CN116047921B - Cutting parameter optimization method, device, equipment and medium - Google Patents
Cutting parameter optimization method, device, equipment and medium Download PDFInfo
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Abstract
The application discloses a cutting parameter optimization method, a device, equipment and a medium, which solve the technical problem that the cutting parameter optimization method in the prior art is unfavorable for energy conservation and emission reduction, and the method comprises the following steps: constructing a multi-objective cutting parameter optimization model according to the input parameters, the optimization targets and the optimization parameters; the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, and the optimization parameters comprise cutting parameters and cutting cutters; and solving the multi-target cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools meeting preset conditions. The method and the device can reduce energy consumption and carbon emission in the processing process under the condition of meeting the processing quality and the processing period.
Description
Technical Field
The application relates to the field of aircraft part machining, in particular to a cutting parameter optimization method, a cutting parameter optimization device, cutting parameter optimization equipment and cutting parameter optimization media.
Background
The machine manufacturing industry is used as a core component of the manufacturing industry in China, and is one of important sources of energy consumption and carbon emission. As a core of a master machine and a manufacturing system in the machine manufacturing industry, machine tools are large in energy consumption and carbon emission. Research shows that the energy consumption in the cutting process of the machine tool is less than 30% of the total energy consumption, and the energy utilization rate is lower than 15%.
The cutting process of the aircraft parts is taken as a typical mechanical manufacturing process, the energy consumption, the carbon emission and the processing quality of the aircraft parts are influenced by cutting parameters, cutting tools and the like, and the optimal cutting parameters are selected to be beneficial to reducing the processing time and the carbon emission and improving the surface quality. The traditional research neglects factors such as energy consumption, carbon emission and the like for optimizing the cutting parameters of the airplane parts, and is not beneficial to energy conservation and emission reduction.
The foregoing is merely provided to facilitate an understanding of the principles of the present application and is not admitted to be prior art.
Disclosure of Invention
The main purpose of the application is to provide a cutting parameter optimization method, a device, equipment and a medium, and aims to solve the technical problem that the cutting parameter optimization method in the prior art is unfavorable for energy conservation and emission reduction.
In order to achieve the above object, the present application provides a cutting parameter optimization method, including the following steps:
constructing a multi-objective cutting parameter optimization model according to the input parameters, the optimization targets and the optimization parameters; the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, and the optimization parameters comprise cutting parameters and cutting cutters;
And solving the multi-target cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools meeting preset conditions.
The step of constructing a multi-objective cutting parameter optimization model according to the input parameters, the optimization targets and the optimization parameters comprises the following steps:
determining constraint conditions according to the idle cutter parameters, the idle machine tool parameters and the part to be processed information;
constructing an optimization objective function according to the optimization target, the optimization parameters and the input parameters, wherein the optimization objective function comprises a processing carbon emission function, a processing time function and a processing cost function;
and constructing a multi-target cutting parameter optimization model according to the constraint conditions and the optimization objective function.
As some optional embodiments of the present application, the expression of the process carbon emission function CE (X) is:
in the method, in the process of the invention,indicating the total carbon emissions generated during the processing in the ith step>Representing the energy carbon emission generated in the processing process of the ith step; />Representing the carbon emissions of the material produced during the processing in the ith step,/->Representing carbon emission of waste generated in the processing process of the ith step, wherein i is a positive integer;
the expression of the energy carbon emission generated in the processing process of the ith step is as follows:
In the method, in the process of the invention,for the ith step start phase power, +.>For the ith step start phase time, +.>For the ith step standby power, +.>For the ith step standby time, +.>For the power of the ith step of tool change phase, < >>For the ith step of tool changing phase time, < >>For the ith step cutting phase power, +.>For the i-th step cutting phase time, +.>For the i-th step withdrawal phase power, < >>For the i-th step withdrawal period EF e Is an electric energy carbon emission factor;
the carbon emission of the materials generated in the processing process of the ith step is calculated by the following formula:
in the method, in the process of the invention,representing carbon emissions resulting from the preparation of the material removed in step i; />Representing carbon emission caused by the preparation of the scrapped cutter in the ith step; />Representing carbon emissions caused by the preparation of the cutting fluid consumed in the processing of the ith step;
the carbon emission of the waste generated in the processing process of the ith step is calculated by the following formula:
in the method, in the process of the invention,representing the carbon emissions caused by the disposal of waste chips of the ith step, +.>Representing the carbon emissions caused by the disposal of the waste tools in the ith step,/->Indicating the ith stepCarbon emissions from the disposal of waste cutting fluids.
As some optional embodiments of the present application, the processing time function T (X) has the expression:
Wherein t is c For cutting time, L denotes the cutter path length, f v Is the feed speed;
the processing cost function CO (X) has the expression:
wherein C is 1 Representing the sum of the management cost per unit time, the equipment depreciation cost and the labor cost, C 2 Representing the cost of electricity per unit time, C ct Representing the cost of the tool during the machining process, C cf Representing the cost of cutting fluid in the processing process, t c Is the cutting time.
As some optional embodiments of the present application, the step of constructing a multi-objective cutting parameter optimization model according to the constraint conditions and the optimization objective function includes:
and establishing a multi-target cutting parameter optimization model according to the optimization objective function, wherein the multi-target cutting parameter optimization model is as follows:
in the method, in the process of the invention,for optimizing the objective function, three: -are mainly comprised>For carbon emission->For processing time, < >>Is the processing cost. CE (X) is the process carbon emission function, T (X) is the process time function, CO (X) is the process cost function;
and establishing a constraint model according to the constraint conditions, wherein the constraint model is as follows:
wherein v represents the cutting speed of the machine tool and the tool, f represents the feeding amount of the machine tool and the tool, and a p Representing the depth of cut of machine tools and tools, F c For the cutting force of the machine tool, P c For cutting power of machine tool, R a V is the predicted value of the surface roughness of the part min Indicating the minimum allowable cutting speed of the machine tool and the cutter v max Indicating the highest allowable cutting speed of the machine tool and the cutter, f min Representing the minimum feed rate allowed by the machine tool and the cutter, f max Indicating the maximum feed rate allowed by the machine tool and the cutter, a pmin Indicating the minimum allowable cutting depth of a machine tool and a cutter, a pmax Indicating the maximum allowable cutting depth of the machine tool and the cutter, F cmax For maximum cutting force of machine tool, P η For the effective power of the machine tool, R a R is the predicted value of the surface roughness of the part max Is a required value of the surface roughness of the part.
