CN112861433B - Product low-carbon design method based on multi-level integrated framework - Google Patents
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Abstract
The invention relates to a product low-carbon design method based on a multi-level integrated frame, which comprises the following steps of: establishing a multi-level integrated frame consisting of a design characteristic layer, a processing process layer, a processing characteristic layer, an operation characteristic layer and a carbon emission characteristic layer with a mapping relation; establishing a carbon emission model and a time model according to the proposed integrated framework; establishing a dual-target optimization model according to the carbon emission model and the time model; performing multi-level coding according to the division of different characteristics of the product, and performing iterative optimization on the design scheme by using a differential evolution algorithm according to a dual-objective optimization model; and analyzing the pareto solution set obtained by optimization to obtain an optimal low-carbon design scheme. The carbon emission of the product is reduced, and the influence on the surrounding environment is reduced.
Description
Technical Field
The invention relates to the technical field of machining, in particular to a method for low-carbon design of a product based on a multi-level integrated frame.
Background
The statements herein merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The emission of greenhouse gases (GHG), especially carbon dioxide, is a major cause of climate change and may disrupt the ecological balance. Industry, as a major source of carbon emissions, is urgently required to take action to reduce the carbon emissions of its products. Therefore, the design and development of low carbon products are critical to mitigating climate change. Since most of the carbon emissions of the product are determined at the design stage, it is an urgent requirement in the industry to quantify the carbon emissions and reduce the emission according to the design information.
The important point of carbon emission quantification in the design phase is to establish a correlation mechanism between design information, processing technology and carbon emission. Computer Aided Process Planning (CAPP) is a key link for associating design information with a processing technology, and in recent years, the CAPP is widely and mature applied, however, the inventor finds that the current research is only limited to a process scheme aiming at reducing production cost or improving production efficiency, and neglects carbon emission. The second correlation is to quantify the carbon emission generated by the processing technology in the design stage, but most of the current research evaluates the carbon emission from the manufacturing point of view, and the carbon emission is difficult to be combined with the product information in the design stage.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method for designing a product with low carbon based on a multi-level integrated frame, so as to obtain an optimal low carbon design scheme and reduce the influence of the product on the environment.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a method for low-carbon design of a product based on a multi-level integrated frame, which comprises the following steps:
establishing a multi-level integrated frame consisting of a design characteristic layer, a processing process layer, a processing characteristic layer, an operation characteristic layer and a carbon emission characteristic layer with a mapping relation;
establishing a carbon emission model and a time model according to the proposed integrated framework;
establishing a dual-target optimization model according to the carbon emission model and the time model;
performing multi-level coding according to the division of different characteristics of the product, and performing iterative optimization on the design scheme by using a differential evolution algorithm according to a dual-objective optimization model;
and analyzing the pareto solution set obtained by optimization to obtain an optimal low-carbon design scheme.
Further, the processing characteristic layer is an information set composed of shape information, size information, tolerance information and material information of the workpiece to be processed.
Further, the operation characteristic layer is an information set consisting of the type of the processing machine tool, the processing auxiliary material and the cutting parameter.
Further, a carbon emission model is obtained according to the energy consumption model and the auxiliary material consumption model.
Further, the dual-target optimization model comprises a carbon emission target optimization model and a time target optimization model, the carbon emission target optimization model is obtained according to the carbon emission model, and the time target optimization model is obtained according to the time model.
Further, the differential evolution algorithm comprises the following specific steps:
initializing a population;
step (2) coding variables of the design characteristic layer, the processing process layer, the processing characteristic layer, the operation characteristic layer and the carbon emission characteristic layer;
step (3) calculating carbon emission and completion time according to the double-target optimization model;
step (4) optimizing the carbon emission and completion time obtained in the step (3) by adopting genetic operation to obtain an optimized design scheme;
step (5) repeatedly executing the step (2) to the step (4) until all the set termination conditions are met;
and (6) outputting a plurality of optimized design schemes.
