CN105652791B - The Discrete Manufacturing Process energy consumption optimization method of order-driven market - Google Patents

Abstract

The invention discloses an order-driven discrete manufacturing process energy consumption optimization method. The premise of realizing the energy consumption optimization of the discrete manufacturing process is to acquire process energy consumption information of the processing process. Therefore, the present invention first realizes the prediction of process energy consumption based on machine tool performance, processing materials, processing parameters, and NC codes, and provides data support for resource allocation for low-energy production at the system level. Then, the design of the present invention adopts the improved multi-objective optimization intelligent algorithm to optimize the allocation of production resources in the discrete manufacturing process, so as to ensure the coordinated optimization of optimization objectives such as completion time, processing cost, and processing energy consumption. The invention provides a new idea for the energy consumption optimization of the order-driven discrete manufacturing process, and provides a reference for realizing low energy consumption production and green manufacturing.

Description

Energy consumption optimization method for order-driven discrete manufacturing process

Technical field:

The invention belongs to the technical field of advanced manufacturing and automation, and in particular relates to an order-driven discrete manufacturing process energy consumption optimization method.

Background technique:

With the development of the manufacturing industry, order-driven customized production has become the mainstream. A single manufacturing enterprise often faces multiple order requirements from different customers. Therefore, the production method is a discrete manufacturing mode for multiple varieties and small batches, and its processing process is a complex process composed of different component processing sub-processes or parallel or series. On the other hand, my country is now the world's largest manufacturing country, and its manufacturing output accounts for about 20% of the world's total output. However, the development of my country's manufacturing industry is at the cost of high energy consumption. The energy consumption of manufacturing industry accounts for about 80% of my country's total industrial energy consumption. Japan's 7 times, also higher than Brazil, Mexico and other developing countries. my country is now the largest country in terms of carbon dioxide emissions, accounting for more than 70% of the global increase. The international pressure on energy conservation and emission reduction is increasing. Therefore, it is necessary to explore a resource allocation optimization method for discrete manufacturing processes that considers energy consumption, arrange production reasonably, and ensure low cost, low energy consumption, and timeliness of production.

In order to ensure low energy consumption in the manufacturing process, experts and scholars at home and abroad have conducted extensive research and achieved certain results, but there are certain limitations, mainly including:

1) The current research mainly focuses on the equipment level to optimize the control, improvement and replacement of key parts of the machine tool, or to optimize the process parameters of the machining process to achieve the goal of low energy consumption production, which cannot be achieved from the manufacturing system level The height guarantees low energy consumption production.

2) Research on low-energy production at the manufacturing system level is mainly concentrated in steel, tire and other flow production workshops, and research on discrete manufacturing processes is relatively small. On the other hand, ensuring low-energy production at the system level requires prior knowledge of the energy consumption of a certain process on a certain machine tool. Many studies lack research in this area, so low-energy production planning is not practical.

3) When optimizing the manufacturing process, the optimization objectives include multiple objectives such as completion time, processing cost, machine tool load rate, and processing energy consumption. The intelligent optimization algorithms used in many studies cannot guarantee the optimal coordination of multiple objectives.

Based on the above problems, it can be seen that the current research has certain limitations and loopholes. Therefore, it is of great significance to study the energy consumption optimization method of the order-driven discrete manufacturing process for the energy saving and emission reduction of manufacturing enterprises. First of all, the premise of realizing the energy consumption optimization of the discrete manufacturing process is to carry out the process energy consumption prediction of the processing process. According to machine tool performance, processing materials, and processing technology parameters, energy consumption prediction is realized, and data support is provided for optimizing low-energy production at the system level. Secondly, the method uses a multi-objective optimization intelligent algorithm to ensure the coordinated optimization of optimization objectives such as completion time, processing cost, and processing energy consumption. Therefore, this method greatly compensates for the shortcomings of traditional methods.

Invention content:

The purpose of the present invention is to provide an order-driven discrete manufacturing process energy consumption optimization method, which can realize the energy consumption prediction of the processing procedure and realize the energy consumption optimization of the discrete manufacturing process.

In order to achieve the above object, the present invention takes the following technical solutions to achieve:

An order-driven discrete manufacturing process energy optimization method, including the following steps:

1) According to the NC code of a certain process of the part to be processed, the load time, no-load time, tool change times, total standby time, total start-up time, feed rate under load, spindle speed and tool model of the process are obtained;

2) Substituting the load power and no-load power calculation model into the load power and no-load power calculation model according to the feed, spindle speed and tool model obtained in step 1), to obtain the load power and no-load power;

3) According to the load time, no-load time, tool change times, total standby time, and total start-up time obtained in step 1), the load power and no-load power obtained in step 2), and the inherent start-up of the machine tool selected in this process Power, standby power and tool change energy consumption are substituted into the calculation model of total process energy consumption to calculate the energy consumption value of this process;

4) According to the process energy consumption prediction method of step 1), step 2) and step 3), the energy consumption values of the same process of multiple different parts in a production batch processed on different machine tools are obtained, and the energy consumption information database is constructed;

5) According to the energy consumption information database obtained in step 4), an improved NSGA-II algorithm is designed to determine the processing machine tools for each process and the order of processing tasks on each machine tool, so as to ensure the completion time and processing cost. Under the constraints, the processing energy consumption is low.

A further improvement of the present invention is that the specific implementation steps of step 1) are as follows:

1-1) Construct an NC code parser by programming in C language, wherein, the number of tool changes N _c is obtained through the T command, and the model of the tool used at this time is obtained to obtain the milling width B; the start-up time and Turn off the time, so as to obtain the total running time T; obtain the spindle speed n of the machine tool through the S command; obtain the feed rate f through the F command; obtain the coordinate position point through the G command, and calculate the milling time T _l by combining the feed rate and the empty space Milling time T _is , combined with the total running time to calculate the standby time;

1-2) Input the NC code of the part to be processed into the NC code parser to automatically obtain the load time, no-load time, tool change times, total standby time, total start-up time, and feed during a certain process of processing , spindle speed and tool model.

