CN105652791A - Order-driven discrete manufacturing process energy consumption optimization method - Google Patents
Order-driven discrete manufacturing process energy consumption optimization method Download PDFInfo
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Abstract
The invention discloses an order-driven discrete manufacturing process energy consumption optimization method. A precondition of realizing energy consumption optimization of a discrete manufacturing process is to acquire operation energy consumption information of a processing process. Therefore, in the invention, firstly, according to machine tool performance, a processing material, a processing technology parameter and prediction of NC code realization operation energy consumption, data support is provided for resource configuration which is performed on low energy consumption production of a system layer; and then, an improved multi-object optimization intelligence algorithm is designed and used to carry out production resource optimization configuration of the discrete manufacturing process so that optimization objects of finishing time, processing cost, processing energy consumption and the like are guaranteed to be coordinated and optimized. In the invention, a new idea is provided for the energy consumption optimization of the order-driven discrete manufacturing process and a reference is provided for realizing low energy consumption production and green manufacturing.
Description
Technical field:
The invention belongs to advanced manufacture and technical field of automation, particularly relate to a kind of Discrete Manufacturing Process energy consumption optimization method towards order-driven market.
Background technology:
Along with the development of production, the customized production of order-driven market becomes main flow. Single manufacturing concern often faces the multiple order demand from different client, and therefore, the mode of production is the discrete manufacturing mode towards multi-varieties and small-batch, and its course of processing is the different sub-process of parts machining or complex process that is in parallel or that be composed in series. And on the other hand, China has been the world now is the first manufacture big country, and production output accounts for the 20% of world's Gross Output. But, the development of China's manufacturing industry take high energy consumption as cost, and production energy consumption accounts for the 80% of China's industry total energy consumption, unit industrial added value energy consumption level is 2.5 times of world average level, 3.3 times of the U.S., 7 times of Japan, also higher than developing countries such as Brazil, Mexico. China has been CO2 emissions first big country now, and increment also accounts for more than the 70% of the whole world, and the increasing pressure of the energy-saving and emission-reduction faced in the world is big. It is therefore desirable to probe into a kind of Discrete Manufacturing Process resource allocation optimization method considering energy consumption, production is reasonably arranged, ensure low cost, less energy-consumption and the promptness produced.
For ensureing the less energy-consumption of manufacturing processed, domestic and international experts and scholars conduct extensive research, and achieve certain achievement, but there is certain limitation, mainly contain:
1) focus is mainly gathered in equipment aspect by current research, carry out the optimal control of lathe key components and parts, improvement and replacing, or carry out the process parameter optimizing of the course of processing, to realize the target that less energy-consumption is produced, cannot ensure that less energy-consumption is produced from the height of manufacturing system layer.
2) research carrying out less energy-consumption production in manufacturing system aspect is mainly gathered in the flow shop such as iron and steel, tire, and the research of Discrete Manufacturing Process is fewer. On the other hand, ensureing that system layer less energy-consumption need of production obtains the priori of certain procedure power consumption of polymer processing on certain lathe, many researchs lack the research of this aspect, and the less energy-consumption production planning thus carried out lacks practicality.
3) when carrying out manufacturing processed and optimize, optimization aim comprises multiple targets such as completion date, tooling cost, lathe load factor, power consumption of polymer processing, and the intelligent optimization algorithm that many research adopts cannot ensure that the coordination of multiple target is optimum.
Knowing based on above problem, current research also exists certain limitation and leak, thus studies the Discrete Manufacturing Process energy consumption optimization method of order-driven market, the energy-saving and emission-reduction of manufacturing concern is of great importance. First the prerequisite realizing the energy optimization of Discrete Manufacturing Process is the process energy consumption prediction carrying out the course of processing. Realize the prediction of energy consumption according to machine tool capability, work material, working process parameter, it is that system layer is optimized less energy-consumption and produced and provide data supporting. Secondly, the method adopts multiple-objection optimization intelligent algorithm, ensures the optimization aim coordination optimizations such as completion date, tooling cost, power consumption of polymer processing. Thus, the method compensate for the deficiency of traditional method greatly.
Summary of the invention:
It is an object of the invention to provide the Discrete Manufacturing Process energy consumption optimization method of a kind of order-driven market, it may be achieved course of processing process energy consumption is predicted, and realizes discrete course of processing energy optimization.
In order to achieve the above object, the present invention takes following technical scheme to realize:
The Discrete Manufacturing Process energy consumption optimization method of order-driven market, comprises the following steps:
1) according to the NC Code obtaining of certain procedure of part to be processed process the load time of this operation, dead time, number of changing knife, standby total time, start total time, load time the amount of feed, the speed of mainshaft and cutter model;
2) according to step 1) in obtain load time the amount of feed, the speed of mainshaft, cutter model, substitute into load power and no-load power computation model, obtain load power and no-load power;
3) according to step 1) load time that obtains, dead time, number of changing knife, standby total time, start total time, step 2) load power that obtains and no-load power, and the intrinsic starting power of the selected lathe of this procedure, standby power and tool changing energy consumption, substitute into total operation energy consumption calculation model, calculate this process energy consumption value;
4) according to step 1), step 2) and step 3) process energy consumption Forecasting Methodology, obtain the power consumption values of same operation processing on different lathe of multiple different part in a production batch, build consumption information storehouse;
5) according to step 4) the consumption information storehouse that obtains, the NSGA-II algorithm that design improves, carry out the determination of processing tasks order on the determination of every procedure machining tool and every platform lathe, for ensureing under the constraint of completion date, tooling cost so that power consumption of polymer processing is low.