Further, the specific steps of the step (4) are as follows:
randomly selecting a set number of individuals from a population to perform mutation operation;
performing cross operation between the variant population after the variant operation and the original population;
selecting operation is carried out by adopting a greedy algorithm;
further, the specific method of selecting operation is: and when the pareto grade of the tested individual after the cross operation is higher than that of the original individual, the tested individual replaces the original individual, otherwise, the original individual is kept.
Further, a pareto solution set of a plurality of optimized design schemes is obtained according to a differential evolution algorithm, fuzzy positive ideal solutions and fuzzy negative ideal solutions are determined according to the pareto solution set results, then the distance between the pareto results and the fuzzy positive ideal solutions and the fuzzy negative ideal solutions is calculated by adopting Euclidean distances, and the weight of carbon emission and time is calculated to obtain the optimal design scheme.
Further, after the weight of carbon emission and time is obtained by an entropy weight method, an optimal design scheme of the workpiece is obtained by a fuzzy TOPS I S decision method.
The invention has the beneficial effects that:
according to the method, a multi-level integrated frame, a carbon emission model and a time model which take the incidence mapping relation of each level into consideration are constructed, a dual-target optimization model is established, an optimal design scheme is obtained by utilizing a differential evolution algorithm, the carbon emission is quantized in a design stage to obtain the optimal design scheme, the product design information and the carbon emission are combined in the design stage, the low-carbon design of the product is realized, the carbon emission of the product is reduced, and the influence on the surrounding environment is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flowchart of a method of example 1 of the present invention;
FIG. 2 is a schematic view of an integrated framework according to embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of modeling a carbon emission model according to example 1 of the present invention;
FIG. 4 is a schematic flowchart of a differential evolution algorithm in embodiment 1 of the present invention;
FIG. 5 is a schematic diagram of multi-layer coding according to embodiment 1 of the present invention;
FIG. 6 is a schematic view of a flange in an application case of embodiment 1 of the present invention;
FIG. 7 is a schematic view of a cast slab in an application of example 1 of the present invention;
fig. 8 is a schematic diagram illustrating the flange plate design feature division in the application case of embodiment 1 of the present invention;
FIG. 9 is a diagram of a pareto solution set in an application case of embodiment 1 of the present invention;
FIG. 10 is a schematic diagram showing the design characteristic carbon emission and the actual analysis in the case of application of example 1 of the present invention;
FIG. 11 is a schematic view of the DF13 and DF14 modification process in the application case of example 1 of the present invention;
FIG. 12 is a graph comparing the results of the optimum processing scheme and the improved scheme in the application case of example 1 of the present invention;
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As described in the background art, currently, most of the mechanical processing researches evaluate carbon emission from the manufacturing perspective, and it is difficult to combine the carbon emission with product information in the design stage.
In a typical embodiment of the present application, a method for designing a low carbon product based on a multi-level integrated frame, the low carbon design scheme includes configuration of machine tool types of operation features, selection of machining auxiliary materials, selection of cutting parameters, and carbon emission features, machining process features, and design features obtained by mapping the integrated frame, as shown in fig. 1, and includes the following steps:
step 1: according to the characteristic engineering technology, a multi-level integrated framework which is composed of a design characteristic layer, a processing process layer, a processing characteristic layer, an operation characteristic layer and a carbon emission characteristic layer and has a mapping relation is established.
The characteristic technology is used as the basis of the CAPP system function, can effectively integrate design information and manufacturing information, and is widely applied. In the product design process, the design feature layer is used as a basic unit of the product, such as a hole, a key slot, a chamfer and the like. The CAPP takes the design characteristics as input to generate a processing scheme as a processing layer. The correlation OF the process information with the environmental impact information is characterized by introducing a process characteristic (MF), an operational characteristic (OF) and a carbon emission Characteristic (CF). Therefore, the quantification OF carbon emissions for each design feature can be accomplished by the DF-MP-MF-OF-CF mapping mechanism. On the basis, a multi-level integration framework is established.