A further improvement of the present invention is that the load power _P1 calculation model in step 2) is as follows:

In the formula, K _l is the load power coefficient, which is related to the workpiece material, cutting tool and machine tool performance; f is the feed rate under load, the unit is mm/min; a _p is the milling depth, the unit is mm; λ ₁ , λ ₂ , Both λ ₃ and λ ₄ are power exponents;

The calculation model of no-load power P _{is is} as follows:

In the formula, K _is the no-load power coefficient, which is related to the workpiece material, cutting tool and machine tool performance; α ₁ and α ₂ are power exponents.

A further improvement of the present invention is that the calculation model of the total process energy consumption E in step 3) is as follows:

E＝P _s T _s +P _i T _i +P _is T _is +P _l T _l +N _c E _c (5)

In the formula, P _s : equipment startup power, P _i : equipment standby power, P _is : idle milling power, P _l : equipment milling power, E _c : energy consumption of tool change, T _s : total startup time, T _i : standby Total time, T _is : total time of idle milling, T _l : milling time, N _c : number of tool changes.

A further improvement of the present invention is that the specific implementation steps of step 5) are as follows:

5-1) Processing information input: processing information includes processing task process information, processing machine tools that can be selected for processing a certain process of a part, processing time of each process on different machine tools, processing energy consumption, and standby power of the machine tool , Transportation energy consumption information between machine tools;

Construct the switch decision model of the machine tool, as follows:

if T ^SP +T ^PS > T _in

then: keep the machine unloaded;

else if E ^SP +E ^PS >C _I T _in

then keep the machine unloaded

else shut down the machine

In the model: T ^SP is the conversion time from shutdown to normal operation of the equipment; T ^PS is the conversion time from normal operation to shutdown of the equipment; T _in is the waiting time of the processing gap of the equipment; E ^SP is the conversion of the equipment from shutdown to normal operation Energy consumption; E ^PS is the converted energy consumption from normal operation to shutdown of the equipment; C _I is the no-load power of the equipment;

5-2) Construct a mathematical model of the planning problem, in which the optimization targets are processing energy consumption, production cost and completion time, the calculation formulas are shown in formulas (6), (7), and (8) respectively, and the constraints are shown in formula ( 9)～(13):

Processing energy consumption, including production energy consumption, processing interval energy consumption and transportation energy consumption:

Cost of production:

Completion Time:

T=max(C ₁ ,C ₂ ...C _m ) (8)

Restrictions:

C _k =max(c _ijk )i=1,2,...,n; j=1,2,...,p _i ; k∈M _ij (9)

c _ijk =s _ijk +t _ijk i=1,2,...,n; j=1,2,...,p _i ;k=1,2,...,m (10)

s _ijk -c _i(j-1)l ≥0 (11)

Among them, D _ijk represents the decision variable of selecting machine k for the jth process of workpiece i, Indicates the energy consumption of the jth process of the workpiece i on the machine k, Indicates the transportation energy consumption from the jth process of workpiece i to the next process, e _k represents the energy consumption of machine k in non-processing time, Indicates the processing cost of the jth process of the job i on the machine k, c _k represents the completion time of the machine k, c _ijk represents the completion time of the jth process of the task i on the machine k, p _i represents the process of the workpiece i The total number, M _ij represents the set of optional machine tools for the process j of the workpiece i, s _ijk represents the start time of the jth process of the task i on the machine k, and t _ijk represents the processing time of the jth process of the task i on the machine k , G _ijk represents the selection variable of the jth process selection machine k of the task i;

Equations (6), (7), and (8) are energy consumption target function, production cost function and completion time function respectively;

Constraint (9): Ensure that the completion time of machine k is the time of the last completion process on machine i;

Constraint (10): The completion time of the jth process of task i on machine k is the sum of the start time and process time;

Constraint condition (11): The processing sequence constraint of task i ensures that the start time of the process is after the end time of the previous process;

Constraint (12): Ensure that the jth process of task i has multiple optional machine tools;

Constraint condition (13): ensure that only one optional machine tool is selected for the jth process of task i;

5-3) Design of NSGA-II algorithm:

(1) Using the improved multi-objective optimization algorithm ED-NSGA-II to solve:

1) Coding and decoding design: design a two-dimensional coding method based on process and machine tool;

2) Evaluation of individual pros and cons: use non-dominated sorting value and congestion value to sort individual pros and cons;

3) Selection method: Championship selection method;

4) Crossover method: Binary POX crossover;

5) Mutation operation: random mutation;

6) Population retention mechanism: population retention mechanism based on elite strategy;

(2) The calculation process of the algorithm is:

1) Set the basic parameters of the algorithm: the maximum number of iterations is 150, the population size is 500, the crossover probability is 0.8; the mutation probability is 0.1;

2) Initialize the population, perform non-dominated sorting of individuals and calculation of crowding degree;

3) Select the crossover operation: the total number of mutation individuals is the product of the population size and the crossover probability: 400; two individuals are selected using the binary tournament method, and the individual with the lowest Rank value and the highest degree of crowding in non-dominated sorting is selected first, according to the crossover probability Perform binary POX crossover;

4) Selection of mutation operation: the total number of mutation individuals is the product of population size and mutation probability: 50; an individual is also selected using the binary tournament method, and the individual gene chain genes are randomly mutated according to the mutation probability;

