the model is solved by adopting the method, further the cutting speed value Vc and the feeding amount f at the comprehensive optimal time of carbon emission, noise emission and dust emission are obtained, and finally the aim of reducing the carbon emission, the noise emission and the dust emission is realized by controlling mechanical processing equipment.
Drawings
Fig. 1 is a GRNN structural diagram.
FIG. 2 is a flow chart of NSGA-II operation.
FIG. 3 is a process of multi-objective optimization solution optimization.
Detailed Description
A thread turning process parameter optimization method facing green manufacturing comprises the following steps: (1) determining an optimization variable: cutting speed V in thread turningcAnd a feed amount f;
(2) establishing a multi-objective optimization function;
(3) determining a constraint condition;
(4) optimizing the objective function to obtain a Pareto optimal solution set thereof, as shown in fig. 3;
(5) and (4) obtaining the weight of each objective function by a combined weight determination method based on the analytic hierarchy process AHP and the rough set theory RS, and obtaining the optimal solution from the optimal solution set in the step (4).
The establishment of the multi-objective optimization function in the step (2) specifically comprises the following steps:
1) establishing a multi-objective optimization function containing carbon emission, machine tool noise and dust emission;
wherein the carbon emission function model is as follows:
Cp＝Ce+Ct+Cc，
wherein, CpRepresenting thread turning carbon emissions, contains three parts: ceIndicating the carbon emission of the turning energy consumption, Ce＝Pe·EeIn the formula PeRepresents the carbon emission factor of the electric energy with the unit of kgCO2/kWh，EeRepresenting the electrical energy consumed by the turning process, in whichIn the formula tpIndicates the preparation time tctIndicating the time of single tool change, tmDenotes the cutting time, TtIndicating tool life, PuDenotes the no-load power, PeIndicating tool change power, PcRepresenting the load power, PaRepresenting the load loss power; ctIndicating that the tool is consuming carbon emissions,in the formula ftDenotes the carbon emission factor, m, of the tooltRepresenting the mass of the tool; ccIndicating the consumption of carbon emissions by the cutting fluid,in the formula TcShowing the cutting fluid replacement period, VfIndicates the amount of cutting fluid used,Represents the concentration of the cutting fluid; (the letters in the formula have been corrected)
The machine noise function model is as follows:
as shown in fig. 1, a machine tool noise function is represented by a black box function VE (x) ═ VE (n, f), and is analyzed and predicted by using a radial basis function GRNN, where x is a set of cutting speed and feed amount in an actual machining process, f is feed amount, and n is the number of threads;
the dust particle function model is as follows:
wherein D isuExpressed as mass of PM2.5 dust particles to mass of swarf,
wherein A represents a scale factor, βmaxrepresenting the maximum value of the division coefficient, β representing the division coefficient, βcRepresenting a segmentation critical value; rarepresents the roughness [. eta. ]sRepresenting a segmentation density; v0Is a reference cutting speed; vcRepresents the cutting speed; eARepresenting the energy state of the dust particles;wherein alpha represents a tilt angle, phi represents a shear angle, and FcRepresents the cutting force; k represents the cutting edge angle of the cutter; a ispIndicating the depth of cut; f represents the feed amount in turning; delta denotes a parameter related to the type of material, as shown below,
3) determining a constraint condition:
wherein,for cutting speed constraint, where nmin，nmaxThe minimum and maximum rotating speeds of the main shaft of the machine tool are respectively;
0.2r/min≤f1the feed amount range during the external circle turning is not less than 0.4r/min, and f is not less than 0.1r/min2The feed amount range when the thread is turned is not more than 0.2 r/min;for the constraint of processing quality, where rεThe radius of the arc of the tool nose of the tool; rmaxThe maximum allowable value of the surface roughness of the part;for the purpose of power constraint, where η represents the effective coefficient of machine tool power, PmaxMaximum effective cutting power for the machine tool;for cutting force restraint, wherein FmaxIndicating the maximum feed force.
As shown in fig. 2, the objective function is optimized in step (4) to obtain a Pareto optimal solution set thereof, specifically, the objective function is optimized by using NSGA-II based on the Pareto method to obtain the Pareto optimal solution set.
