CN105652791B - The Discrete Manufacturing Process energy consumption optimization method of order-driven market - Google Patents
The Discrete Manufacturing Process energy consumption optimization method of order-driven market Download PDFInfo
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Abstract
The invention discloses a kind of Discrete Manufacturing Process energy consumption optimization method of order-driven market, to realize that the premise of the energy optimization of Discrete Manufacturing Process is to carry out the process energy consumption information of acquisition process.Therefore, the present invention realizes the prediction of process energy consumption according to machine tool capability, rapidoprint, working process parameter, NC codes first, the configuration provides that carry out resource for system layer low energy consumption production data supporting.Then, present invention design is distributed rationally using the resources of production of improved multiple-objection optimization intelligent algorithm progress Discrete Manufacturing Process, ensures the optimization aims such as completion date, processing cost, power consumption of polymer processing coordination optimization.The present invention provides new thinking for the energy optimization of the Discrete Manufacturing Process of order-driven market, to realize that low energy consumption production, green manufacturing provide reference.
Description
The technical field is as follows:
the invention belongs to the technical field of advanced manufacturing and automation, and particularly relates to an order-driven energy consumption optimization method in a discrete manufacturing process.
The background art comprises the following steps:
with the development of manufacturing, order-driven custom production becomes the mainstream. A single manufacturing enterprise often faces a plurality of order demands from different customers, so the production mode is a discrete manufacturing mode oriented to a plurality of varieties and small batches, and the processing process is a complex process consisting of different part processing sub-processes or parallel or serial connection. On the other hand, china is the first world in China, and the production yield of the manufacturing industry accounts for about 20% of the total production yield of the world. However, the development of the manufacturing industry in China is at the cost of high energy consumption, the energy consumption of the manufacturing industry accounts for about 80 percent of the total energy consumption of the industry in China, and the unit industry added value energy consumption level is 2.5 times of the average level of the world, 3.3 times of the United states, and 7 times of Japan, which are higher than those of developing countries such as Brazil, mexico and the like. China is the first big country of carbon dioxide emission, the increment also accounts for more than 70% of the world, and the international pressure on energy conservation and emission reduction is increasing. Therefore, it is necessary to explore a resource allocation optimization method in a discrete manufacturing process considering energy consumption, to reasonably arrange production, and to 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 carry out extensive research to obtain certain results, but certain limitations exist, and the method mainly comprises the following steps:
1) The current research mainly focuses on the equipment level, and the optimization control, improvement and replacement of key parts of a machine tool or the optimization of process parameters in the machining process are carried out, so that the aim of low-energy-consumption production is fulfilled, and the low-energy-consumption production cannot be guaranteed from the height of a manufacturing system layer.
2) Research on low-energy-consumption production at the manufacturing system level is mainly focused on flow production workshops of steel, tires and the like, and research on discrete manufacturing processes is less. On the other hand, ensuring the low-energy-consumption production of the system layer needs to acquire the priori knowledge of the processing energy consumption of a certain procedure on a certain machine tool, and many researches lack the research on the aspect, so that the low-energy-consumption production planning is lack of practicability.
3) When the manufacturing process is optimized, the optimization targets comprise multiple targets such as completion time, machining cost, machine tool load rate and machining energy consumption, and the intelligent optimization algorithm adopted by a few researches cannot ensure the coordination and optimization of the multiple targets.
Based on the problems, the existing research has certain limitations and loopholes, so that the research of the order-driven energy consumption optimization method in the discrete manufacturing process has great significance for energy conservation and emission reduction of manufacturing enterprises. The premise of realizing energy consumption optimization of the discrete manufacturing process is to predict the process energy consumption of the machining process. And the energy consumption is predicted according to the machine tool performance, the processing material and the processing technological parameter, and data support is provided for optimizing low-energy-consumption production of a system layer. Secondly, the method adopts a multi-objective optimization intelligent algorithm, and guarantees the coordination optimization of optimization objectives such as completion time, processing cost, processing energy consumption and the like. Therefore, the method greatly makes up for the defects of the traditional method.
The invention content is as follows:
the invention aims to provide an order-driven discrete manufacturing process energy consumption optimization method, which can realize process energy consumption prediction in a machining process and realize energy consumption optimization in a discrete machining process.
In order to achieve the purpose, the invention adopts the following technical scheme to realize:
an order driven discrete manufacturing process energy consumption optimization method, comprising the steps of:
1) Acquiring load time, dead time, tool changing times, total standby time, total starting time, feeding amount during loading, spindle rotating speed and tool model of a certain procedure for machining a part to be machined according to an NC code of the procedure;
2) Substituting the load power and the no-load power into a load power and no-load power calculation model according to the feed amount, the spindle rotating speed and the cutter model obtained in the step 1) during loading to obtain load power and no-load power;
3) Substituting the load power and the no-load power obtained in the step 2) and the inherent starting power, the standby power and the tool changing energy consumption of the machine tool selected in the procedure into a total procedure energy consumption calculation model according to the load time, the no-load time, the tool changing times, the standby total time and the starting total time obtained in the step 1), and calculating to obtain an energy consumption value of the procedure;
4) According to the process energy consumption prediction method of the steps 1), 2) and 3), acquiring energy consumption values of the same process of a plurality of different parts in a production batch for processing on different machine tools, and constructing an energy consumption information base;
5) And (4) designing an improved NSGA-II algorithm according to the energy consumption information base obtained in the step 4), and determining a processing machine tool in each procedure and a processing task sequence on each machine tool so as to ensure that the processing energy consumption is low under the constraints of completion time and processing cost.