5) Elite strategy population retention: merge the new population generated by crossover and mutation with the initially generated population, perform non-dominated sorting and crowding calculation of all individuals, and retain the top 500 outstanding individuals;

6) Termination condition detection: If the rank values of all individuals are 1 in the first 19 iterations, the iteration is terminated; if not satisfied, check whether the number of iterations is 150: not reached, Then turn to step 3) and enter the next iteration; when it reaches, the iteration is terminated;

7) output the iteration result;

5-5) Optimal solution determination:

Since the result of ED-NSGA-II solution is the optimal solution set, it is necessary to determine the optimal solution, so the weight determination of multiple objectives based on the DEMATEL+ANP method is used to determine the optimal solution;

5-6) Generation of production planning:

Through the determined optimal solution, the corresponding processing energy consumption is obtained, and at the same time, the processing sequence of the processing machine tools of each process and the tasks on each machine tool is decoded, and the corresponding production optimization configuration results are generated.

Compared with prior art, the beneficial effect that the present invention has is:

The energy consumption optimization method of the order-driven discrete manufacturing process provided by the present invention obtains and calculates the variables required for processing the process according to the NC codes of the different processes of the parts to be processed, and substitutes them into the energy consumption calculation model to obtain that the process is processed on a certain machine tool In order to establish the energy consumption information database of multiple parts processed on different machine tools, and then design a multi-objective optimization algorithm for the production planning of discrete processing, and realize the energy consumption optimization of the processing process. The steps of this matching method are clear and clear, which provides a reference for the energy consumption optimization at the system level of the order-driven discrete manufacturing process. On the one hand, the energy consumption prediction method based on processing parameters, NC codes, machine tool characteristics and processing materials proposed by the present invention can effectively Redundancy is reduced, thereby simplifying the construction of the energy consumption information database of parts processing procedures. On the other hand, the present invention proposes an energy consumption optimization method for the discrete manufacturing process at the system level, considers multiple energy consumption indicators of processing energy consumption, transportation energy consumption, and processing gap energy consumption, and constructs the switch of the machine tool during the processing gap Machine decision-making model to further reduce energy consumption; improved multi-objective optimization algorithm NSGA-II to allocate production resources, ensure completion time, and reduce processing costs and energy consumption.

Description of drawings:

Fig. 1 is the internal working flow chart of NC code parser;

Fig. 2 is a milling process power curve;

Fig. 3 is a relation diagram of machine tool state transformation;

Fig. 4 is the algorithm flowchart that the present invention designs;

Figure 5 is the case gene chain decoding Gantt chart;

Fig. 6 is a target value distribution diagram;

Figure 7 is a schematic diagram of binary POX crossover;

Figure 8 is a flowchart of the NSGA-II algorithm;

Fig. 9 is the improved NSGA-II algorithm flowchart of the present invention design;

Fig. 10 is the part information map of the processing example; wherein, Fig. 10 (a) is the parts diagram and the process card of the spout seat, Fig. 10 (b) is the parts diagram and the process card of the conductor 1, and Fig. 10 (c) is the conductor 2, and Figure 10(d) is the part diagram and process card of the FES shell;

Fig. 11 is the optimization result of the processing example algorithm; wherein, Fig. 11(a) is the convergence curve diagram of the optimal surface individual number, and Fig. 11(b) is the optimal surface diagram;

Figure 12 is a Gantt chart of an optimization example.

Detailed ways:

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific examples.

The order-driven discrete manufacturing process energy consumption optimization method provided by the present invention includes the following steps:

1) According to the NC code of a certain process of the part to be processed, the load time, no-load time, tool change times, total standby time, total start-up time, feed rate under load, spindle speed, tool model, etc. ;

2) Substituting the load power and no-load power calculation model into the load power and no-load power calculation model to obtain the load power and no-load power according to the feed rate, spindle speed, tool model, etc. obtained in step 1);

3) According to the variables obtained in step 1) and step 2) and the inherent start-up power, standby power and tool change energy consumption of the selected machine tool for this process, they are substituted into the calculation model of total process energy consumption to calculate the energy consumption value of this process.

4) According to the process energy consumption prediction method of step 1), step 2) and step 3), obtain the energy consumption value of a certain process of multiple different parts in a production batch processed on different machine tools, and build an energy consumption information database .

5) According to the energy consumption information database obtained in step 4), determine the processing machine tool for each process and the processing task on each machine tool, and design an improved NSGA-II algorithm to ensure that under the constraints of completion time and processing cost, Make the processing energy consumption low.

The concrete operation of described step 1) is:

Taking milling as an example, build an NC code parser, which is realized by programming in C language. The internal workflow is shown in Figure 1:

Obtain the number of tool changes N _c through the T command, and at the same time obtain the model of the tool used at this time to obtain the milling width B; obtain the startup time and shutdown time of the machine tool through the M command, so as to obtain the total calculation running time T; obtain the machine tool through the S command The spindle speed n; the feed f is obtained by the F command; the coordinate position point is obtained by the G command, the milling time T _l and the empty milling time T _is are calculated by combining the feed, and the standby time is calculated by combining the total running time.

The concrete operation of described step 2) is:

The first is to obtain the milling power formula: refer to the machining process manual, under the condition of certain machine tools, tools and materials, there is a complex power function relationship between milling power and milling parameters:

In the formula, K _l : the coefficient related to the workpiece material, tool and machine tool performance; n: the spindle speed, the unit is r/min; f: the feed rate, the unit is mm/min; P _l equipment milling power, a _p : Milling depth, unit mm; B: milling width, unit mm; λ ₁ , λ ₂ , λ ₃ , λ ₄ are power exponents.