As some optional embodiments of the present application, the step of solving the multi-objective cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools that meet preset conditions includes:
and solving the multi-target cutting parameter optimization model according to an improved squirrel search algorithm to obtain cutting parameters and cutting tools meeting preset conditions, wherein the improved squirrel search algorithm is that a Pareto sorting operator of an improved non-dominant sorting genetic algorithm is introduced into the squirrel search algorithm.
As some optional embodiments of the present application, the step of solving the multi-objective cutting parameter optimization model according to the modified squirrel search algorithm to obtain the cutting parameters and the cutting tools that meet the preset conditions includes:
setting algorithm parameters according to preset parameters, wherein the preset parameters comprise population quantity, iteration times, random sliding distance, sliding constant, the number of squirrels moving from the oak tree to the walnut tree, the number of squirrels moving from the common tree to the oak tree and the number of squirrels moving from the common tree to the walnut tree;
initializing the position of each individual in the first population according to a preset formula;
evaluating the individuals in the first population to obtain the processed carbon emission, the processing time and the processing cost of each individual;
ranking the first population according to a non-dominant ranking algorithm;
distributing each individual in the first group after sequencing to a hickory tree, a oak and a common tree according to a preset distribution rule, wherein the hickory tree represents a globally optimal solution, and the oak represents a locally optimal solution;
generating a second population according to the new solution generation rule;
combining the first population and the second population to obtain an intermediate population;
Sorting the intermediate populations according to the non-dominant sorting algorithm;
selecting the first population after iteration from the first n individuals in the intermediate population, wherein n is the population number
Returning the first population to the step of sorting the first population according to the non-dominant sorting algorithm according to the first population after iteration when the iteration number is smaller than the maximum iteration number;
and when the iteration times are equal to the maximum iteration times, obtaining cutting parameters and cutting tools meeting preset conditions.
As some optional embodiments of the present application, the step of assigning each individual in the first population after sorting to hickory, oak, and common trees according to a preset assignment rule includes:
acquiring a non-dominant sequencing number and a crowding distance of each individual;
individuals with non-dominant ranking numbers 1 and infinite crowding distances are assigned to walnut trees, other individuals with non-dominant ranking numbers 1 are assigned to oaks, and all other individuals are assigned to common trees.
To solve the above problems, the present application further provides a cutting parameter optimizing apparatus, including:
the model construction module is used for constructing a multi-objective cutting parameter optimization model according to the input parameters, the optimization targets and the optimization parameters; the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, and the optimization parameters comprise cutting parameters and cutting cutters;
And the model solving module is used for solving the multi-target cutting parameter optimization model based on a preset algorithm so as to obtain cutting parameters and cutting tools meeting preset conditions.
The beneficial effects that this application can realize are as follows:
according to the cutting parameter optimization method, device, equipment and medium, a multi-objective cutting parameter optimization model is built according to input parameters, optimization objectives and optimization parameters; the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, the optimization parameters comprise cutting parameters and cutting cutters, the processing time and the processing cost are considered, the carbon emission in the processing process is further set as the optimization targets, the factors such as energy consumption and carbon emission are considered, the cutting cutters select to have important influences on the carbon emission and the processing time, and the carbon emission in the cutting process is reduced through optimizing the cutting parameters, so that energy conservation and emission reduction are facilitated; and finally, solving the multi-target cutting parameter optimization model to obtain cutting parameters and cutting tools meeting preset conditions so as to realize the reduction of energy consumption and carbon emission in the processing process under the conditions of meeting the processing quality and the processing period.
Drawings
FIG. 1 is a schematic flow chart of a cutting parameter optimization method according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a cutting parameter optimizing apparatus according to an embodiment of the present application;
FIG. 3 is a Pareto optimal front obtained by the modified squirrel search algorithm, the second-generation non-dominant ranking genetic algorithm and the particle swarm algorithm according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
the realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The shape, the size, the relative position, the performance and the like of a blank are gradually changed according to a certain sequence by a mechanical processing scheme during the mechanical processing process until the blank is processed into a qualified part, in the process, along with the reduction of materials of the blank, equipment loss is caused, meanwhile, energy consumption and waste are also caused, because the prior art optimizes the cutting parameters of the aircraft parts, such as indexes as processing time, cost, surface quality and the like, usually consider, the factors such as energy consumption, carbon emission and the like, the cutting parameters obtained by optimization are ignored, the cutting efficiency and the effect are improved to a certain extent, the improvement of energy consumption and carbon emission is easy to be caused, the actual carbon emission sum calculation in the prior art only considers the processing process of the part on the equipment, and the part processing process such as cutter abrasion, waste liquid and waste gas generation and the processed carbon emission are ignored, so that a certain deviation exists in the carbon emission result, which is unfavorable for energy conservation and emission reduction.
Meanwhile, the traditional research is to optimize cutting parameters, and the traditional NSGA-II multi-objective algorithm is often adopted, so that the problems of local optimization and the like are easily caused.