As shown in fig. 2, the integrated frame is divided into five layers: the device comprises a design characteristic layer, a processing technology layer, a processing characteristic layer, an operation characteristic layer and a carbon emission characteristic layer which are sequentially in a mapping relation. The relationship among the levels is as follows: one design feature may be divided into i manufacturing processes according to production requirements. Each process can be integrated by MF, OF and CF. A process is described by a process feature, and there are j optional operational features to meet the requirements of each process feature, from which the designer can choose. And finally, quantifying the carbon emission according to the selected operating characteristics. Wherein a plurality of alternative operating characteristics can change the input-output list of carbon emission characteristics, with the potential to reduce carbon emissions.
In this embodiment, the processing feature layer refers to an information set for carrying the geometric shape and material of the product entity, and the main features of the processing feature layer are shape information, size information, tolerance information, and material information.
MF＝{F，S，T，M} (1)
F denotes shape information, which refers to a shape entity composed of a set of geometric elements;
s represents size information, including not only the length, width, height, radius and angle of geometric elements, but also volume, surface area and the like;
t represents tolerance information, and refers to technical requirements such as roughness, tolerance and the like;
m is material information, which is information related to properties such as material type and material properties.
The operation characteristic layer is mainly influenced by the processing characteristic layer, and the operation characteristic layer is an operation information set related to physical and functional changes of product entities and influences the environment, including the type of a processing machine tool, processing auxiliary materials and cutting parameter information.
OF＝{E，R，C} (2)
E refers to the selected machine tool type;
r is processing auxiliary materials such as cutters, cutting fluid and the like;
c is cutting parameters including cutting speed, feeding speed, cutting depth or cutting width and the like;
carbon emission feature (CF) refers to emissions resulting from the completion of operations corresponding to the processing of the feature, and is primarily referred to as greenhouse gases. All greenhouse gas (GHG) emissions can be converted to carbon dioxide equivalents for comparison. CAPP determines to a large extent the direct carbon emissions generated during the raw material preparation. In this example, the materials were selected in advance and had no optimization potential. Thus, indirect carbon emissions from energy sources and secondary materials are considered to be a major contributor to carbon emissions.
CF＝{P，A} (3)
P is carbon emission generated by energy consumption and is related to the consumption of electricity for production and processing;
a represents the carbon emission consumed by auxiliary materials, mainly the carbon emission of cutters, cutting fluid and the like;
and 2, step: establishing a carbon emission model and a time model;
in this embodiment, the method for establishing the carbon emission model includes:
as shown in fig. 3, product carbon emissions modeling is performed through the built multi-level integration framework. The information of each layer is related and corresponding, wherein the information of the next layer is determined by the previous layer.
The total carbon emission of the design feature layer is the sum of the carbon emissions of a plurality of processing features corresponding to the design feature
Carbon emissions per process and carbon emissions for corresponding process characteristics correspond to MP i ＝MF i The processing characteristics have a carbon emission equal to the sum of the carbon emissions of the selected operating characteristics.
Wherein
The total carbon emission of the design feature is therefore the carbon emission feature CF j ＝{P j ,A j And integrating and accumulating the carbon emission model according to the energy consumption model and the auxiliary material consumption model, wherein the carbon emission quantity generated by the energy consumption and the auxiliary material consumption is taken as a main factor.
C j ＝(P j ·F p )+(A j ·F a ) (4)
In the formula, C j Carbon emissions characteristic of jth carbon emissionAn amount; p j Energy consumption for jth carbon emission profile; a. The j An amount of auxiliary material consumption characteristic of jth carbon emission; f p Carbon emission coefficient as an energy source; f a Is the carbon emission coefficient of the auxiliary material.
The method for establishing the energy consumption model comprises the following steps:
the energy is supplied by electricity, and the energy consumption of the processing process is equal to the electricity consumption. In this example, the specific energy consumption (SEC, i.e. the energy required to remove a unit volume or mass of material) is introduced to quantify the energy consumption of the machine tool.
P j ＝SEC j ·V j (5)
In the formula (II) SEC j Specific energy consumption of selected machine tool for jth carbon emission characteristic in kJ/mm 3 ；V j The amount of material removed in mm corresponding to the jth carbon emission characteristic 3 ；vc j 、f j 、ap j Selecting cutting parameters for the jth carbon emission characteristic, namely cutting speed, feeding speed and cutting depth; c. C 1 ，c 2 Representing the fit coefficient, determined by the workpiece and the cutting conditions.