The further improvement of the invention is that the concrete implementation steps of the step 1) are as follows:
1-1) adopting C language programming to construct an NC code resolver, wherein the tool changing times N are obtained through T instructions c Simultaneously obtaining the model of the cutter used at the moment to obtain the milling width B; acquiring the starting time and the closing time of the machine tool through the M instruction so as to acquire the total calculated operation time T; acquiring the main shaft rotating speed n of the machine tool through the S instruction; acquiring a feeding amount F through an F instruction; coordinate position points are obtained through the G command, and milling time T is calculated through combining the feeding amount l And empty milling time T is Calculating to obtain standby time by combining with the total running time;
1-2) inputting the NC codes of the parts to be machined into an NC code analyzer so as to automatically acquire the load time, dead time, tool changing times, total standby time, total starting time, feeding amount during loading, spindle rotating speed and tool model of a certain process.
The invention further improves the load power P in the step 2) l The calculation model is as follows:
in the formula, K l Is a load power coefficient, and is related to workpiece materials, cutters and machine tool performance; f is the feed amount in mm/min under load; a is p Milling depth in mm; lambda [ alpha ] 1 、λ 2 、λ 3 、λ 4 Are all power exponents;
no load power P is The calculation model is as follows:
in the formula, K is The no-load power coefficient is related to the workpiece material, the cutter and the machine tool performance; alpha (alpha) ("alpha") 1 、α 2 Are all power exponentials.
The further improvement of the invention is that the total process energy consumption E calculation model in the 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 : device startup Power, P i : standby power of the device, P is : empty milling power, P l : milling power of the apparatus, E c : energy consumption of tool changing, T s : total time of start, T i : total time of standby, T is : total time of empty milling, T l : milling time, N c : and (5) changing the tool times.
The further improvement of the invention is that the concrete implementation steps of the step 5) are as follows:
5-1) inputting processing information: the processing information comprises processing task process information, a processing machine tool which can be selected for processing a certain process of the part, processing time, processing energy consumption, standby power of the machine tool and transportation energy consumption information among the machine tools, wherein each process is processed on different machine tools;
a startup and shutdown decision model of the machine tool is constructed as follows:
if T SP +T PS ＞T in
then keeping the machine tool unloaded;
else if E SP +E PS ＞C I T in
then keeping the machine tool unloaded
else closing machine tool
In the model: t is SP The conversion time from the shutdown to the normal operation of the equipment; t is PS The conversion time from normal operation to shutdown of the equipment; t is a unit of in Waiting for a machining gap of the equipment; e SP For the equipment to be switched off fromEnergy consumption for conversion to normal operation; e PS Energy consumption for converting equipment from normal operation to shutdown; c I Is the no-load power of the equipment;
5-2) constructing a mathematical model of the planning problem, wherein the optimization targets are processing energy consumption, production cost and completion time, the calculation formulas are respectively shown as formulas (6), (7) and (8), and the constraint conditions are shown as formulas (9) to (13):
processing energy consumption, including production energy consumption, processing gap energy consumption and transportation energy consumption:
the production cost is as follows:
completion time:
T＝max(C 1 ,C 2 ...C m ) (8)
constraint conditions are as follows:
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)
wherein D is ijk A decision variable of the j-th process selection machine k representing the workpiece i,represents the energy consumption of the processing of the j-th process of the workpiece i on the machine k,represents the energy consumption for transporting the workpiece from the jth process to the next process, e k Representing the amount of energy consumed by machine k during the non-processing time,represents the processing cost of the j-th process of the workpiece i on the machine k, c k Indicating the finish time of the machine tool k, c ijk Represents the completion time, p, of the j-th process of task i on machine k i Indicates the total number of steps, M, of the workpiece i ij Optional tool set, s, for Process j representing workpiece i ijk Indicates the start time, t, of the j-th process of task i on machine k ijk Represents the processing time, G, of the j-th process of task i on machine k ijk A selection variable indicating the j-th process selection machine k of the task i;
the energy consumption objective function, the production cost function and the completion time function are respectively expressed by the formulas (6), (7) and (8);
constraint (9): ensuring the completion time of the machine tool k to be the time of the last completion process on the machine tool i;
constraint (10): the completion time of the j-th procedure of the task i on the machine tool k is the sum of the starting time and the procedure time;
constraint (11): the processing sequence of the task i is restricted, and the starting time of the working procedure is ensured to be after the finishing time of the previous working procedure;
constraint (12): ensuring that a plurality of optional machine tools exist in the jth procedure of the task i;
constraint (13): ensuring that only one optional machine tool is selected for processing in the jth procedure of the task i;
5-3) design of NSGA-II Algorithm:
(1) Solving by adopting an improved multi-objective optimization algorithm ED-NSGA-II:
1) And (3) coding and decoding design: designing a two-dimensional coding mode based on a process and a machine tool;
2) Evaluating the individual quality: sorting the individual advantages and disadvantages based on the non-dominated sorting values and the congestion values;
3) The selection mode is as follows: a championship selection method;
4) The crossing mode comprises the following steps: crossing the binary POX;
5) Mutation operation: random variation;
6) A population retention mechanism: a population retention mechanism based on an elite strategy;