Referring to fig. 1, an embodiment of the present application provides a cutting parameter optimization method for an aircraft component, where in the embodiment, the aircraft component may include: skin, wing main box, flaps, spoilers, ailerons, leading edge slats, engine hangers, etc., the above aircraft components are by way of example only and not limitation;
the cutting parameter optimization method comprises the following steps:
s1, constructing a multi-objective cutting parameter optimization model according to input parameters, an optimization target and optimization parameters; the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, and the optimization parameters comprise cutting parameters and cutting cutters;
specifically, in the step, a multi-objective cutting parameter optimization model is firstly constructed according to input parameters, optimization targets and optimization parameters, and the selection of the cutting parameters and the cutting tools has important influence on carbon emission and processing time, so that the optimization parameters of the constructed multi-objective cutting parameter optimization model comprise cutting parameters and cutting tools, wherein the cutting parameters comprise, but are not limited to, cutting depth, cutting width, cutting speed and feeding amount; the multi-objective cutting parameter optimization model also considers the targets of processing time and processing cost while guaranteeing low carbon in the processing process, so that the optimization targets comprise processing carbon emission, processing time and processing cost, the carbon emission in the processing process is further set as the optimization target on the premise of considering the processing time and the processing cost, the factors such as energy consumption and carbon emission are considered, and the carbon emission in the cutting process is reduced by optimizing the cutting parameters, so that energy conservation and emission reduction are facilitated.
As some optional embodiments of the present application, the step of constructing a multi-objective cutting parameter optimization model according to the input parameters, the optimization objective, and the optimization parameters includes:
s11, determining constraint conditions according to the idle cutter parameters and the idle machine tool parameters;
in the actual machining process, the following aspects need to be considered, firstly, in order to ensure the normal use of a machine tool and a cutter in the machining process, cutting parameters cannot exceed the bearable range of the machine tool and the cutter, cutting speed, feeding amount and cutting depth need to be limited, secondly, the main cutting force must be within the maximum cutting force allowed by the machine tool, the cutting power needs to be within the effective power range of the machine tool, and then, in order to ensure the normal use of the cutter in the machining process, the service life of the cutter needs to be limited; finally, limiting the roughness of the surface of the part in a finishing stage according to the machining requirement of the part; through the definition, the multi-target cutting parameter optimization model can obtain the cutting parameters capable of achieving efficient, economical and green low-carbon targets in the machining process on the premise of ensuring normal use of a machine tool and a cutter.
S12, constructing an optimization objective function according to the optimization objective and the optimization parameters, wherein the optimization objective function comprises a processing carbon emission function, a processing time function and a processing cost function;
specifically, according to the optimization objective, a corresponding optimization objective function is established for each optimization objective, where the optimization objective function includes a process carbon emission function, a process time function, and a process cost function, and in an embodiment of the present application, as some optional embodiments of the present application, the expression of the process carbon emission function CE (X) is:
in the method, in the process of the invention,representing carbon emissions generated during the processing of the ith step->Representing the energy carbon emission generated in the processing process of the ith step; />Representing the carbon emissions of the material produced during the processing in the ith step,/->Representing carbon emission of waste generated in the processing process of the ith step, wherein i is a positive integer;
the expression of the energy carbon emission generated in the processing process of the ith step is as follows:
in the method, in the process of the invention,for the ith step start phase power, +.>For the ith step start phase time, +.>For the ith step standby power, +.>For the ith step standby time, +.>For the power of the ith step of tool change phase, < >>For the ith step of tool changing phase time, < > >For the ith step cutting phase power, +.>For the i-th step cutting phase time, +.>For the i-th step withdrawal phase power, < >>For the i-th step withdrawal period EF e Is an electric energy carbon emission factor;
the energy carbon emission of the material clamping stage and the tool changing stage depends on the machine tools, tools and fixtures used by two adjacent process steps, and the energy carbon emission generated by one process step on a specific machine tool is calculated by the following formula in terms of characteristics, parts and workshops:
in the method, in the process of the invention,for the i-th step cutting stage power vm i Representing the actual cutting volume, EF, of the ith step e MRR is an electric energy carbon emission factor i The cutting material removal rate in the i-th step is shown.
In the processing of parts, the processing sequence of the process steps has influence on the cutting length, the volume and the like, and the actual cutting volume vm is caused i Is a variable that varies with the position of the process step in the sequence. Considering the influence of the intersection of different features of the part on the actual cutting volume, modeling the actual cutting volume of the ith step by using the principle of capacity and repulsion, and calculating by the following formula:
wherein V is i Representing the cutting volume when the step contains all the intersecting volumes, the value being equal to the cutting volume when the row in the i-th step is processed at the 1 st position; g j Indicating the cutting volume at the j-th position when the step contains all the intersecting volumes,is an operator for calculating the inter-step intersection volume, j 1 、j 2 And j M-1 Representing the position sequence number of the work step in the sequence; m represents the maximum number of simultaneous steps in a part.
In the tool changing stage of the machine tool, the machine tool mainly comprises standby power and tool changing power, and the calculation formula is as follows:
in the method, in the process of the invention,for standby phase power of the ith step, +.>The power of the tool changing stage of the ith step;
in the cutting stage of the ith step, the main power is standbySpindle rotational power->Feed power->Material removal Power->And spraying cutting fluid power->The composition is calculated by the following formula:
in the method, in the process of the invention,for the ith stepStandby phase power->For spindle rotational power of the ith step, < +.>For the feed power of the ith step, +.>Removing power for the material of step i, < >>Spraying power for the cutting fluid in the ith step;
wherein the material removal power is closely related to the tool, the machined part and the machine tool used during machining, said material removal power being calculated according to the following formula, irrespective of the tool wear:
the material removal power is calculated in consideration of tool wear according to the following formula:
Wherein:representing the feed rate of the ith processing step, y t ，z t ，x d For coefficients, it is necessary to determine experimentally, < >>Indicating the cutting speed of the ith step; k (K) t ，K m ，K d ，K b Representing a scaling factor; VB (VB) i The abrasion loss of the rear cutter surface of the cutter used in the ith step is represented; />Representing the cutting depth of the ith step; />Representing the cutting width of the ith step; w (w) t ，x t ，y t ，z t ，w m ，y m ，z m ，w b ，x b ，y b ，z b ，w d ，x d ，y d ，z d Representing coefficients to be fitted; d (D) i Indicating the diameter of the tool used in the ith step. It can be seen that the standby power, spindle rotation power, feed power are determined by machine tool selection, and the material removal power level is determined by the tool and part materials, cutting parameters, etc.