The tool material of the embodiment is 45 steel bars, and the machining quality requirement RbNo more than 6.4 microns, a main deflection angle of the tool of 70 degrees, a front angle of 20 degrees, a blade inclination angle of 5 degrees, the tool is a hard alloy turning tool, and the circular arc radius r of the tool tipε0.8mm, back bite when turning outer circle and thread1mm and 0.5mm are respectively selected, the selected machine tool specification parameters are shown in a table 3, the thread parameters are shown in a table 4, the correlation coefficients of the tool life and the cutting force are shown in a table 5, other calculation related parameters are shown in a table 6, dust emission related parameters are shown in a table 7, related index weight values are shown in a table 8, and related optimal solution corresponding parameters are shown in a table 9:
TABLE 3 machine tool Specification parameters
TABLE 4 thread parameters
TABLE 5 tool parameters and cutting force coefficient table
TABLE 6 table of calculation-related parameters
TABLE 7 dust emission related parameters
TABLE 8 index weight values
TABLE 9 optimal solution correspondence parameter Table
Claims (7)
1. A thread turning process parameter optimization method for green manufacturing is characterized in that: the method comprises the following steps: (1) determining an optimization variable: cutting speed V in thread turningcAnd a feed amount f;
(2) establishing a multi-objective optimization function;
(3) determining a constraint condition;
(4) optimizing the objective function to obtain a Pareto optimal solution set of the objective function;
(5) and (4) obtaining the weight of each objective function by a combined weight determination method based on the analytic hierarchy process AHP and the rough set theory RS, and obtaining the optimal solution from the optimal solution set in the step (4).
2. The thread turning process parameter optimization method for green manufacturing according to claim 1, wherein: and (3) establishing a multi-objective optimization function in the step (2), wherein the multi-objective optimization function comprises a carbon emission function, a machine tool noise function and a dust emission function.
3. The thread turning process parameter optimization method for green manufacturing according to claim 2, wherein: the carbon emission function model is as follows:
Cp＝Ce+Ct+Cc，
wherein, CpRepresenting thread turning carbon emissions, contains three parts: ceIndicating the carbon emission of the turning energy consumption, Ce＝Pe·EeIn the formula PeRepresents the carbon emission factor of the electric energy with the unit of kgCO2/kWh，EeRepresenting the electrical energy consumed by the turning process, in whichIn the formula tpIndicates the preparation time tctIndicating the time of single tool change, tmDenotes the cutting time, TtIndicating tool life, PuDenotes the no-load power, PeIndicating tool change power, PcRepresenting the load power, PaRepresenting the load loss power; ctIndicating that the tool is consuming carbon emissions,in the formula ftDenotes the carbon emission factor, m, of the tooltRepresenting the mass of the tool; ccIndicating the consumption of carbon emissions by the cutting fluid,in the formula TcIndicating a cutting fluid replacement cycle、VfIndicates the amount of cutting fluid used,The cutting fluid concentration is shown.
4. The thread turning process parameter optimization method for green manufacturing according to claim 3, wherein: the machine tool noise function model is as follows:
and (3) representing a machine tool noise function by a black box function VE (x) which is a set of cutting speed and feed amount in the actual machining process, representing the feed amount by a radial basis neural network GRNN, and performing analysis and prediction by using a black box function VE (x) which is a machine tool noise function VE (n, f) which is a thread number.
5. The thread turning process parameter optimization method for green manufacturing according to claim 4, wherein: the dust particle function model is as follows:
wherein D isuRepresents the mass ratio of dust particles to swarf in PM 2.5;indicating the scrap mass when cutting the outer circle;representing the mass ratio of dust particles to chips when cutting the outer circle;indicating the scrap quality of the cut thread;representing the ratio of the mass of dust particles to the mass of cut when cutting a thread;
wherein A represents a scale factor, βmaxrepresenting the maximum value of the division coefficient, β representing the division coefficient, βcRepresenting a segmentation critical value; rarepresents the roughness [. eta. ]sRepresenting a segmentation density; v0Is a reference cutting speed; vc represents the cutting speed; eARepresenting the energy state of the dust particles;wherein alpha represents a tilt angle, phi represents a shear angle, and FcRepresents the cutting force; k represents the cutting edge angle of the cutter; a ispIndicating the depth of cut; f represents the feed amount in turning; delta represents a parameter relating to the type of material, delta.gtoreq.1 in the case of a ductile material and 0.5 in the case of a semi-ductile material<δ<1, if it is a brittle material, 0<δ<0.5。
6. The thread turning process parameter optimization method for green manufacturing according to claim 5, wherein: the determination constraint conditions in the step (3) are as follows:
wherein,for cutting speed constraint, where dwIndicating the diameter of the blank; n isminAnd nmaxRespectively representing the lowest and highest rotating speeds of a machine tool spindle;
0.2r/min≤f1the feed amount range during the external circle turning is not less than 0.4r/min, and f is not less than 0.1r/min2The feed amount range when the thread is turned is not more than 0.2 r/min;for the constraint of processing quality, where rεF represents the feed amount during turning, wherein f is the arc radius of the tool nose of the tool; rmaxThe maximum allowable value of the surface roughness of the part;for process power constraint, where FcRepresents the cutting force; vcrepresenting the cutting speed, η representing the effective coefficient of machine tool power, PmaxMaximum effective cutting power for the machine tool;for cutting force restraint, in the formula CFf，xFf，yFf，nFfRepresenting coefficients relating to the workpiece material and cutting conditions; a ispIndicating the depth of cut; kFfRepresents a cutting force correction coefficient; fmaxIndicating the maximum feed force.