Design an orthogonal test, use Jenica UMG-604 to configure the energy consumption acquisition system, use the software GridVis to perform statistics on processing energy consumption information, and use the multiple linear regression method (SPSS) to obtain the milling power formula:

When the machine tool, tool and material are fixed, the idle milling power is only related to the feed rate and speed, and the milling idle power formula is shown in formula (3):

In the formula, K _is the no-load power coefficient, which is related to the workpiece material, cutting tool and machine tool performance; α ₁ and α ₂ are power exponents.

The power exponent and the no-load power coefficient can be obtained by using the multiple linear regression method (SPSS), as shown in formula (4).

P _is ＝6.143×10 ^-16 n ^0.982 f ^0.124 (4)

Substitute the spindle speed, feed rate, milling depth, and milling width obtained in step 1) into formula (2) to obtain the milling power. Substitute the spindle speed and feed into formula (4) to get the idle milling power.

The concrete operation of described step 3) is:

The milling machine power curve of an actual machining process is shown in Figure 2, which can be divided into startup phase, standby phase, spindle startup phase, no-load phase and load phase. The power of the machine tool is different at each stage, and the total energy consumption can be calculated by formula (5):

E＝P _s T _s +P _i T _i +P _is T _is +P _l T _l +N _c E _c (5)

In the formula: P _s : equipment starting power, P _i : equipment standby power, P _is : idle milling power, P _l : equipment milling power, E _c : energy consumption of tool change, T _s : total startup time, T _i : Total standby time, T _is : total idle milling time, T _l : milling time, N _c : number of tool changes.

Combine the equipment start-up power, equipment standby power, tool change energy consumption and the idle milling power obtained in the first two steps, milling power, tool change times, total startup time, total standby time, total load time, idle milling The total time is substituted into formula (5) to obtain the energy consumption of this process on a certain machine tool.

The concrete operation of described step 4) is:

Obtain the processing process of multiple parts in a certain batch, and according to the actual situation of the workshop, determine the optional machine tool for a certain process of processing parts, according to the NC code, part material and machine tool performance, use the first 3 steps to determine The process energy consumption prediction method is used to calculate the energy consumption prediction value of a certain process of a part processed on different machine tools, and build the energy consumption information database of the batch of multiple parts processed on its optional machine tool.

The concrete operation of described step 5) is:

1. Description of planning problem:

1. n processing tasks, J _i , i=1, 2, 3...n;

2. m processing machine tools, the kth machine tool is M _k , k=1,2,3...m;

3. Each processing task has multiple processes, and O _ij represents the jth process of the i-th task.

4. A process can be processed on multiple machine tools, and the time, cost and energy consumption of different machine tools are different.

Contains two sub-questions:

Select the appropriate machine tool for each process, that is, the problem of machine allocation;

The processing procedures of multiple tasks on each machine tool are reasonably sequenced.

Assumptions:

1. At time t=0, all machines are available and all workpieces can be processed;

2. The processing time and cost of all processes on the available machines are different, the energy consumption value is known, and the transportation time is ignored;

3. There are successive process constraints between the processes of the same workpiece, and the processing sequence has been predetermined;

Restrictions:

The same machine can only process one part at the same time;

Each workpiece can only be processed on one machine at a certain time, and each operation cannot be interrupted midway;

There are sequence constraints between the processes of the same workpiece, but there is no sequence constraint between the processes of different workpieces

Different artifacts have the same priority

Goal: shortest completion time, lowest processing energy consumption, lowest cost

2. Construction of switch machine decision model:

During the processing, when the machine tool is waiting for the next processing procedure, it often adopts the mode of no-load waiting, which will cause a large amount of power consumption. In order to reduce the energy consumption of the machining process, it is necessary to build a decision-making model for turning on and off the machine tool during the machining interval.

The machine tool has three states of normal operation (P), no-load (I), and shutdown (S). The conversion between the states takes a certain amount of time and consumes a certain amount of energy. The conversion relationship is shown in Figure 3:

Parameter description in Fig. 3: C _I , C _P are the power of the machine tool in idling and normal operation states respectively, Ex ^xy represents the energy consumption in transition between states, for example E ^IP is the energy consumption in transition from idling to normal operation, T ^xy represents the transition time between states, for example, T ^IP is the time consumed for transition from idle to normal operation.

The decision model is:

if T ^SP +T ^PS > T _in

then: keep the machine unloaded;

else if E ^SP +E ^PS >C _I T _in

then keep the machine unloaded

else shut down the machine

1. If the sum of the transition time from shutdown to operation and the time from operation to shutdown is greater than the waiting gap time of the machine tool, keep the machine tool unloaded to ensure that the machine switch does not affect the completion time.

2. On the premise that the sum of the conversion time from shutdown to operation and the time from operation to shutdown is not greater than the waiting interval time of the machine tool, if the sum of the energy consumption from shutdown to operation and the energy consumption from operation to shutdown is greater than the no-load energy of the machine tool If the energy consumption is low, keep the machine tool unloaded to ensure the lowest energy consumption.

3. In other cases, turn off the machine tool. (Note: This model mainly considers the energy consumption factor, and other factors are not considered. For example, the loss cost of the machine tool caused by frequent switching on and off).