The carbon emission of the materials generated in the processing process of the ith step is calculated by the following formula:
in the method, in the process of the invention,representing carbon emissions resulting from the preparation of the material removed in step i; />Representing carbon emission caused by the preparation of the scrapped cutter in the ith step; />Representing carbon emissions caused by the preparation of the cutting fluid consumed in the processing of the ith step;
the carbon emissions caused by the preparation of the material removed in the ith step are calculated by the following formula:
in the method, in the process of the invention,representing the volume of material removed in step i; />Representing the density of the workpiece material; />Representing the carbon emission factor of the workpiece material preparation;
the carbon emissions caused by the preparation of the scrap cutter in the ith step are calculated by the following formula:
In the method, in the process of the invention,represents the cutting time of the ith step, +.>Indicating the sharpening times of the cutter used in the ith step; />Representing the quality of the tool used in the ith step; />Representing the carbon emission factor of scrapped cutter preparation; />Representing the tool life of the tool used in the ith step under the given cutting conditions, calculated by the following formula:
wherein:representing a related tool life factor; />Representing the feeding amount per tooth of the ith step; />Indicating the number of teeth of the tool>The rotational speed of the machine tool and the tool at the i-th step of the machining is shown.
The carbon emissions caused by the preparation of the cutting fluid consumed in the process of step i are calculated by the following formula:
in the method, in the process of the invention,represents the cutting time of the ith step, +.>Representing the initial volume of cutting fluid; />Representing the volume of supplemental cutting fluid; />Indicating the replacement cycle of the cutting fluid, < > and->Representing the waste cutter treatment carbon emission factor;
the carbon emission of the waste generated in the processing process of the ith step is calculated by the following formula:
in the method, in the process of the invention,representing the carbon emissions caused by the disposal of waste chips of the ith step, +.>Representing the carbon emissions caused by the disposal of the waste tools in the ith step,/->Represents carbon emissions caused by the treatment of the waste cutting fluid in the i-th step.
The carbon emissions caused by the disposal of the waste chips in the i-th step are calculated by the following formula:
In the method, in the process of the invention,representing the volume of material excised in step i, +.>Indicating the density of the workpiece material, EF op-wjob Representing waste chip disposal carbon emission factor;
the carbon emissions caused by the disposal of the waste tools in the i-th step are calculated by the following formula:
in the method, in the process of the invention,represents the cutting time of the ith step, +.>Representing the tool life of the tool used in step i under the given cutting conditions, +.>Indicating the quality of the tool used in step i, < >>Indicating the sharpening number of the cutter used in the ith step,representing the waste cutter treatment carbon emission factor;
the carbon emissions caused by the treatment of the waste cutting fluid in the i-th step are calculated by the following formula:
in the method, in the process of the invention,represents the cutting time of the ith step, +.>Representing the initial volume of cutting fluid; />Representing the volume of supplemental cutting fluid; />Showing the replacement cycle of the cutting fluid; />Representing the carbon emission factor of the waste cutting fluid treatment.
S13, constructing a multi-objective cutting parameter optimization model according to the constraint conditions and the optimization objective function.
As some optional embodiments of the present application, the step of constructing a multi-objective cutting parameter optimization model according to the optimization parameters, the optimization targets, the constraint conditions and the output parameters includes:
establishing a multi-target cutting parameter optimization model according to the optimization parameters and the optimization targets, wherein the multi-target cutting parameter optimization model is as follows:
Wherein CE (X) is the processed carbon emission, T (X) is the processing time, CO (X) is the processing cost,for optimizing the objective function, three: -are mainly comprised>For carbon emission->For processing time, < >>Is the processing cost;
and establishing a constraint model according to the constraint conditions, wherein the constraint model is as follows:
in the formula, v min Indicating the minimum allowable cutting speed of the machine tool and the cutter v max Indicating the highest allowable cutting speed of the machine tool and the cutter, f min Representing the minimum feed rate allowed by the machine tool and the cutter, f max Indicating the maximum feed rate allowed by the machine tool and the cutter, a pmin Indicating the minimum depth of cut allowed by the machine + bed and tool, a pmax Indicating the maximum allowable cutting depth of the machine tool and the cutter, F cmax For maximum cutting force of machine tool, P η For the effective power of the machine tool, v denotes the cutting speed of the machine tool and the cutter, f denotes the feeding amount of the machine tool and the cutter, a p Representing the depth of cut of machine tools and tools, F c For the cutting force of the machine tool, P c For cutting power of machine tool, R a Is a predicted value of the surface roughness of the part.
As some optional embodiments of the present application, the processing time optimization function T (X) has the expression:
wherein t is c L table for cutting time Showing the cutter path length, f v Is the feed speed;
the expression of the processing cost optimization function CO (X) is as follows:
wherein C is 1 Representing the sum of the management cost per unit time, the equipment depreciation cost and the labor cost, C 2 Representing the cost of electricity per unit time, C ct Representing the cost of the tool during the machining process, C cf Representing the cost of cutting fluid in the processing process, t c Is the cutting time.
S2, solving the multi-target cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools meeting preset conditions;
the method comprises the steps that a built multi-target cutting parameter optimization model is solved, cutting parameters and cutting tools meeting preset conditions can be obtained, wherein the preset conditions are the cutting parameters and the cutting tools capable of reducing energy consumption and carbon emission in a machining process under the conditions of meeting machining quality and machining period;
because the established model is a multi-target model, the traditional squirrel search algorithm can only solve the model of a single target, while the improved non-dominant genetic algorithm can solve the multi-target model, but because the defects of the algorithm are easy to fall into a local optimal solution, as some optional embodiments of the application, the step of solving the multi-target cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools meeting preset conditions comprises the following steps:
S21, solving the multi-target cutting parameter optimization model according to an improved squirrel search algorithm to obtain cutting parameters and cutting tools meeting preset conditions, wherein the improved squirrel search algorithm is that a Pareto sorting operator of an improved non-dominant sorting genetic algorithm is introduced into the squirrel search algorithm.