(2) The calculation process of the algorithm is as follows:
1) Setting basic parameters of the algorithm: the maximum iteration frequency is 150 times, the population size is 500, and the cross probability is 0.8; the variation probability is 0.1;
2) Initializing a population, and performing non-dominated sorting and congestion value calculation of individuals;
3) Selecting and crossing operation: the total variant is the product of population size and crossover probability: 400, respectively; selecting two individuals by a binary tournament method, wherein the individuals with the lowest non-dominated sorting Rank value and the highest crowding degree are selected preferentially, and carrying out binary POX crossing according to the crossing probability;
4) Selecting a mutation operation: the total variant is the product of the population size and the variant probability: 50; selecting an individual by adopting a binary tournament method, wherein the gene chain gene of the individual is randomly mutated according to the mutation probability;
5) Elite strategy population preservation: combining the new population generated by crossing and mutation with the initially generated population, performing non-dominated sorting and crowding degree calculation on all individuals, and reserving the first 500 excellent individuals;
6) And (3) detection of termination conditions: if the Rank values of all the individuals are 1 in the previous 19 iterations, the iteration is terminated; if not, checking whether the iteration number is 150: if not, turning to the step 3), and entering the next iteration; if the iteration is reached, the iteration is terminated;
7) Outputting an iteration result;
5-5) determining an optimal solution:
because the result obtained by solving the ED-NSGA-II is an optimal solution set and the optimal solution needs to be determined, the weight determination of a plurality of targets is carried out by adopting a DEMATEL + ANP method to determine the optimal solution;
5-6) generation of production plan:
and obtaining corresponding processing energy consumption through the determined optimal solution, decoding and determining the processing machine tool of each procedure and the processing sequence of tasks on each machine tool, and generating a corresponding production optimal configuration result.
Compared with the prior art, the invention has the following beneficial effects:
according to the order-driven energy consumption optimization method for the discrete manufacturing process, variables required for calculating the process are obtained according to NC codes of different processes of parts to be processed, the variables are substituted into an energy consumption calculation model to obtain the energy consumption value of the process on a certain machine tool, so that an energy consumption information base for processing a plurality of parts on different machine tools is established, then a multi-objective optimization algorithm is designed to carry out production planning of discrete processing, and energy consumption optimization of the processing process is realized. The matching method has clear step order and clear level, and provides reference for energy consumption optimization of a system layer in an order-driven discrete manufacturing process. On one hand, compared with the traditional method for obtaining the empirical knowledge of the energy consumption of a certain process through multiple measurements, the energy consumption prediction method based on the machining process parameters, the NC codes, the machine tool characteristics and the machining materials can effectively reduce the redundancy, thereby simplifying the construction of an energy consumption information base of the part machining process. On the other hand, the invention provides an energy consumption optimization method for a discrete manufacturing process on a system level, a plurality of energy consumption indexes of processing energy consumption, transportation energy consumption and processing gap energy consumption are considered, and a startup and shutdown decision model of a machine tool in a processing gap is constructed to further reduce the energy consumption; the multi-objective optimization algorithm NSGA-II is improved for production resource allocation, the completion time is ensured, and the processing cost and the processing energy consumption are reduced.
Description of the drawings:
FIG. 1 is an NC code parser internal work flow diagram;
FIG. 2 is a power curve for a milling process;
FIG. 3 is a diagram of machine state translation;
FIG. 4 is a flow chart of an algorithm designed by the present invention;
FIG. 5 is a decoding gantt chart for an example gene chain;
FIG. 6 is a target value profile;
FIG. 7 is a schematic diagram of a binary POX crossover;
FIG. 8 is a flow chart of the NSGA-II algorithm;
FIG. 9 is a flow chart of the improved NSGA-II algorithm designed by the present invention;
FIG. 10 is a process example part information diagram; wherein, fig. 10 (a) is a part view and a process card of the nozzle base, fig. 10 (b) is a part view and a process card of the conductor 1, fig. 10 (c) is a part view and a process card of the conductor 2, and fig. 10 (d) is a part view and a process card of the FES case;
FIG. 11 is a process example algorithm optimization result; among them, fig. 11 (a) is a convergence graph of the number of optimum curved surfaces, and fig. 11 (b) is an optimum curved surface graph;
FIG. 12 is a Gantt chart of an optimization example.
The specific implementation mode is as follows:
the invention is described in further detail below with reference to the figures and the specific examples.
The invention provides an energy consumption optimization method for an order-driven discrete manufacturing process, which comprises the following steps of:
1) Acquiring load time, dead time, tool changing times, standby total time, starting total time, feeding amount during loading, spindle rotating speed, tool model and the like of a certain procedure for machining a part according to an NC code of the procedure;
2) Substituting the load power and the no-load power calculation model according to the feeding amount, the spindle rotating speed, the cutter model and the like obtained in the step 1) during loading to obtain load power and no-load power;
3) Substituting the variables obtained in the step 1) and the step 2) and the inherent starting power, standby power and tool changing energy consumption of the machine tool selected in the process into a total process energy consumption calculation model to calculate and obtain the process energy consumption value.