The method for establishing the auxiliary material consumption model comprises the following steps:
the carbon emission of the auxiliary material mainly refers to the carbon emission of the cutter and the cutting fluid.
A j ·F a ＝(A j-tool ·F t )+(A j-fluid ·F f ) (7)
In the formula, A j-tool Tool consumption as a jth carbon emission characteristic; a. The j-fluid Cutting fluid consumption as a jth carbon emission characteristic; f t Is the carbon emission coefficient of the tool; f f The carbon emission coefficient of the cutting fluid.
The carbon emission of the cutter mainly refers to indirect carbon emission generated by cutter abrasion in the machining process. The longer the life of the tool, the better the durability of the tool and the lower the indirect carbon emissions. The generalized taylor equation (equation 8) is a common variation of an approximate mathematical model describing tool life as a function of cutting parameters, illustrating that cutting parameters have a significant effect on tool carbon emissions.
In the formula, x, y and z are life coefficients of the cutter; c T Is a constant related to the product and tool material; m j Time (cutting time); t is j ，N j ，(m t ) j The durability, sharpening times and quality of the selected tool for the jth carbon emission characteristic.
Water-based cutting fluids, as a commonly used resource, are considered to be a major source of carbon emissions from cutting fluids. The consumption amount of the cutting fluid is inversely proportional to the replacement period of the cutting fluid.
In the formula (Tcycle) j ，(I jo ) j ，(A jd ) j A replacement cycle for cutting fluid, an initial volume in the cutting fluid tank, and an additional volume of cutting fluid characteristic of jth carbon emission.
In this embodiment, the method for establishing the time model includes:
time is defined as the time spent from the blank to the completion of the processing requirement, i.e. the maximum completion time of all jobs.
In the formula, M j Process time representing the jth carbon emission characteristic; v. of cj Represents the j (th) carbonA cutting speed of the discharge feature; ap j Feed rate, f, which is characteristic of the j-th carbon emission j Feed rate, V, representing the jth carbon emission characteristic j The mass removal amount corresponding to the jth carbon emission characteristic.
And step 3: and establishing a dual-target optimization model according to the carbon emission model and the time model.
From multi-level mapping association and a target model, the machine tool configuration, the selection of the tool and the cutting fluid and the parameter setting are closely related to carbon emission and time, and the method has the potential of low-carbon design. Therefore, the dual-objective optimization model established in the present embodiment is as follows.
Wherein:
1≤i≤n，1≤j≤m (14)
m is the total number of carbon emission features and n is the total number of processing operations.
In the formula, the formula (12) and the formula (13) are objective functions, wherein Obj1C total Total carbon emissions; obj2T total For the total completion time, the value range of each level is represented by the formula (14); x in formula (15) ij Assigning values to the operating characteristics of each machining characteristic;
and 4, step 4: as shown in fig. 4, each feature layer of the machined workpiece is encoded, and a plurality of optimized design solutions are obtained by using an improved differential evolution algorithm (IDE) according to a dual-objective optimization model.
In this embodiment, the differential evolution algorithm is a random direct search and global optimization algorithm. The method has the advantages of high convergence rate and high robustness, and is a universal tool for solving various optimization problems and practical engineering problems. Compared with other evolutionary algorithms, the method reserves a global search strategy based on the population, and simultaneously provides a simple differential mutation operation to reduce the complexity of the genetic process. The differential evolution algorithm of the embodiment is optimized by taking carbon emission and time as targets, and comprises the following specific steps:
step (1): and initializing the population. At the beginning of IDE, an initial population X of p individuals is randomly generated 1 All search spaces are evenly covered. Setting iteration times gen =1, and initializing a population X 1 ＝{X 1 1 ，X 2 1 ，...，X p 1 } (i =1,2,.., p), in which X is i 1 ＝{x i1 1 ，x i2 1 ，...，x in 1 } (j =1, 2.., n). The mutation operator and the crossover operator are set to F and cp, respectively.
And (2) coding variables of the design characteristic layer, the processing process layer, the processing characteristic layer, the operation characteristic layer and the carbon emission characteristic layer.