The method is characterized in that the existing squirrel search algorithm is used for single-target continuous optimization, the problem that the existing squirrel search algorithm cannot be suitable for solving a multi-target model constructed in the previous steps of the application can be solved, the problem that the existing squirrel search algorithm can only be used for single-target continuous optimization can be solved by introducing a Pareto sorting operator of an improved non-dominant sorting genetic algorithm into the existing squirrel search algorithm, layering of MOSSA populations and calculation of crowding distances of individuals are achieved through Pareto sorting, so that each individual is located on a tree of which type can be distinguished, the squirrel search algorithm is used for solving the problem that the individual's foraging activity is influenced by seasonal variation through setting of seasonal variation conditions, the foraging activity is not active due to low-temperature conditions in winter, and the situation that the obtained solution falls into local optimum is avoided under the condition that the condition of achieving multi-target model solving.
As some optional embodiments of the present application, the step of solving the multi-objective cutting parameter optimization model according to the modified SSA algorithm based on a preset algorithm to obtain the cutting parameters and the cutting tools that meet the preset conditions includes:
S211, setting algorithm parameters according to preset parameters, wherein the preset parameters comprise population quantity, iteration times, random sliding distance, sliding constant, the number of squirrels moving from oak to walnut tree, the number of squirrels moving from common tree to oak tree and the number of squirrels moving from common tree to walnut tree;
specifically, the population number is the number of squirrels in the algorithm, firstly, the parameters of the improved squirrel search algorithm are set according to preset parameters, in an embodiment of the application, the population number is 5, the iteration number is 200, the sliding constant realizes the balance between global search and local search, the sliding constant is 1.9, the random sliding distance is [0.05,3.11], and the number of squirrels moving from oak trees to walnut trees is 5;
s212, initializing the position of each individual in the first population according to a preset formula;
after the initialization of the algorithm is completed, setting the position of each individual in the first population according to a preset formula, wherein the position of the ith individual can be determined by a vector, and the positions of all the individuals are randomly initialized within a boundary range, wherein the formula is as follows:
FS i,j =lb+rand*（ub-lb）
in FS i,j Values representing the j-th dimension of the i-th squirrel, ub, lb being the upper and lower boundaries of the variable, rand being [0,1 ]Random numbers in between;
s213, evaluating the individuals in the first population to obtain the processing carbon emission, the processing time and the processing cost of each individual;
the fitness value of each individual's location reflects the quality of the food it obtains, i.e., the optimal food source (hickory), normal food source (oak) and no food source (on ordinary trees), and thus also reflects their survival probability. By further random selection, some squirrels are considered to have met their daily energy requirements and moved toward hickory. The rest squirrel will go on going to the oak tree (to meet their daily energy demands), through putting the value of the decision variable into the fitness function defined by user, can calculate the fitness of each individual, finish the evaluation to each individual, said fitness function and aforesaid goal optimization function, can calculate the processing carbon emission, processing time and processing cost of each individual through the said goal optimization function;
s214, sorting the first population according to a non-dominant sorting algorithm;
the purpose of non-dominant ranking is to rank the population, the purpose of crowding distance ranking is to rank individuals in the same rank, and the crowdedness of each individual is calculated by the following formula:
Wherein x is i Position of i-1 st squirrel, CD im Indicating the congestion level of the ith individual in the mth objective function. f (f) m Represents the mth objective function, X max Represents the maximum under the m function, X in all individuals min Representing the minimum under the m function in all individuals; the non-dominant ranking algorithm is the prior art and is not repeated hereThe said;
s215, distributing each individual in the first population after sequencing to a hickory tree, a oak tree and a common tree according to a preset distribution rule, wherein the hickory tree represents a globally optimal solution, and the oak tree represents a locally optimal solution;
as some optional embodiments of the present application, the step of assigning each individual in the first population after sorting to hickory, oak and common tree according to a preset assignment rule includes:
s2151, obtaining a non-dominant sequencing number and a crowding distance of each individual;
after non-dominant ordering is performed on each individual in the first population, a non-dominant ordering sequence number and a crowding distance of each individual can be obtained; the purpose of non-dominant ranking is to rank groups, and the purpose of crowding distance ranking is to rank individuals in the same rank. Eventually, individuals with high levels and large crowding distances are selected as the next generation.
S2153, assigning individuals with non-dominant ranking number 1 and infinite crowding distance to the walnut tree, assigning other individuals with non-dominant ranking number 1 to the oak tree, and assigning all other individuals to the common tree.
Specifically, the individual with the non-dominant ranking sequence number of 1 represents that the individual generates the dominant to the individual with the non-dominant ranking sequence number of not 1, namely, the function values obtained in the objective function of the individual with the non-dominant ranking sequence number of 1 are better than the function values of the individual with the non-ranking sequence number of not 1 in the objective function, and the genetic algorithm has the property of automatic convergence, so that in order to ensure the diversity of solutions, the introduction of the crowding degree is to ensure the obtained solutions to be more uniform in the objective space, ensure the diversity of the solutions, the solutions in the same non-dominant ranking sequence number can be mutually separated, the solutions with large distances between the solutions are better than the solutions with small distances between the solutions, and the crowding distance of each individual is calculated by calculating the sum of the distance differences between two individuals adjacent to the crowding distance between the two individuals on each sub-objective function.