4) According to the process energy consumption prediction method of the steps 1), 2) and 3), acquiring the energy consumption values of processing of a certain process of a plurality of different parts in a production batch on different machine tools, and constructing an energy consumption information base.
5) And (4) determining the processing machine tool of each procedure and the processing task on each machine tool according to the energy consumption information base obtained in the step 4), and designing an improved NSGA-II algorithm to ensure that the processing energy consumption is low under the constraints of completion time and processing cost.
The specific operation of the step 1) is as follows:
taking milling as an example, an NC code parser is constructed and implemented by C language programming, and the internal workflow is shown in fig. 1:
obtaining tool changing times N through T instruction c Simultaneously obtaining the model of the cutter used at the moment to obtain the milling width B; acquiring the starting time and the closing time of the machine tool through the M command so as to acquire the total operating time T; acquiring the main shaft rotating speed n of the machine tool through the S instruction; acquiring a feeding amount F through an F instruction; coordinate position points are obtained through the G command, and milling time T is calculated through combining the feeding amount l And empty milling time T is And calculating the standby time by combining the total running time.
The specific operation of the step 2) is as follows:
firstly, a milling power formula is obtained: referring to a mechanical processing manual, under the condition of a certain machine tool, a certain cutter and a certain material, a complex power function relationship exists between milling power and milling parameters:
in the formula, K l : with respect to the material of the workpiece, the tool and the performance of the machine toolA coefficient of off; n: the unit of the rotating speed of the main shaft is r/min; f: feeding amount, unit mm/min; p l Milling power of the apparatus a p : milling depth in mm; b: milling width in mm; lambda [ alpha ] 1 、λ 2 、λ 3 、λ 4 Is a power exponent.
Designing an orthogonal test, configuring an energy consumption acquisition system by adopting Jieney search UMG-604, carrying out statistics on processing energy consumption information by using software GridVis, and processing by using a multiple linear regression method (SPSS) to obtain a milling power formula:
under the condition that a machine tool, a cutter and materials are fixed, the idle milling power is only related to the feeding amount and the rotating speed, and the milling idle power formula is shown as the formula (3):
in the formula, K is The no-load power coefficient is related to the workpiece material, the cutter and the machine tool performance; alpha is alpha 1 、α 2 Is a power exponent.
The power exponent and the no-load power coefficient can be obtained by processing with a multiple linear regression method (SPSS), as shown in formula (4).
P is ＝6.143×10 -16 n 0.982 f 0.124 (4)
Replacing the spindle rotating speed, the feeding amount, the milling depth and the milling width obtained in the step 1) with the milling width in the step (2) to obtain the milling power. And (4) substituting the spindle rotating speed and the feeding amount into the formula (4) to obtain the idle milling power.
The specific operation of the step 3) is as follows:
the milling machine power curve of an actual machining process is shown in fig. 2 and can be divided into a starting stage, a standby stage, a spindle starting stage, an idle stage and a load stage. The total energy consumption can be calculated by equation (5) when the machine tool power is different at each stage:
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 : device startup Power, P i : standby power of the device, P is : empty milling power, P l : milling power of the apparatus, E c : energy consumption of tool changing, T s : total time of start, T i : total standby time, T is : total time of empty milling, T l : milling time, N c : and (5) changing the tool times.
And (3) substituting the equipment starting power, equipment standby power, tool changing energy consumption and the idle milling power, tool changing times, total starting time, total standby time, total load time and total idle milling time which are only related to the equipment into a formula (5) to obtain the energy consumption of the process for machining on a certain machine tool.
The specific operation of the step 4) is as follows:
the method comprises the steps of obtaining the processing technological processes of a plurality of parts in a certain batch, determining an optional machine tool for a certain procedure for processing the parts according to the actual condition of a workshop, calculating the energy consumption predicted value of the certain procedure of the parts on different machine tools by adopting the procedure energy consumption prediction method determined in the previous 3 steps according to NC codes, part materials and machine tool performances, and constructing an energy consumption information base for processing the plurality of parts in the batch on the optional machine tools.
The specific operation of the step 5) is as follows:
1. describing a planning problem:
1. n number of machining tasks, J i ,i＝1,2,3...n；
2. M machine tools, the kth machine tool being M k ,k＝1,2,3...m；
3. Each working task has a plurality of working procedures, O ij The jth process of the ith task is shown.
4. A certain procedure can be processed on a plurality of machine tools, and the processing time, cost and energy consumption on different machine tools are different.
Two sub-problems are involved:
selecting a proper machine tool in each process, namely solving the problem of machine distribution;
and reasonably sequencing the machining processes of a plurality of tasks on each machine tool.
Suppose that:
1. at time t =0, all machines are available and all workpieces can be machined;
2. the time and cost of all processes on available machines are different, the energy consumption value is known, and the transportation time is ignored;
3. the procedures of the same workpiece have sequential process constraints, and the processing sequence of the workpieces is predetermined;
constraint conditions are as follows:
only one part can be processed by the same machine at the same time;
each workpiece can be processed on one machine at a certain moment, and each operation cannot be interrupted midway;
sequential constraint exists between working procedures of the same workpiece, and sequential constraint does not exist between working procedures of different workpieces
Different workpieces having the same priority
Target: the completion time is shortest, the processing energy consumption is lowest, and the cost is lowest
2. Establishing a power-on and power-off decision model:
in the machining process, when a machine tool waits for the next machining procedure, the machine tool often adopts a no-load waiting mode, which causes a large amount of electric energy consumption. In order to reduce the energy consumption of the machining process, a startup and shutdown decision model of the machine tool in the machining gap needs to be constructed.