As shown in fig. 5, the first layer is a design feature representing design information. The second layer is the process, representing the process of completing the production requirements for the design feature. The third layer is the processing feature, i.e., the information set for each process. The fourth layer is an operation characteristic and represents information such as machine tools, cutters, cutting fluid, cutting parameters and the like of preset machining characteristics; the last layer is a carbon emission feature. The population stores the attribute information of the gene segments through real number coding, and all the gene segments form a chromosome.
Step (3), fitness function: and selecting a population to calculate the carbon emission and completion time according to a dual-target optimization model.
And (4) genetic operators: optimizing the carbon emission and the maximum completion time obtained in the step (3) by using a differential algorithm to obtain an optimized design processing scheme; the differential algorithm includes genetic operations, i.e., differential mutation operations, crossover operations, and selection operations. The method comprises the following steps:
step (4.1) from population X i gen Medium random selectionThree individuals X from gen generation v1 gen ,X v2 gen ,X v3 gen The mutation operation is performed as described below.
Wherein F is in the range of [0,2]The scale factor of (2) can determine the distribution of the population and ensure that the population is searched in the global range; m is i gen+1 The population obtained after mutation operation; x v1 gen ,X v2 gen ,X v3 gen Is from a population X gen Three different individuals were randomly selected.
Step (4.2) performing cross operation between the mutation population after the mutation operation and the original population;
step (4.3) selecting operation by adopting a greedy algorithm;
pareto rating comparisons were made for all individuals, with higher rated individuals being retained.
Wherein, rank (C) i gen+1 ),rank(X i gen ) Respectively represent C i gen+1 ,X i gen Pareto rating of (d). When the tested individual C i gen+1 Is higher than X i gen In time, the subject replaces the original subject. Otherwise, the original individual will be retained and the test individual will be discarded.
And (5) re-executing the evolution termination judgment from the step (2) to the step (4) until all the set termination conditions are met.
And (6) outputting a plurality of pareto results, wherein each pareto result corresponds to an optimized design processing scheme to form a pareto solution set.
And 5: and obtaining the optimal design scheme of the workpiece according to a plurality of optimized design scheme decisions obtained through optimization.
Specifically, firstly, a Fuzzy Positive Ideal Solution (FPIS) and a Fuzzy Negative Ideal Solution (FNIS) are determined according to the pareto result, and then the Euclidean distance is adopted to calculate the distance between the pareto result and the FPIS and FNIS.
And calculating the weight of carbon emission and time by using an entropy weight method, and obtaining an optimal design scheme according to a fuzzy TOPSIS method.
By adopting the method of the embodiment, the carbon emission is quantized in the design stage to obtain the optimal design scheme, the carbon emission and the product information are combined in the design stage, the low-carbon design of the product is realized, the carbon emission in the processing process is favorably reduced, and the influence of the processing on the surrounding environment is reduced.
In an application case of the method of this embodiment, a flange is machined, the structure of the flange is shown in fig. 6, and the parameters of the casting blank (shown in fig. 7) are selected as: the material is HT200, the diameters are respectively
The length is 95mm. The workpiece is divided into 17 design features, the division of each feature is intuitively illustrated in fig. 8, and the carbon emission is quantified and optimized in the design stage, so that an optimal low-carbon process scheme can be obtained.
The CAPP generates processing technologies of various design features according to production requirements. The 17 design features are divided into 33 Machining Processes (MP), and main machining feature information { shape; diameter/volume; roughness; materials }, as in table 1. The material removal amount of each processing procedure can be obtained through the volume difference value of the blank and the finished workpiece.
TABLE 1 processing technique and processing characteristic information Table
In an actual production process, there are cases where a plurality of machining processes are machined on the same machine tool. Therefore, the present document assumes that 15 machine tools are available for machining, all dry cutting is adopted, and 2 tools meet the machining requirements in each process. Table 2 and table 3 present details of the machine tool and the tool, respectively. Table 4 illustrates information of the operation characteristics.