S216, generating a second population according to the new solution generation rule;
in the foregoing step, the location of each individual in the first population has been updated once according to a non-dominant ranking algorithm, where the location of the individual is further updated according to a squirrel search algorithm, and in one embodiment, the new solution generation rule includes:
The individuals located on the common tree move toward the oak, and the location information is calculated by the following formula:
r2 is [0,1 ]]Random numbers within a range; d, d g Representing a random glide distance; g c Representing the sliding constant;representing the location of an individual on a t-th generation oak; />Representing the position of a squirrel on a t-th generation common tree; p (P) dp The probability of natural enemies in the foraging process of the squirrel is represented;
the individual located on the general tree moves toward the hickory tree and the positional information is calculated by the following formula:
wherein R is 3 Is [0,1]Random numbers in the range of the random numbers,is the position of an individual on the t generation hickory tree;
the individual on the oak moves toward the hickory, and the position information is calculated by the following formula;
wherein R is 1 Is [0,1]Random numbers in the range of the random numbers,representing the location of an individual on a t-th generation oak; is the position of the individual on the t generation oak;
when the season variation is smaller than or equal to a preset value, randomly changing the position of an individual on a common tree, wherein the season variation is calculated by the following formula:
in the formula, t is the current iteration times; z is the number of individuals on the hickory in the t-th iteration and d is the number of individuals on the oak in the t-th iteration;
the preset value is calculated by the following formula:
Wherein t is the current iteration number, t max The maximum iteration number;
s217, combining the first population and the second population to obtain an intermediate population;
s218, sorting the intermediate population according to the non-dominant sorting algorithm;
s219, selecting the first population after iteration from the first n individuals in the middle population, wherein n is the population number;
after the second population is obtained, the first population and the second population are combined to obtain a middle population, in the improved non-dominant ranking genetic algorithm, each parent can generate a child through intersection and mutation, after the parents and the child are combined, due to limitation of population scale, the combined population is ranked through non-dominant ranking, proper individuals are selected for inheritance, namely, the next iteration is carried out, the purpose of carrying out rapid non-dominant ranking before selection is to enable better Jie Bao to be stored for carrying out the next iteration, an elite strategy is met, the elite strategy ensures that the whole population evolution process is carried out towards the optimal direction, and in the design of the evolution algorithm, whether the optimal solution can be converged is the main target of the algorithm.
S220, returning the first population to the step of sorting the first population according to the non-dominant sorting algorithm according to the first population after iteration when the iteration number is smaller than the maximum iteration number;
Specifically, when the iteration number is smaller than the maximum iteration number, returning the first population to the step of sorting the first population according to the non-dominant sorting algorithm according to the first population after iteration, and further iterating the first population.
S221, when the iteration times are equal to the maximum iteration times, obtaining cutting parameters and cutting tools meeting preset conditions.
When the iteration times are equal to the maximum iteration times, cutting parameters and cutting tools meeting preset conditions can be obtained;
in summary, according to the cutting parameter optimization method provided by the embodiment of the application, a multi-objective cutting parameter optimization model is constructed according to the input parameters, the optimization objectives and the optimization parameters; the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, the optimization parameters comprise cutting parameters and cutting cutters, the processing time and the processing cost are considered, the carbon emission in the processing process is further set as the optimization targets, the factors such as energy consumption and carbon emission are considered, the cutting cutters select to have important influences on the carbon emission and the processing time, and the carbon emission in the cutting process is reduced through optimizing the cutting parameters, so that energy conservation and emission reduction are facilitated; and finally, solving the multi-target cutting parameter optimization model to obtain cutting parameters and cutting tools meeting preset conditions so as to realize the reduction of energy consumption and carbon emission in the processing process under the conditions of meeting the processing quality and the processing period.
In one embodiment, the correctness and validity of the model and method are verified by taking the machining of an axis of an aircraft as an example. The chip volume generated by the machining process is generally determined at the design stage of the part and is constant and has no effect on the optimization process, so the production carbon emissions of workpiece material in the feed carbon and the chip disposal carbon emissions in the waste carbon are not considered in the optimization process herein. The machining length is 197mm, the diameter of the blank is 79mm, the diameter is 65mm finally, the single-side allowance is set to be 6.8mm, and the allowance of 0.2mm is reserved for finish machining. The material of this part was 45 steel, and the possible cutting tool information is shown in table 1 below, the tool materials being cemented carbide.
Table 1 working tool
The relevant cutting force coefficients and production cost coefficients are shown in table 2:
TABLE 2 cutting force and cost correlation coefficients
Wherein, the liquid crystal display device comprises a liquid crystal display device,，/>，/>，/>all obtained through a cutting quantity manual, C1 represents the sum of the unit time management cost, the equipment depreciation cost and the labor cost, C2 represents the unit time electric power cost, +/-up>Is the maximum time.
Based on the model and parameters constructed by the application, the improved squirrel search algorithm in the embodiment of the application is used for solving the model, and through multiple experiments, the optimal algorithm parameters are found, as shown in table 3:
TABLE 3MOSSA Algorithm operating parameters
In order to compare the feasibility and the advantages of the algorithm, the multi-objective cutting parameter optimization model is solved by using a second-generation non-dominant ordering genetic algorithm and a particle swarm algorithm, fig. 3 shows a Pareto optimal front obtained by optimizing the improved squirrel search algorithm MOSSA, the second-generation non-dominant ordering genetic algorithm and the particle swarm algorithm, and the front obtained by the improved squirrel search algorithm has more competitive advantage by comparing the Pareto front. The present algorithm has advantages in solving this problem. Table 4 shows some Pareto results obtained from the improved squirrel search algorithm solution. From the results, it can be seen that scheme 2 can achieve the minimum carbon emissions, but the processing time and cost are increased by 16% accordingly. Therefore, under different processing requirements, a process planning person can select different schemes according to the processing requirements so as to realize different processing targets.
Table 4Pareto solution set
In the above table: v represents the cutting speed of the machine tool and the cutter, f represents the feeding amount of the machine tool and the cutter,the cutting depth of the machine tool and the cutter is represented, T is cutting time, CE is processing carbon emission, and CO is processing cost.