The machine tool has three states of normal operation (P), no load (I) and closing (S), the conversion between the states needs a certain time and consumes a certain energy, and the conversion relation is shown in figure 3:
the parameters in fig. 3 illustrate: c I 、C P Power of the machine tool in idle and normal operating states, respectively, E xy Representing energy consumption for conversion between states, example E IP Is turned to positive for nullEnergy consumption, T, converted between normal runs xy Representing the time of transition between states, example T IP The elapsed time for the transition from idle to normal operation.
The decision model is:
if T SP +T PS ＞T in
then keeping the machine tool unloaded;
else if E SP +E PS ＞C I T in
then keeping the machine tool unloaded
else closing machine tool
1. If the sum of the conversion time from closing to running and the time from running to closing is greater than the waiting clearance time of the machine tool, the machine tool is kept in no-load, and the machine tool is guaranteed not to be switched on or off to influence the completion time.
2. On the premise that the sum of the conversion time from closing to running and the time from running to closing is not more than the waiting clearance time of the machine tool, if the sum of the conversion energy consumption from closing to running and the energy consumption from running to closing is more than the no-load energy consumption of the machine tool, the machine tool is kept in no-load, and the lowest energy consumption is ensured.
3. Otherwise, the machine tool is shut down. (illustratively: the model primarily considers energy consumption factors, other factors not being considered; e.g., the cost of wear on the machine due to frequent power-on and power-off).
3. Constructing a mathematical model:
the selected optimization targets comprise processing energy consumption, production cost and completion time:
(1) Processing energy consumption:
(2) The production cost is as follows:
(3) Completion time:
T＝max(C 1 ,C 2 ...C m ) (8)
constraint conditions are as follows:
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)
wherein D is ijk Represents the decision variables of the j-th process selection machine k of the task i,represents the energy consumption of the processing of the j-th process of the workpiece i on the machine k,represents the energy consumption for transporting the workpiece from the jth process to the next process, e k Representing the amount of energy consumed by the machine k during the non-processing time,represents the processing cost of the j-th process of the workpiece i on the machine k, c k Indicating the finish time of the machine tool k, c ijk Represents the completion time, p, of the j-th process of task i on machine k i Indicates the total number of steps, M, of the workpiece i ij Optional tool set, s, for Process j representing workpiece i ijk Indicates the start time, t, of the j-th process of task i on machine k ijk Represents the processing time, G, of the j-th process of task i on machine k ijk J-th representing task iThe process selects the selection variables of the machine k.
Equations (6), (7), (8) are the energy consumption objective function, the production cost function and the completion time function, respectively.
Constraint (9): ensuring the completion time of the machine tool k to be the time of the last completion process on the machine tool i;
constraint (10): the completion time of the j-th procedure of the task i on the machine tool k is the sum of the starting time and the procedure time;
constraint (11): the processing sequence of the task i is restricted, and the starting time of the working procedure is ensured to be after the finishing time of the previous working procedure;
constraint (12): ensuring that a plurality of optional machine tools exist in the jth procedure of the task i;
constraint (13): ensuring that only one optional machine tool is selected for processing in the jth procedure of the task i;
3. algorithm design:
the problem is solved by adopting an improved multi-objective optimization algorithm NSGA-II, the algorithm flow is shown in figure 4, and the algorithm is designed as follows:
1. and (3) encoding and decoding: a
And a two-dimensional coding mode based on a process and a machine tool is designed by considering the characteristic of flexible scheduling. The coding scheme is as follows:
wherein the first action sequence is as follows: the first 2 represents the first pass of the workpiece 2, the first 3 represents the first pass of the workpiece 3, the first 1 represents the first pass of the workpiece 1, the second 1 represents the second pass of the workpiece 1, and so on, the sequence of the steps to obtain the machining is [ O 21 O 31 O 11 O 12 O 22 O 32 O 33 O 23 O 13 ]. The second action the machine allocation sequence: the first 1 represents the first process of the corresponding workpiece 2 to select the machine 1 for machining, the first 2 represents the first process of the corresponding workpiece 3 to select the machine 2 for machining,and so on. The corresponding map of the gene chain is shown in FIG. 5.
2. Constructing a non-dominance set:
define 1 (dominant relationship in solution space):
let p i And p j Is any two different individuals if:
(1) For all sub-targets, p i Is not better than p j A difference of f k (p i )≤f k (p j )，k＝(1,2,...,n)；
(2) At least one sub-target exists, such that p i Ratio p j It is preferable that the content of the first component,so that i is q (p i )＜f q (p j )；
Then call p i Dominating p j Can be represented as p i ＞p j 。
Definition 2 (Pareto optimal): in all individuals { p 1 ,p 2 ,...p m In, if for an individual p i Absence of individual p i Such that: p is a radical of formula j ＞p i Then is called p i The Pareto optimal individuals.