TABLE 2 tool information
TABLE 3 specific energy consumption information for machine tool cutting
TABLE 4 operating characteristics information
The case was programmed with Matlab and tested experimentally on Intel i5-8400 kernel, 2.80GHz, 8GB RAM and Windows10 operating systems. The parameter settings of the IDE are shown in Table 5.
TABLE 5 IDE parameter settings
Example calculations are shown in fig. 9, and the calculations show that the proposed method can effectively optimize a process recipe, all of which are evenly distributed. Two extreme points (a and B) were selected for further analysis. The results of the optimal carbon emission point (point a) and the optimal completion time point (point B) Ttotal =1177s, ctotal =2.547e + 5; ttotal =800.8, ctotal =4.318e + 5. The results of the optimization of the machine tool, tool and parameters are shown in table 6.
TABLE 6 extreme point optimization results
As can be seen from table 6, 1) the specific energy consumption of the selected machine for the machine at point a avoids the maximum value, the purpose of which is to reduce the carbon emissions of the process. Machines at point B tend to select a maximum or medium SEC, which results in a smaller machining time; 2) Aiming at the cutter, selecting a lower value of mt/(N + 1) of the cutter at the point A to reduce the carbon emission of the cutter; 3) For the cutting parameters, point B, vc, is higher, but both f and ap (ae) are smaller than point A. As can be seen from formula (11), time is inversely proportional to vc ap f. Therefore, point B corresponds to a larger vc ap f, resulting in a minimum completion time.
And obtaining the optimal result of the pareto front edge solution in the figure 8 by adopting a fuzzy TOPSIS decision method. Firstly, determining a Fuzzy Positive Ideal Solution (FPIS) and a Fuzzy Negative Ideal Solution (FNIS) according to the pareto result, and then calculating the distance between the pareto result and the FPIS and the FNIS by adopting the Euclidean distance.
TABLE 7 pareto solutions
By analyzing the pareto solution in table 7, the weights for carbon emissions and time were calculated as 0.4651196, 0.5348804, respectively, using entropy weighting. According to the fuzzy TOPSIS method, 16 optimized pareto solutions are ordered as follows: <xnotran> 9>8>5>4>6>3>7>10>11>12>14>13>2>15>16>1. </xnotran> Therefore, the 9 th result is an optimal design. The 17 design features under this protocol were further analyzed for carbon emissions and time, as shown in fig. 10.
And 5 design characteristics with the largest influence on carbon emission and time are obtained by adopting a comprehensive evaluation method, namely DF2, DF14, DF13, DF9 and DF3. Sensitivity analysis is carried out on the product, and the influence of changing the design characteristics on the carbon emission and the time of the product is quantitatively obtained. By modifying these most influential design features, carbon emissions can be effectively reduced. By analysis, the sensitivity of each design feature is ranked as: DF14, DF13, DF9, DF2, DF3. DF14 and DF13 are studied extensively herein. DF13 and DF14 are both milled
Back plane, the analytical principle is the same. Therefore, for DF14, the width of the rear plane is selected to be improved to reduce carbon emissions and time, taking into account geometric and dimensional constraints. The target value is known to decrease with decreasing material removal through the constructed optimization model. Thus, the design feature DF14 sets the back plane width to 27.5mm when the blank is cast. The same applies to DF13, the rear plane width being set at 29.5mm. The planes of DF13 and DF14 are shown in fig. 11.