In order to solve the above technical problem, referring to fig. 2, the present application further proposes: a cutting parameter optimizing apparatus, the apparatus comprising:
The model construction module is used for constructing a multi-objective cutting parameter optimization model according to the input parameters, the optimization targets and the optimization parameters; the input parameters comprise idle cutter parameters, parts to be machined and idle machine tool parameters, the optimization targets comprise machining carbon emission, machining time and machining cost, and when the machining stage is a rough machining stage, the optimization parameters comprise cutting parameters and cutting cutters; when the machining stage is a finishing stage, the optimized parameters include cutting width, cutting speed, feed amount and cutting tool;
and the model solving module is used for solving the multi-target cutting parameter optimization model based on a preset algorithm so as to obtain cutting parameters and cutting tools meeting preset conditions.
It should be noted that, each module in the cutting parameter optimizing apparatus of the present embodiment corresponds to each step in the cutting parameter optimizing method in the foregoing embodiment one by one, so specific implementation manner and achieved technical effects of the present embodiment may refer to implementation manner of the foregoing cutting parameter optimizing method, and will not be described herein in detail.
In addition, a cutting parameter optimization method according to the embodiment of the present invention described in connection with fig. 1 may be implemented by an electronic device. Fig. 4 shows a schematic hardware structure of an electronic device according to an embodiment of the present invention.
The electronic device may comprise at least one processor 301, at least one memory 302 and computer program instructions stored in the memory 302, which, when executed by the processor 301, implement the method as described in the above embodiments.
In particular, the processor 301 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present invention.
Memory 302 may include mass storage for data or instructions. By way of example, and not limitation, memory 302 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Memory 302 may include removable or non-removable (or fixed) media, where appropriate. Memory 302 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 302 is a non-volatile solid-state memory. In particular embodiments, memory 302 includes Read Only Memory (ROM). The ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these, where appropriate.
The processor 301 implements any of the cutting parameter optimization methods of the above embodiments by reading and executing computer program instructions stored in the memory 302.
In one example, the electronic device may also include a communication interface and a bus. As shown in fig. 4, the processor 301, the memory 302, and the communication interface 303 are connected to each other by a bus 310 and perform communication with each other. The communication interface 303 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present invention.
The bus includes hardware, software, or both that couple components of the electronic device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. The bus may include one or more buses, where appropriate. Although embodiments of the invention have been described and illustrated with respect to a particular bus, the invention contemplates any suitable bus or interconnect.
In addition, in combination with the cutting parameter optimization method in the above embodiment, the embodiment of the present invention may be implemented by providing a computer readable storage medium. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the cutting parameter optimization methods of the above embodiments.
It should be understood that the invention is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In the foregoing, only the specific embodiments of the present invention are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present invention is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and they should be included in the scope of the present invention.
Claims (9)
1. A method of optimizing cutting parameters, comprising the steps of:
determining constraint conditions according to the idle cutter parameters, the idle machine tool parameters and the information of the parts to be processed;
Constructing an optimization objective function according to an optimization target, an optimization parameter and an input parameter, wherein the optimization objective function comprises a processing carbon emission function, a processing time function and a processing cost function;
wherein the expression of the processed carbon emission function CE (X) is:
in the method, in the process of the invention,representing the total carbon emissions generated during the ith process step; />Representing the energy carbon emission generated in the processing process of the ith step; />Representing the carbon emissions of the material produced during the processing in the ith step,/->Representing carbon emission of waste generated in the processing process of the ith step, wherein i is a positive integer;
the expression of the energy carbon emission generated in the processing process of the ith step is as follows:
in the method, in the process of the invention,for the ith step start phase power, +.>For the ith step start phase time, +.>For the ith step standby power, +.>For the ith step standby time, +.>For the power of the ith step of tool change phase, < >>For the ith step of tool changing phase time, < >>For the ith step cutting phase power, +.>For the i-th step cutting phase time, +.>For the i-th step withdrawal phase power, < >>For the i-th step withdrawal period EF e Is an electric energy carbon emission factor;
the carbon emission of the materials generated in the processing process of the ith step is calculated by the following formula:
In the method, in the process of the invention,representing carbon emissions resulting from the preparation of the material removed in step i; />Representing carbon emission caused by the preparation of the scrapped cutter in the ith step; />Representing carbon emissions caused by the preparation of the cutting fluid consumed in the processing of the ith step;
the carbon emission of the waste generated in the processing process of the ith step is calculated by the following formula:
in the method, in the process of the invention,representing the carbon emissions caused by the disposal of waste chips of the ith step, +.>Representing the carbon emissions caused by the disposal of the waste tools in the ith step,/->Representing carbon emissions caused by the treatment of the waste cutting fluid in the ith step;
constructing a multi-target cutting parameter optimization model according to the constraint conditions and the optimization objective function;
the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, and the optimization parameters comprise cutting parameters and cutting cutters;
and solving the multi-target cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools meeting preset conditions.
2. The cutting parameter optimization method as set forth in claim 1, wherein the processing time function T (X) has the expression:
Wherein t is c For cutting time, L denotes the cutter path length, f v Is the feed speed;
the processing cost function CO (X) has the expression:
wherein C is 1 Representing the sum of the management cost per unit time, the equipment depreciation cost and the labor cost, C 2 Representing the cost of electricity per unit time, C ct Representing the cost of the tool during the machining process, C cf Representing the cost of cutting fluid in the processing process, t c Is the cutting time.