Define 3 (Pareto optimal front end or Pareto optimal boundary): namely, the region 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 curved surface):
F r ＝{f 1 (p i ),f 2 (p i ),...f n (p i )}；i＝(1,2,...,m)
the non-dominating set construction process is a process of performing optimized hierarchical division on all solutions. Assuming there are m individuals in the population, if: collectionAll individuals in the group are not controlled by other individuals, namely, the Pareto optimal individual, then the Rank =1 corresponding to the set, and the set is defined as:
then only F is accepted in the remaining individuals 1 Rank =2 for individuals with middle dominance, the set of:
among the remaining individuals then only receive F 2 Rank =3 for individuals corresponding to the individual dominated by the middle individual, the set consisting of:
and by analogy, constructing each hierarchical set. Finally obtaining the Rank values of all individuals, defining p i The Rank value is V i . The NSGA-II algorithm carries out a non-dominated sorting process in each generation, a Rank value is the basis of algorithm selection operation and whether an individual is reserved, and the Rank values of all the individuals are gradually made to be 1 through selection, intersection and variation in the algorithm iteration process, namely all the individuals are pareto optimal solutions, so that an optimal front end is formed.
3. Congestion degree calculation
The concentration is used to describe the target value density of the individual. The congestion degree is mainly used for ranking the advantages and disadvantages of the individuals in each layer. Taking two optimization objectives as examples, refer to fig. 6;
let a body p i Has a value of p on the kth sub-target i .f k Then, the crowdedness of the 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, the crowdedness of the individual i is:
for convenience, data was normalized:
description of the drawings:andthe maximum and minimum fingers on the kth target for all individuals, respectively.
The congestion degree is a basis for the selection operation, and an individual with a higher congestion degree is considered to be more preferable.
4. Selection operation
The selection scheme designed herein is a tournament method, where two individuals, i and j, are randomly generated and selected by comparing their Rank values and crowdedness. 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. in a crossing manner
The crossover pattern is a binary POX crossover pattern, and as shown in fig. 7, two parents are selected by a selection operation, and the genes in the crossover sequence are interchanged at any position where the crossover sequence is generated, thereby generating a new individual.
6. Mutation operation
The purpose of the mutation operation is to ensure the diversity of the solution, the generally set mutation probability value is smaller, if the mutation probability value is set to be too large, the algorithm becomes a random algorithm, and the convergence and the optimization speed of the algorithm are reduced. The mutation operation of the algorithm design is random mutation, for a gene at a certain position of a certain individual, a value between 0 and 1 is randomly generated, and if the value is smaller than the set mutation probability value, a new gene is randomly generated at the position.
7. Population retention mechanism and improvements thereof
The conventional elite retention strategy for NSGA-II is shown in FIG. 8, P i The number of individuals reserved for the elite strategy after the ith evolution is N and R i The number of new individuals generated by cross variation after the ith evolution is set as M, and the new individuals are combined to generate a new population. Then, the new population is constructed without domination and the crowdedness of the individuals in each level is calculated, and then the first N elite individuals are selected to form a next generation elite individual set P i+1 . As shown, may be F 1 、F 2 Is completely reserved, P i Some of the individuals with higher crowdedness are retained. In order to keep the diversity of the algorithm solution and improve the optimizing capability of the algorithm, the algorithm is improved correspondingly, as shown in fig. 9:
P i the number of individuals reserved for the elite strategy after the ith evolution is N.
Step1, constructing a new population without dominance and calculating the crowdedness of individuals in each level, and mainly aiming at providing the Rank value and the aggregation value of the individuals for cross variation.
Step2:P i All individuals in the population are replicated, and a new population consisting of new individuals (the number of M) is generated with the cross-mutated individuals.
Step3, constructing a new population without domination and calculating the crowdedness of the individuals in each layer, and selecting the first N elite individuals to form a next generation elite individual set P i+1 。
4. DEMATEL + ANP index weight determination
The Pareto optimal solution obtained by the ED-NSGA-II algorithm is an optimal solution set, and the final solution needs to be determined, so that the method for determining the index weight based on the DEMATEL + ANP method is provided, and multi-index normalization is performed.
The Decision Making experiment and Evaluation experiment method is called as Degreel Making Trial and Evaluation Laboratory, and calculates the influence degree and influenced degree of each factor and other factors by analyzing the logic relation and direct influence relation between the factors in the system. The method comprises the following steps:
step1 determination of the relationship of the mutual influence between the indices can be quantified with reference to table 1.
TABLE 1 quantitative scores of the relationship of the mutual influence
Step2, normalizing the initial matrix A to obtain a normalized matrix D:
step 3. Overall Effect calculation:
T＝D(I-D) -1 (17)
step 4. Threshold calculation based on the maximum mean entropy algorithm (the maximum mean de-entropy (MMDE) algorithm (Li & Tzeng, 2009)).
ANP method for determining importance relation w between indexes f The importance scores between the indexes can be referred to table 2; TABLE 2 quantitative evaluation table of importance relationship
The overall impact matrix is:
w＝T×w f (18)
the final weights are determined as follows:
5. case analysis
Certain high-voltage switch equipment is mainly developed and produced by enterprises, and has higher requirements on energy conservation and emission reduction in the manufacturing process. Taking a certain machining shop of the company as an example, the workshop has 6 machine tools, 4 part production tasks exist in a certain production batch, and the part information is shown in fig. 10.