3. Mathematical Model Model Construction:

The selected optimization objectives are processing energy consumption, production cost and completion time:

(1) Processing energy consumption:

(2) Production cost:

(3) Completion time:

T=max(C ₁ ,C ₂ ...C _m ) (8)

Restrictions:

C _k =max(c _ijk )i=1,2,...,n; j=1,2,...,p _i ; k∈M _ij (9)

c _ijk =s _ijk +t _ijk i=1,2,...,n; j=1,2,...,p _i ;k=1,2,...,m (10)

s _ijk -c _i(j-1)l ≥0 (11)

Among them, D _ijk represents the decision variable of selecting machine k for the jth process of task i, Indicates the energy consumption of the jth process of the workpiece i on the machine k, Indicates the transportation energy consumption from the jth process of workpiece i to the next process, e _k represents the energy consumption of machine k in non-processing time, Indicates the processing cost of the jth process of the job i on the machine k, c _k represents the completion time of the machine k, c _ijk represents the completion time of the jth process of the task i on the machine k, p _i represents the process of the workpiece i The total number, M _ij represents the set of optional machine tools for the process j of the workpiece i, s _ijk represents the start time of the jth process of the task i on the machine k, and t _ijk represents the processing time of the jth process of the task i on the machine k , G _ijk represents the selection variable of the jth process selection machine k of the task i.

Equations (6), (7), and (8) are energy consumption target function, production cost function and completion time function respectively.

Constraint (9): Ensure that the completion time of machine k is the time of the last completion process on machine i;

Constraint (10): The completion time of the jth process of task i on machine k is the sum of the start time and process time;

Constraint (11): the processing order constraint of task i, to ensure that the start time of the process is after the end time of the previous process;

Constraint (12): Ensure that the jth process of task i has multiple optional machine tools;

Constraint (13): Ensure that the jth process of task i only selects one optional machine tool for processing;

3. Algorithm design:

The improved multi-objective optimization algorithm NSGA-II is used to solve the problem. The algorithm flow is shown in Figure 4. The algorithm design is as follows:

1. Encoding and decoding:,

Considering the characteristics of flexible scheduling, a two-dimensional coding method based on process and machine tool is designed. Encoding schemes such as:

Among them, the first line is the process order column: the first 2 represents the first process of workpiece 2, the first 3 represents the first process of workpiece 3, the first 1 represents the first process of workpiece 1, and the second 1 represents the second process of workpiece 1, and so on, the processing sequence is [O ₂₁ O ₃₁ O ₁₁ O ₁₂ O ₂₂ O ₃₂ O ₃₃ O ₂₃ O ₁₃ ]. The second line is the machine tool allocation sequence: the first 1 represents the first process of the corresponding workpiece 2 and selects machine tool 1 for processing, the first 2 represents the first process of the corresponding workpiece 3 and selects machine tool 2 for processing, and so on . The scheduling Gantt chart corresponding to this segment of the gene chain is shown in Figure 5.

2. Non-dominated set construction:

Definition 1 (domination relation in the solution space):

Let p _i and p _j be any two different individuals, if:

(1) For all sub-objectives, p _i is not worse than p _j , that is, f _k (p _i )≤f _k (p _j ), k=(1,2,...,n);

(2) At least one sub-goal exists such that p _i is better than p _j , , so that f _q (p _i )＜f _q (p _j );

Then it is said that p _i dominates p _j , which can be expressed as p _i > p _j .

Definition 2 (Pareto optimal): Among all individuals {p ₁ ,p ₂ ,...p _m }, if for individual p _i , there is no individual p _i such that: p _j ＞p _i , then p _i is called is the Pareto optimal individual.

Definition 3 (Pareto optimal front end or Pareto optimal boundary): that is, the area formed by the target values corresponding to all Pareto optimal individuals (the two-dimensional space is a curve, and the three-dimensional space is a surface):

F _r ={f ₁ (p _i ),f ₂ (p _i ),...f _n (p _i )}; i=(1,2,...,m)

The non-dominated set construction process is the process of optimizing all solutions. Suppose there are m individuals in the population, if: set All individuals in are not dominated by other individuals, that is, they are Pareto optimal individuals, then the corresponding Rank of the set=1, and the set is defined as:

Then the Rank= ₂ of the individuals corresponding to the individuals only dominated by the individuals in F1 among the remaining individuals, the composed set is:

Among the remaining individuals, the individuals corresponding to the individuals only dominated by the individuals in F2 have Rank= ₃ , and the set formed is:

By analogy, build the collection of each level. Finally, the Rank value of all individuals is obtained, and the Rank value of p _i is defined as V _i . Each generation of the NSGA-II algorithm will perform a non-dominated sorting process. The Rank value is the basis for the algorithm selection operation and whether the individual is retained. The algorithm iteration process will gradually make the Rank value of all individuals 1 through selection, crossover, and mutation, that is, all individuals Both are Pareto optimal solutions, thus forming the optimal front end.

3. Calculation of congestion degree

Agglomeration is used to describe the target value density of an individual. The crowding degree is mainly used for the ranking of individuals in each level. Take two optimization objectives as an example, refer to Figure 6;

Suppose the value of individual p _i on the kth sub-goal is p _i .f _k , then the crowding degree of individual i is:

p _i .dist＝(p _i+1 .f ₁ -p _i-1 .f ₁ )+(p _i+1 .f ₂ -p _i-1 .f ₂ ) (14)

If there are m target values, then the crowding degree of individual i is:

For convenience, normalize the data:

illustrate: and are the maximum and minimum indices of all individuals on the kth target, respectively.

The degree of congestion is the basis for selecting an operation, and the individual with a greater degree of congestion is considered to be better.

4. Select operation

The selection method designed in this paper is a tournament method, which randomly generates two individuals i and j, and selects by comparing their Rank value and crowding degree. The selection process is as follows:

If: V _i < V _j

Then: Choose V _i

Else if:V _i ＝V _j &&p _i .dist＞p _j .dist

Then: Choose V _i

Else:

Then: Choose V _j

5. Cross way

The crossover method is a binary POX crossover method. As shown in Figure 7, two parent individuals are selected through the selection operation, and the position of the exchange column is arbitrarily generated to carry out the gene exchange of the column to generate a new individual.