According to the above measures, the material removal of DF13 and DF14 is calculated as: v (DF 13) =2344.965, V (DF 14) =2439.847; the carbon emissions and time results for the modified version are:
[318019.241339917769.148840057488]
the results of the optimal solution (9 th optimization) compared to the improved solution are shown in fig. 12. Obviously, the effect of the improvement is better than the former, and the proposed measures can effectively improve the design scheme. From the above results and analysis, it can be seen that the proposed IDE algorithm can provide an optimal solution for the process solution optimization problem. Analysis and modification of design features also enables low carbon design.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (7)
1. A method for designing a product with low carbon based on a multi-level integrated framework is characterized by comprising the following steps:
establishing a multi-level integrated frame consisting of a design characteristic layer, a processing process layer, a processing characteristic layer, an operation characteristic layer and a carbon emission characteristic layer with a mapping relation;
the relationship among the levels of the multi-level integrated framework is as follows: one design feature can be divided into a plurality of processing technologies according to production requirements, each processing technology can be integrated through the processing feature, the operation feature and the carbon emission feature, one processing technology is described through one processing feature, a plurality of optional operation features exist to meet the requirements of each processing feature, a designer can select the processing features, and finally carbon emission quantification is carried out according to the selected operation features;
establishing a carbon emission model and a time model according to the proposed integrated framework;
establishing a dual-target optimization model according to the carbon emission model and the time model;
performing multi-level coding according to the division of different characteristics of the product, and performing iterative optimization on the design scheme by using a differential evolution algorithm according to a dual-objective optimization model;
analyzing the pareto solution set obtained by optimization to obtain an optimal low-carbon design scheme;
the low-carbon design scheme comprises machine tool type configuration of operation characteristics, processing auxiliary material selection, cutting parameter selection, carbon emission characteristics obtained through integrated frame mapping, processing characteristics, processing process characteristics and design characteristics;
the carbon emission characteristic layer is the emission generated by finishing the operation corresponding to the processing characteristic layer;
the processing characteristic layer is an information set consisting of shape information, size information, tolerance information and material information of the workpiece to be processed;
the operation characteristic layer is an information set consisting of the type of a processing machine tool, processing auxiliary materials and cutting parameters;
in the product design process, taking a design feature layer as a basic unit of a product; the CAPP system takes the design characteristics as input to generate a processing scheme as a processing layer;
the total carbon emission of the design feature layer is the sum of the carbon emissions of a plurality of processing features corresponding to the design feature, the carbon emission of each processing corresponds to the carbon emission of the corresponding processing feature, the carbon emission of the processing features is equal to the sum of the carbon emissions of the selected operating features, and the total carbon emission of the design feature is formed by integrating and accumulating the carbon emission features.
2. The method for designing the low carbon product based on the multi-level integration framework as claimed in claim 1, wherein the dual-objective optimization model comprises a carbon emission objective optimization model and a time objective optimization model, the carbon emission objective optimization model is obtained according to the carbon emission model, and the time objective optimization model is obtained according to the time model.
3. The method for designing the low carbon product based on the multilevel integration framework as claimed in claim 1, wherein the differential evolution algorithm comprises the following specific steps:
initializing a population;
coding variables of a design characteristic layer, a processing process layer, a processing characteristic layer, an operation characteristic layer and a carbon emission characteristic layer;
step (3) calculating carbon emission and completion time according to the double-target optimization model;
step (4) optimizing the carbon emission and the maximum completion time obtained in the step (3) by adopting genetic operation to obtain an optimized design scheme;
step (5) repeatedly executing the step (2) to the step (4) until all the set termination conditions are met;
and (6) outputting a plurality of optimized design schemes.
4. The method for designing the low carbon product based on the multilevel integrated frame is characterized in that the specific steps of the step (4) are as follows:
randomly selecting a set number of individuals from a population to perform mutation operation;
performing cross operation between the variant population after the variant operation and the original population;
the selection operation is performed using a greedy algorithm.
5. The method for designing the product with low carbon based on the multilevel integration framework as claimed in claim 4, wherein the specific method for selecting the operation is as follows: and when the pareto grade of the tested individual after the cross operation is higher than that of the original individual, the tested individual replaces the original individual, otherwise, the original individual is kept.
6. The method for designing the product with low carbon content based on the multi-level integration framework as claimed in claim 1, wherein a pareto solution set of a plurality of optimized design schemes is obtained according to a differential evolution algorithm, fuzzy positive ideal solutions and fuzzy negative ideal solutions are determined according to the pareto solution set result, then distances between the pareto result and the fuzzy positive ideal solutions and the fuzzy negative ideal solutions are calculated by using Euclidean distances, and weights of carbon emission and time are calculated to obtain the optimal design scheme.
7. The method for designing the low carbon of the product based on the multilevel integration frame as claimed in claim 6, wherein after the weight of carbon emission and time is obtained by an entropy weight method, an optimal design scheme of the workpiece is obtained by a fuzzy TOPSIS decision method.
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