The first step is as follows: the energy consumption prediction model is adopted to construct a processing energy consumption information table, and meanwhile, the processing time and the processing cost information are obtained, and the obtained processing information table is shown in a table 3:
TABLE 3 processing information Table
After a certain procedure is finished, the next procedure needs to be carried out on the next machine tool, the influence of the weight of the parts on the energy consumption is ignored, only the distance between the machine tools is considered, and the information table of the energy consumption for transportation between the machine tools is obtained as shown in table 4:
table 4 table of transportation energy consumption information between machine tools
The machine tool has certain energy consumption in standby, and the standby power information table is shown in table 5:
TABLE 5 Standby power information table of machine tool
The second step: and planning and configuring production resources by adopting an improved NSGA-II algorithm. According to the information table, a certain process of the part has different processing energy consumption values, processing costs and processing time on different machine tools, and a multi-objective optimization algorithm is adopted to ensure that the energy consumption, the completion time and the processing costs are optimal in a collaborative mode. The parameters of the algorithm are set as shown in table 6:
TABLE 6 Algorithm parameter information Table
The algorithm convergence graph and the optimal curved surface are shown in fig. 11, the index weight is determined through the DEMATEL + ANP, and the correspondingly obtained scheduling gantt generated by the optimal individual is determined as shown in fig. 12, and the result of the production optimization configuration is as follows:
the machining sequence of the machine tool 1 is as follows: o is 11 →O 42 →O 34 →O 35 ；
The machining sequence of the machine tool 2 is: o is 31 →O 43 →O 22 →O 23 →O 24 ；
The machining sequence of the machine tool 3 is as follows: o is 32 →O 33 →O 14 →O 25 ；
The machining sequence of the machine tool 4 is: o is 41 →O 12 →O 44
The machining sequence of the machine tool 5 is: o is 21 →O 13 →O 45 →O 15
Wherein O is ij The j-th process of the i-th workpiece is shown.
The corresponding target values are: the finishing time is 24min, the energy consumption value is 54.3kw.h, and the processing cost is 105 yuan.
The above description is provided for further details of the present invention with reference to specific production cases, which mainly serve to prove the correctness of the method in practical application, and the embodiments of the present invention should not be considered limited to these, but rather a person skilled in the art can make several simple deductions or substitutions without departing from the spirit of the present invention, and all should be considered as belonging to the scope of patent protection defined by the claims which are filed.
Claims (5)
1. An order driven discrete manufacturing process energy consumption optimization method, comprising the steps of:
1) Acquiring load time, dead time, tool changing times, total standby time, total starting time, feeding amount during loading, spindle rotating speed and tool model of a certain procedure for machining a part according to an NC code of the procedure;
2) Substituting the load power and the no-load power calculation model according to the feeding amount, the spindle rotating speed and the cutter model obtained in the step 1) during loading to obtain load power and no-load power;
3) Substituting the load power and the no-load power obtained in the step 2) and the inherent starting power, the standby power and the tool changing energy consumption of the machine tool selected in the procedure into a total procedure energy consumption calculation model according to the load time, the no-load time, the tool changing times, the standby total time and the starting total time obtained in the step 1), and calculating to obtain an energy consumption value of the procedure;
4) According to the process energy consumption prediction method in the steps 1), 2) and 3), acquiring energy consumption values of the same process for machining a plurality of different parts on different machine tools in one production batch, and constructing an energy consumption information base;
5) And (4) designing an improved NSGA-II algorithm according to the energy consumption information base obtained in the step 4), and determining a processing machine tool in each procedure and a processing task sequence on each machine tool so as to ensure that the processing energy consumption is low under the constraints of completion time and processing cost.
2. The order driven discrete manufacturing process energy consumption optimization method according to claim 1, wherein the step 1) is implemented by the following steps:
1-1) adopting C language programming to construct an NC code parser, wherein the tool changing times N are obtained through a T instruction c Simultaneously obtaining the model of the cutter used at the moment to obtain the milling width B; acquiring the starting time and the closing time of the machine tool through the M command so as to acquire the total operating time T; obtaining machine tool by S commandA main shaft rotating speed n; acquiring a feed amount F through an F instruction; coordinate position points are obtained through the G command, and milling time T is calculated through combining the feeding amount l And empty milling time T is Calculating to obtain standby time by combining with the total running time;
1-2) inputting the NC codes of the parts to be machined into an NC code analyzer so as to automatically acquire the load time, dead time, tool changing times, total standby time, total starting time, feeding amount during loading, spindle rotating speed and tool model of a certain process.
3. The order driven discrete manufacturing process energy consumption optimization method of claim 2, wherein the load power P in step 2) l The calculation model is as follows:
in the formula, K l Is a load power coefficient, and is related to workpiece materials, cutters and machine tool performance; f is the feeding amount in the load, and the unit is mm/min; a is p Milling depth in mm; lambda [ alpha ] 1 、λ 2 、λ 3 、λ 4 Are all power indexes;
no load power P is The calculation model is as follows:
in the formula, K is The no-load power coefficient is related to the workpiece material, the cutter and the machine tool performance; alpha is alpha 1 、α 2 Are all power exponentials.
4. The order driven discrete manufacturing process energy consumption optimization method of claim 2, wherein the total process energy consumption E calculation model 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 : device startup Power, P i : standby power of the device, P is : empty milling power, P l : milling power of the apparatus, E c : energy consumption of tool changing, T s : total time of start, T i : total time of standby, T is : total time of empty milling, T l : milling time, N c : and (5) changing the tool times.