6. Mutation operation

The purpose of the mutation operation is to ensure the diversity of solutions. Generally, the value of the mutation probability is relatively small. If the value of the mutation probability is set too high, the algorithm will become a random algorithm, which reduces the convergence and optimization speed of the algorithm. The mutation operation designed by this algorithm is random mutation. For a gene at a certain position in an individual, a value between 0 and 1 is randomly generated. If the value is less than the set mutation probability value, a new gene is randomly generated at this position.

7. Population retention mechanism and its improvement

The traditional NSGA-II elite retention strategy is shown in Figure 8, P _i is the individual retained by the elite strategy after the i-th evolution, the number is N, and R _i is the new individual generated by the cross-mutation after the i-th evolution, The number is set as M, and the two are combined to generate a new population. Then construct the new population without domination and calculate the crowding degree of individuals in each level, and then select the top N elite individuals to form the next generation elite individual set P _i+1 . As shown in the figure, individuals in F ₁ and F ₂ can be completely retained, and some individuals with higher crowding degree in P _i can be retained. In order to maintain the diversity of algorithm solutions and improve the optimization ability of the algorithm, the algorithm has been improved accordingly, as shown in Figure 9:

P _i is the individual retained by the elite strategy after the ith evolution, and the number is N.

Step1: The non-dominated construction of the new population and the calculation of the crowding degree of individuals within each level are mainly aimed at providing individual Rank values and aggregation values for cross-variation.

Step2: All individuals in P _i are copied, and a new population consisting of new individuals (number M) is generated with cross-mutated individuals.

Step3: The non-dominated construction of the new population and the calculation of the crowding degree of individuals within each level are performed, and the top N elite individuals are selected to form the next generation elite individual set P _i+1 .

4. Determination of the weight of the DEMATEL+ANP index

The Pareto optimization solution obtained by the ED-NSGA-II algorithm is an optimal solution set, and the final solution needs to be determined. Therefore, the DEMATEL+ANP method is proposed to determine the index weight and normalize multiple indicators.

DEMATEL (Decision Making Trial and Evaluation Laboratory) is called "decision-making experiment and evaluation experiment method". This method calculates the degree of influence and the degree of influence of each factor and other factors by analyzing the logical relationship and direct influence relationship between various elements in the system. . Proceed as follows:

Step1: The determination of the mutual influence relationship between indicators can be quantified by referring to Table 1.

Table 1 Quantitative scoring table for mutual influence relationship

Step2: Normalize the initial matrix A to get the normalized matrix D:

Step3: Overall impact calculation:

T=D(ID) ^-1 (17)

Step4: Threshold calculation based on the maximum mean de-entropy (MMDE) algorithm (Li&Tzeng, 2009).

The ANP method is used to determine the importance relationship w _f between indicators, and the importance scores between indicators can refer to Table 2; Table 2 Importance relationship quantification score table

The overall impact matrix is:

w=T×w _f (18)

The final weights are determined as follows:

5. Case Analysis

A key R&D and production enterprise of high-voltage switchgear has high requirements for energy saving and emission reduction in the manufacturing process. Take a machining workshop of the company as an example. The workshop has 6 machine tools, and there are 4 parts production tasks in a production batch. The parts information is shown in Figure 10.

Step 1: Use the energy consumption prediction model to construct the processing energy consumption information table, and obtain the processing time and processing cost information at the same time. The obtained processing information table is shown in Table 3:

Table 3 Processing information table

After a part is produced in a certain process, the next process needs to be produced on the next machine tool, ignoring the influence of the weight of the part on energy consumption, and only considering the distance between machine tools, the transportation energy consumption information table between machine tools is obtained as shown in Table 4 Show:

Table 4 Transportation energy consumption information table between machine tools

There is a certain amount of energy consumption in machine tool standby, and the standby power information table is shown in Table 5:

Table 5 Machine tool standby power information table

The second step: use the improved NSGA-II algorithm to plan and configure production resources. It is known from the information table that the processing energy consumption, processing cost, and processing time of a certain process of a part are different on different machine tools. A multi-objective optimization algorithm is used to ensure the synergistic optimization of energy consumption, completion time, and processing cost. The parameters of the algorithm are set as shown in Table 6:

Table 6 Algorithm parameter information table

The algorithm convergence graph and optimal surface are shown in Figure 11. The weight of the index is determined by DEMATEL+ANP, and the optimal individual is determined to obtain the scheduling Gantt graph correspondingly as shown in Figure 12. The result of the production optimization configuration is:

The processing sequence of machine tool 1 is: O ₁₁ → O ₄₂ → O ₃₄ → O ₃₅ ;

The processing sequence of machine tool 2 is: O ₃₁ → O ₄₃ → O ₂₂ → O ₂₃ → O ₂₄ ;

The processing sequence of machine tool 3 is: O ₃₂ → O ₃₃ → O ₁₄ → O ₂₅ ;

The processing sequence of machine tool 4 is: O ₄₁ →O ₁₂ →O ₄₄

The processing sequence of machine tool 5 is: O ₂₁ →O ₁₃ →O ₄₅ →O ₁₅

Among them, O _ij represents the j-th process of the i-th workpiece.

The corresponding target value is: the completion time is 24min, the energy consumption value is 54.3kw.h, and the processing cost is 105 yuan.

The above content is a further detailed description of the present invention in conjunction with specific production cases. It is mainly to prove the correctness of the method in practical applications. It cannot be determined that the specific implementation of the present invention is limited to this. For personnel, without departing from the concept of the present invention, some simple deduction or replacement can also be made, which should be regarded as belonging to the scope of patent protection of the present invention determined by the submitted claims.