7. The thread turning process parameter optimization method for green manufacturing according to claim 1, wherein: and (4) optimizing the objective function to obtain a Pareto optimal solution set, specifically, optimizing the objective function by using NSGA-II based on a Pareto method to obtain the Pareto optimal solution set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201810117933.7A CN108319223A (en) | 2018-02-06 | 2018-02-06 | A kind of thread turning process parameter optimizing method of Oriented Green manufacture |
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| CN113240096A (en) * | 2021-06-07 | 2021-08-10 | 北京理工大学 | Casting cylinder cover micro-structure prediction method based on rough set and neural network |
| CN114415595A (en) * | 2021-11-05 | 2022-04-29 | 山东科技大学 | Turning optimization method and system, computer equipment and storage medium |
| CN116047921A (en) * | 2023-04-03 | 2023-05-02 | 成都飞机工业（集团）有限责任公司 | Cutting parameter optimization method, device, equipment and medium |
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| CN116974241A (en) * | 2023-07-10 | 2023-10-31 | 清华大学 | Geometric optimization method and device for numerical control machine tool for green low-carbon manufacturing |
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| CN109002006A (en) * | 2018-09-13 | 2018-12-14 | 合肥工业大学 | Processing route optimization method based on the constraint of low-carbon low cost |
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| CN109901512A (en) * | 2019-01-08 | 2019-06-18 | 韶能集团韶关宏大齿轮有限公司 | One kind being based on the standardized turning hour norm method of machined parameters |
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| CN112380760B (en) * | 2020-10-13 | 2023-01-31 | 重庆大学 | Multi-algorithm fusion based multi-target process parameter intelligent optimization method |
| CN112380760A (en) * | 2020-10-13 | 2021-02-19 | 重庆大学 | Multi-algorithm fusion based multi-target process parameter intelligent optimization method |
| CN113240096A (en) * | 2021-06-07 | 2021-08-10 | 北京理工大学 | Casting cylinder cover micro-structure prediction method based on rough set and neural network |
| CN114415595A (en) * | 2021-11-05 | 2022-04-29 | 山东科技大学 | Turning optimization method and system, computer equipment and storage medium |
| CN114415595B (en) * | 2021-11-05 | 2024-05-10 | 山东科技大学 | Turning optimization method, system, computer equipment and storage medium |
| CN116047921A (en) * | 2023-04-03 | 2023-05-02 | 成都飞机工业（集团）有限责任公司 | Cutting parameter optimization method, device, equipment and medium |
| CN116047921B (en) * | 2023-04-03 | 2023-08-04 | 成都飞机工业（集团）有限责任公司 | Cutting parameter optimization method, device, equipment and medium |
| CN116203891A (en) * | 2023-04-28 | 2023-06-02 | 深圳华龙讯达信息技术股份有限公司 | Automatic control decision optimization method and system based on PLC |
| CN116203891B (en) * | 2023-04-28 | 2023-08-04 | 深圳华龙讯达信息技术股份有限公司 | Automatic control decision optimization method and system based on PLC |
| CN116974241A (en) * | 2023-07-10 | 2023-10-31 | 清华大学 | Geometric optimization method and device for numerical control machine tool for green low-carbon manufacturing |
| CN116974241B (en) * | 2023-07-10 | 2024-02-06 | 清华大学 | Geometric optimization method and device for numerical control machine tool for green low-carbon manufacturing |
| CN117930787A (en) * | 2024-03-21 | 2024-04-26 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
| CN117930787B (en) * | 2024-03-21 | 2024-06-11 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
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