5. The order driven discrete manufacturing process energy consumption optimization method according to claim 3, wherein the step 5) is implemented by the following steps:
5-1) inputting processing information: the processing information comprises processing task process information, a processing machine tool which can be selected for processing a certain process of the part, processing time, processing energy consumption, standby power of the machine tool and transportation energy consumption information among the machine tools, wherein each process is processed on different machine tools;
a startup and shutdown decision model of the machine tool is constructed as follows:
if T SP +T PS ＞T in
then keeping the machine tool unloaded;
else if E SP +E PS ＞C I T in
then keeping the machine tool unloaded
else closing machine tool
In the model: t is SP The transition time from off to normal operation of the plant; t is a unit of PS The conversion time from normal operation to shutdown of the equipment; t is a unit of in Waiting for a machining gap of the equipment; e SP Energy consumption for converting equipment from shutdown to normal operation; e PS Energy consumption for converting equipment from normal operation to shutdown; c I Is the no-load power of the equipment;
5-2) constructing a mathematical model of the planning problem, wherein the optimization targets are processing energy consumption, production cost and completion time, the calculation formulas are respectively shown as formulas (6), (7) and (8), and the constraint conditions are shown as formulas (9) to (13):
the processing energy consumption comprises the production energy consumption, the processing gap energy consumption and the transportation energy consumption:
the production cost is as follows:
completion time:
T＝max(C 1 ,C 2 ...C m ) (8)
constraint conditions are as follows:
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)
wherein D is ijk A decision variable of the j-th process selection machine k representing the workpiece i,represents the energy consumption of the processing of the j-th process of the workpiece i on the machine k,represents the energy consumption for transporting the workpiece i from the jth process to the next process, e k Indicating that machine k is not addedThe energy consumption of the working time is reduced,represents the processing cost of the j-th process of the workpiece i on the machine k, c k Indicating the finish time of the machine tool k, c ijk Represents the completion time, p, of the j-th process of task i on machine k i Indicates the total number of steps, M, of the workpiece i ij Optional tool set, s, for process j representing workpiece i ijk Indicates the start time, t, of the j-th process of task i on machine k ijk Represents the processing time, G, of the j-th process of task i on machine k ijk A selection variable indicating the j-th process selection machine k of the task i;
the energy consumption objective function, the production cost function and the completion time function are respectively expressed by the formulas (6), (7) and (8);
constraint (9): ensuring the completion time of the machine tool k to be the time of the last completion process on the machine tool i;
constraint (10): the completion time of the j-th procedure of the task i on the machine tool k is the sum of the starting time and the procedure time;
constraint (11): the processing sequence of the task i is constrained, and the starting time of the working procedure is ensured to be after the finishing time of the previous working procedure;
constraint (12): ensuring that a plurality of optional machine tools exist in the jth procedure of the task i;
constraint (13): ensuring that only one optional machine tool is selected for processing in the jth procedure of the task i;
5-3) design of NSGA-II Algorithm:
(1) Solving by adopting an improved multi-objective optimization algorithm ED-NSGA-II:
1) And (3) coding and decoding design: designing a two-dimensional coding mode based on a process and a machine tool;
2) Evaluation of individual quality: sorting the individual advantages and disadvantages based on the non-dominated sorting values and the congestion values;
3) The selection mode is as follows: a tournament selection method;
4) The crossing mode is as follows: crossing the binary POX;
5) Mutation operation: random variation;
6) A population retention mechanism: a population retention mechanism based on an elite strategy;
(2) The calculation process of the algorithm is as follows:
1) Setting basic parameters of the algorithm: the maximum iteration frequency is 150 times, the population size is 500, and the cross probability is 0.8; the variation probability is 0.1;
2) Initializing a population, and performing non-dominated sorting and congestion value calculation of individuals;
3) Selecting and crossing operation: the total variant is the product of population size and crossover probability: 400, respectively; selecting two individuals by a binary tournament method, wherein the individual with the lowest non-dominated sorting Rank value and the highest crowding degree is selected preferentially, and carrying out binary POX crossing according to the crossing probability;
4) Selecting a mutation operation: the total variant is the product of the population size and the variant probability: 50; selecting an individual by adopting a binary tournament method, wherein the gene chain gene of the individual is randomly mutated according to the mutation probability;
5) Elite strategy population preservation: combining the new population generated by crossing and mutation with the initially generated population, performing non-dominated sorting and crowding degree calculation on all individuals, and reserving the first 500 excellent individuals;
6) And (3) detection of termination conditions: if the Rank values of all the individuals are 1 in the previous 19 iterations, the iteration is terminated; if not, checking whether the iteration number is 150: if not, turning to the step 3), and entering the next iteration; if the iteration is reached, the iteration is terminated;
7) Outputting an iteration result;
5-5) determining an optimal solution:
because the result obtained by solving the ED-NSGA-II is an optimal solution set and the optimal solution needs to be determined, the weight of a plurality of targets is determined by adopting a DEMATEL + ANP method to determine the optimal solution;
5-6) generation of production plan:
and obtaining corresponding processing energy consumption through the determined optimal solution, decoding and determining the processing machine tool of each procedure and the processing sequence of tasks on each machine tool, and generating a result of corresponding production optimization configuration.
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