Claims (5)

1. An order-driven discrete manufacturing process energy optimization method, characterized in that, comprising the following steps: 1) According to the NC code of a certain process of the part to be processed, the load time, no-load time, tool change times, total standby time, total start-up time, feed rate under load, spindle speed and tool model of the process are obtained; 2) Substituting the load power and no-load power calculation model into the load power and no-load power calculation model according to the feed, spindle speed and tool model obtained in step 1), to obtain the load power and no-load power; 3) According to the load time, no-load time, tool change times, total standby time, and total start-up time obtained in step 1), the load power and no-load power obtained in step 2), and the inherent start-up of the machine tool selected in this process Power, standby power and tool change energy consumption are substituted into the calculation model of total process energy consumption to calculate the energy consumption value of this process; 4) According to the process energy consumption prediction method of step 1), step 2) and step 3), the energy consumption values of the same process of multiple different parts in a production batch processed on different machine tools are obtained, and the energy consumption information database is constructed; 5) According to the energy consumption information database obtained in step 4), an improved NSGA-II algorithm is designed to determine the processing machine tools for each process and the order of processing tasks on each machine tool, so as to ensure the completion time and processing cost. Under the constraints, the processing energy consumption is low. 2. The order-driven discrete manufacturing process energy consumption optimization method according to claim 1, characterized in that the specific implementation steps of step 1) are as follows: 1-1) Construct an NC code parser by programming in C language, wherein, the number of tool changes N _c is obtained through the T command, and the model of the tool used at this time is obtained to obtain the milling width B; the start-up time and Turn off the time, so as to obtain the total running time T; obtain the spindle speed n of the machine tool through the S command; obtain the feed rate f through the F command; obtain the coordinate position point through the G command, and calculate the milling time T _l by combining the feed rate and the empty space Milling time T _is , combined with the total running time to calculate the standby time; 1-2) Input the NC code of the part to be processed into the NC code parser to automatically obtain the load time, no-load time, tool change times, total standby time, total start-up time, and feed during a certain process of processing , spindle speed and tool model. 3. the discrete manufacturing process energy consumption optimization method of order-driven according to claim 2, is characterized in that, the load power _P1 calculation model in step 2) is as follows: <mrow><msub><mi>P</mi><mi>l</mi></msub><mo>=</mo><msub><mi>K</mi><mi>l</mi></msub><msup><mi>n</mi><msub><mi>&amp;lambda;</mi><mn>1</mn></msub></msup><msup><mi>f</mi><msub><mi>&amp;lambda;</mi><mn>2</mn></msub></msup><msup><msub><mi>a</mi><mi>p</mi></msub><msub><mi>&amp;lambda;</mi><mn>3</mn></msub></msup><msup><mi>B</mi><msub><mi>&amp;lambda;</mi><mn>4</mn></msub></msup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow> In the formula, K _l is the load power coefficient, which is related to the workpiece material, cutting tool and machine tool performance; f is the feed rate under load, the unit is mm/min; a _p is the milling depth, the unit is mm; λ ₁ , λ ₂ , Both λ ₃ and λ ₄ are power exponents; The calculation model of no-load power P _{is is} as follows: <mrow><msub><mi>P</mi><mrow><mi>i</mi><mi>s</mi></mrow></msub><mo>=</mo><msub><mi>K</mi><mrow><mi>i</mi><mi>s</mi></mrow></msub><msup><mi>n</mi><msub><mi>&amp;alpha;</mi><mn>1</mn></msub></msup><msup><mi>f</mi><msub><mi>&amp;alpha;</mi><mn>2</mn></msub></msup><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>3</mn><mo>)</mo></mrow></mrow> In the formula, K _is the no-load power coefficient, which is related to the workpiece material, cutting tool and machine tool performance; α ₁ and α ₂ are power exponents. 4. the order-driven discrete manufacturing process energy consumption optimization method according to claim 2, is characterized in that, the calculation model of total process energy consumption E in step 3) is as follows: E＝P _s T _s +P _i T _i +P _is T _is +P _l T _l +N _c E _c (5) In the formula, P _s : equipment startup power, P _i : equipment standby power, P _is : idle milling power, P _l : equipment milling power, E _c : energy consumption of tool change, T _s : total startup time, T _i : standby Total time, T _is : total time of idle milling, T _l : milling time, N _c : number of tool changes. 5. The order-driven discrete manufacturing process energy consumption optimization method according to claim 3, characterized in that the specific implementation steps of step 5) are as follows: 5-1) Processing information input: processing information includes processing task process information, processing machine tools that can be selected for processing a certain process of a part, processing time of each process on different machine tools, processing energy consumption, and standby power of the machine tool , Transportation energy consumption information between machine tools; Construct the switch decision model of the machine tool, as follows: if T ^SP +T ^PS > T _in then: keep the machine unloaded; else if E ^SP +E ^PS >C _I T _in then keep the machine unloaded else shut down the machine In the model: T ^SP is the conversion time from shutdown to normal operation of the equipment; T ^PS is the conversion time from normal operation to shutdown of the equipment; T _in is the waiting time of the processing gap of the equipment; E ^SP is the conversion of the equipment from shutdown to normal operation Energy consumption; E ^PS is the converted energy consumption from normal operation to shutdown of the equipment; C _I is the no-load power of the equipment; 5-2) Construct a mathematical model of the planning problem, in which the optimization targets are processing energy consumption, production cost and completion time, the calculation formulas are shown in formulas (6), (7), and (8) respectively, and the constraints are shown in formula ( 9)～(13): Processing energy consumption, including production energy consumption, processing interval energy consumption and