3. The cutting parameter optimization method of claim 1, wherein the constructing a multi-objective cutting parameter optimization model from the constraint and the optimization objective function comprises:
and establishing a multi-target cutting parameter optimization model according to the optimization objective function, wherein the multi-target cutting parameter optimization model is as follows:
in the method, in the process of the invention,for optimizing the objective function, three: -are mainly comprised>For carbon emission->For processing time, < >>For processing cost, CE (X) is the processing carbon emission function, T (X) is the processing time function, and CO (X) is the processing cost function;
and establishing a constraint model according to the constraint conditions, wherein the constraint model is as follows:
wherein v represents the cutting speed of the machine tool and the tool, f represents the feeding amount of the machine tool and the tool, and a p Representing the depth of cut of machine tools and tools, F c For the cutting force of the machine tool, P c For cutting power of machine tool, R a V is the predicted value of the surface roughness of the part min Indicating the minimum allowable cutting speed of the machine tool and the cutter v max Indicating the highest allowable cutting speed of the machine tool and the cutter, f min Representing the minimum feed rate allowed by the machine tool and the cutter, f max Indicating the maximum feed rate allowed by the machine tool and the cutter, a pmin Indicating the minimum allowable cutting depth of a machine tool and a cutter, a pmax Indicating the maximum allowable cutting depth of the machine tool and the cutter, F cmax For maximum cutting force of machine tool, P η For the effective power of the machine tool, R a R is the predicted value of the surface roughness of the part max Is a required value of the surface roughness of the part.
4. The method for optimizing cutting parameters according to claim 1, wherein the solving the multi-objective cutting parameter optimization model based on a preset algorithm to obtain cutting parameters and cutting tools satisfying preset conditions comprises:
and solving the multi-target cutting parameter optimization model according to an improved squirrel search algorithm to obtain cutting parameters and cutting tools meeting preset conditions, wherein the improved squirrel search algorithm is that a Pareto sorting operator of an improved non-dominant sorting genetic algorithm is introduced into the squirrel search algorithm.
5. The method of optimizing cutting parameters according to claim 4, wherein solving the multi-objective cutting parameter optimization model according to the modified squirrel search algorithm to obtain cutting parameters and cutting tools satisfying preset conditions comprises:
setting algorithm parameters according to preset parameters, wherein the preset parameters comprise population quantity, iteration times, random sliding distance, sliding constant, the number of squirrels moving from the oak tree to the walnut tree, the number of squirrels moving from the common tree to the oak tree and the number of squirrels moving from the common tree to the walnut tree;
initializing the position of each individual in the first population according to a preset formula;
evaluating the individuals in the first population to obtain the processed carbon emission, the processing time and the processing cost of each individual;
ranking the first population according to a non-dominant ranking algorithm;
distributing each individual in the first group after sequencing to a hickory tree, a oak and a common tree according to a preset distribution rule, wherein the hickory tree represents a globally optimal solution, and the oak represents a locally optimal solution;
generating a second population according to the new solution generation rule;
combining the first population and the second population to obtain an intermediate population;
Sorting the intermediate populations according to the non-dominant sorting algorithm;
selecting the first population after iteration from the first n individuals in the intermediate population, wherein n is the population number
Returning the first population to the step of sorting the first population according to the non-dominant sorting algorithm according to the first population after iteration when the iteration number is smaller than the maximum iteration number;
and when the iteration times are equal to the maximum iteration times, obtaining cutting parameters and cutting tools meeting preset conditions.
6. The method of optimizing cutting parameters according to claim 5, wherein assigning each individual in the first population to hickory, oak, and common tree according to a preset assignment rule comprises:
acquiring a non-dominant sequencing number and a crowding distance of each individual;
individuals with non-dominant ranking numbers 1 and infinite crowding distances are assigned to walnut trees, other individuals with non-dominant ranking numbers 1 are assigned to oaks, and all other individuals are assigned to common trees.
7. A cutting parameter optimizing apparatus, the apparatus comprising:
the model construction module is used for determining constraint conditions according to the idle cutter parameters, the idle machine tool parameters and the information of the parts to be processed;
Constructing an optimization objective function according to an optimization target, an optimization parameter and an input parameter, wherein the optimization objective function comprises a processing carbon emission function, a processing time function and a processing cost function;
wherein the expression of the processed carbon emission function CE (X) is:
in the method, in the process of the invention,representing the total carbon emissions generated during the ith process step; />Representing the energy carbon emission generated in the processing process of the ith step; />Representing the carbon emissions of the material produced during the processing in the ith step,/->Representing carbon emission of waste generated in the processing process of the ith step, wherein i is a positive integer;
the expression of the energy carbon emission generated in the processing process of the ith step is as follows:
in the method, in the process of the invention,for the ith step start phase power, +.>For the ith step start phase time, +.>For the ith step standby power, +.>For the ith step standby time, +.>For the power of the ith step of tool change phase, < >>For the ith step of tool changing phase time, < >>For the ith step cutting phase power, +.>For the i-th step cutting phase time, +.>For the i-th step withdrawal phase power, < >>For the i-th step withdrawal period EF e Is an electric energy carbon emission factor;
the carbon emission of the materials generated in the processing process of the ith step is calculated by the following formula:
In the method, in the process of the invention,representing carbon emissions resulting from the preparation of the material removed in step i; />Representing carbon emission caused by the preparation of the scrapped cutter in the ith step; />Representing carbon emissions caused by the preparation of the cutting fluid consumed in the processing of the ith step;
the carbon emission of the waste generated in the processing process of the ith step is calculated by the following formula:
in the method, in the process of the invention,representing the carbon emissions caused by the disposal of waste chips of the ith step, +.>Representing the carbon emissions caused by the disposal of the waste tools in the ith step,/->Representing carbon emissions caused by the treatment of the waste cutting fluid in the ith step;
constructing a multi-target cutting parameter optimization model according to the constraint conditions and the optimization objective function;
the input parameters comprise idle cutter parameters, part information to be processed and idle machine tool parameters, the optimization targets comprise processing carbon emission, processing time and processing cost, and the optimization parameters comprise cutting parameters and cutting cutters;
and the model solving module is used for solving the multi-target cutting parameter optimization model based on a preset algorithm so as to obtain cutting parameters and cutting tools meeting preset conditions.
8. An electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of any one of claims 1-6.
9. A storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1-6.
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