transportation energy consumption: <mrow><mi>C</mi><mo>=</mo><munderover><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></munderover><munderover><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>P</mi><mi>i</mi></msub></munderover><munderover><mo>&amp;Sigma;</mo><mrow><mi>k</mi><mo>=</mo><mn>1</mn></mrow><mi>m</mi></munderover><mrow><mo>(</mo><msub><mi>D</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><msubsup><mi>C</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow><mrow><mi>p</mi><mi>r</mi><mi>o</mi><mi>d</mi></mrow></msubsup><mo>)</mo></mrow><mo>+</mo><munderover><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></munderover><munderover><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>z</mi><mo>-</mo><mn>1</mn></mrow></munderover><mrow><mo>(</mo><msubsup><mi>C</mi><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow><mrow><mi>t</mi><mi>r</mi><mi>a</mi><mi>n</mi><mi>s</mi></mrow></msubsup><mo>)</mo></mrow><mo>+</mo><munderover><mo>&amp;Sigma;</mo><mrow><mi>k</mi><mo>=</mo><mn>1</mn></mrow><mi>m</mi></munderover><msub><mi>e</mi><mi>k</mi></msub><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>6</mn><mo>)</mo></mrow></mrow> Cost of production: <mrow><mi>P</mi><mo>=</mo><munderover><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></munderover><munderover><mo>&amp;Sigma;</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>z</mi></munderover><munderover><mo>&amp;Sigma;</mo><mi>k</mi><mi>m</mi></munderover><mrow><mo>(</mo><msub><mi>D</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><msubsup><mi>P</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow><mrow><mi>p</mi><mi>r</mi><mi>o</mi><mi>d</mi></mrow></msubsup><mo>)</mo></mrow><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>7</mn><mo>)</mo></mrow></mrow> Completion Time: T=max(C ₁ ,C ₂ ...C _m ) (8) Restrictions: C _k =max(c _ijk )i=1,2,...,n; j=1,2,...,p _i ; k∈M _ij (9) c _ijk =s _ijk +t _ijk i=1,2,...,n; j=1,2,...,p _i ;k=1,2,...,m (10) s _ijk -c _i(j-1)l ≥0 (11) Among them, D _ijk represents the decision variable of selecting machine k for the jth process of workpiece i, Indicates the energy consumption of the jth process of the workpiece i on the machine k, Indicates the transportation energy consumption from the jth process of workpiece i to the next process, e _k represents the energy consumption of machine k in non-processing time, Indicates the processing cost of the jth process of the job i on the machine k, c _k represents the completion time of the machine k, c _ijk represents the completion time of the jth process of the task i on the machine k, p _i represents the process of the workpiece i The total number, M _ij represents the set of optional machine tools for the process j of the workpiece i, s _ijk represents the start time of the jth process of the task i on the machine k, and t _ijk represents the processing time of the jth process of the task i on the machine k , G _ijk represents the selection variable of the jth process selection machine k of the task i; Equations (6), (7), and (8) are energy consumption target function, production cost function and completion time function respectively; Constraint (9): Ensure that the completion time of machine k is the time of the last completion process on machine i; Constraint (10): The completion time of the jth process of task i on machine k is the sum of the start time and process time; Constraint condition (11): The processing sequence constraint of task i ensures that the start time of the process is after the end time of the previous process; Constraint (12): Ensure that the jth process of task i has multiple optional machine tools; Constraint condition (13): ensure that only one optional machine tool is selected for the jth process of task i; 5-3) Design of NSGA-II algorithm: (1) Using the improved multi-objective optimization algorithm ED-NSGA-II to solve: 1) Coding and decoding design: design a two-dimensional coding method based on process and machine tool; 2) Evaluation of individual pros and cons: use non-dominated sorting value and congestion value to sort individual pros and cons; 3) Selection method: Championship selection method; 4) Crossover method: Binary POX crossover; 5) Mutation operation: random mutation; 6) Population retention mechanism: population retention mechanism based on elite strategy; (2) The calculation process of the algorithm is: 1) Set the basic parameters of the algorithm: the maximum number of iterations is 150, the population size is 500, the crossover probability is 0.8; the mutation probability is 0.1; 2) Initialize the population, perform non-dominated sorting of individuals and calculation of crowding degree; 3) Select the crossover operation: the total number of mutation individuals is the product of the population size and the crossover probability: 400; two individuals are selected using the binary tournament method, and the individual with the lowest Rank value and the highest degree of crowding in non-dominated sorting is selected first, according to the crossover probability Perform binary POX crossover; 4) Selection of mutation operation: the total number of mutation individuals is the product of population size and mutation probability: 50; an individual is also selected using the binary tournament method, and the individual gene chain genes are randomly mutated according to the mutation probability; 5) Elite strategy population retention: merge the new population generated by crossover and mutation with the initially generated population, perform non-dominated sorting and crowding calculation of all individuals, and retain the top 500 outstanding individuals; 6) Termination condition detection: If the rank values of all individuals are 1 in the first 19 iterations, the iteration is terminated; if not satisfied, check whether the number of iterations is 150: not reached, Then turn to step 3) and enter the next iteration; when it reaches, the iteration is terminated; 7) output the iteration result; 5-5) Optimal solution determination: Since the result of ED-NSGA-II solution is the optimal solution set, it is necessary to determine the optimal solution, so the weight determination of multiple objectives based on the DEMATEL+ANP method is used to determine the optimal solution; 5-6) Generation of production planning: Through the determined optimal solution, the corresponding processing energy consumption is obtained, and at the same time, the processing sequence of the processing machine tools of each process and the tasks on each machine tool is decoded, and the corresponding production optimization configuration results are generated.