The further improvement of the present invention is, step 1) specific implementation step as follows:
1-1) adopt C language programming to build NC code parser, wherein, obtain number of changing knife N by T instructionc, the model simultaneously obtaining the cutter now used is to obtain milling width B; Obtained start time and the turn-off time of lathe by M instruction, thus obtain and calculate operation total time T; The speed of mainshaft n of lathe is obtained by S instruction; Amount of feed f is obtained by F instruction; Obtain coordinate position point by G instruction, calculate milling time T by being incorporated into gaugelWith empty milling time Tis, total time calculate standby time in conjunction with operation;
1-2) by the NC code of part to be processed input NC code parser, with automatically obtain the load time of certain procedure of processing, dead time, number of changing knife, standby total time, start total time, load time the amount of feed, the speed of mainshaft and cutter model.
The further improvement of the present invention is, step 2) in load power PlComputation model is as follows:
In formula, KlFor load power coefficient, relevant to workpiece material, cutter, machine tool capability;F is amount of feed during load, and unit is r/min; apFor milling depth, unit mm; ��1����2����3������4It is power exponent;
No-load power PisComputation model is as follows:
In formula, KisFor no-load power coefficient, relevant to workpiece material, cutter, machine tool capability; ��1����2It is power exponent.
The further improvement of the present invention is, step 3) in total process energy consumption E computation model as follows:
E=PsTs+PiTidt+PisTis+PlTl+NcEc(5)
In formula, Ps: device start power, Pi: device standby power, Pis: empty milling steel fiber, PlEquipment milling steel fiber, Ec: tool changing energy consumption, Ts: start total time, Ti: standby total time, Tis: empty milling total time, Tl: milling steel fiber, Nc: number of changing knife.
The further improvement of the present invention is, step 5) specific implementation step as follows:
5-1) machining information input: machining information comprises processing tasks process information, carry out certain procedure of part processes the transport consumption information between alternative machining tool, process period of every procedure processing on different lathe, power consumption of polymer processing, the standby power of lathe, lathe;
Build the switching on and shutting down decision model of lathe, as follows:
ifTSP+TPS>Tin
Then: keep lathe unloaded;
elseifESP+EPS>CITin
Then keeps lathe unloaded
Else closes lathe
In model: TSPFor equipment is from the transformation time being closed to normal operation; TPSFor equipment is from the transformation time normally running to closedown; TinFor the machining gap waiting time of equipment; ESPFor equipment is from the conversion energy consumption being closed to normal operation; EPSFor equipment is from the conversion energy consumption normally running to closedown; CIFor the no-load power of equipment;
5-2) build the mathematical model of planning problem, wherein, optimization aim is power consumption of polymer processing, production cost and completion date, calculation formula is respectively such as formula, shown in (6), (7), (8), constraint condition is such as formula shown in (9)��(13):
Power consumption of polymer processing, comprises production energy consumption, machining gap energy consumption and transport energy consumption:
Production cost:
Completion date:
T=max (C1,C2...Cm)(8)
Constraint condition:
Ck=max (cijk) i=1,2 ..., n; J=1,2 ..., pi; K �� Mij(9)
cijk=sijk+tijkI=1,2 ..., n; J=1,2 ..., pi; K=1,2 ..., m (10)
sijk-ci(j-1)l��0(11)
Wherein, DijkThe jth procedure of expression task i selects the decision variable of machine k,The energy consumption of the processing of the jth procedure of expression workpiece i on machine k,Represent the transport energy consumption of jth procedure to next process of workpiece i, ekRepresent the energy input of machine k non-process period,Represent the tooling cost of the jth procedure of workpiece i on machine k, ckRepresent the completion date of lathe k, cijkThe completion date of the jth procedure of expression task i on machine k, piRepresent the operation sum of workpiece i, MijRepresent the optional lathe collection of operation j of workpiece i, sijkThe time opening of the jth procedure of expression task i on machine k, tijkThe process period of the jth procedure of expression task i on machine k, GijkThe jth procedure of expression task i selects the choice variable of machine k;
Formula (6), (7), (8), be respectively power dissipation obj ectives function, production cost function and completion date function;
Constraint condition (9): the completion date of guarantee lathe k is the time of last completion procedures on lathe i;
The completion date of the jth procedure of constraint condition (10): task i on lathe k is its time opening and activity time sum;
Constraint condition (11): the machining sequence constraint of task i, ensured that the time opening of operation was after a upper procedure end time;
Constraint condition (12): ensure that the jth procedure of task i has multiple optional lathe;
Constraint condition (13): ensure that the jth procedure of task i only selects an optional lathe to process;
5-3) the design of NSGA-II algorithm:
(1) the multi-objective optimization algorithm ED-NSGA-II improved is adopted to solve:
1) coding and decoding design: design is based on the two-dimensional encoded mode of operation and lathe;
2) individual superior and inferior evaluating: adopt and carry out individual trap queuing based on non-dominated ranking value and crowded angle value;
3) selection mode: algorithm of tournament selection method;
4) interleaved mode: binary POX intersects;
5) mutation operation: random variation;
6) population retention mechanism: based on the population retention mechanism of elitism strategy;
(2) computation process of algorithm is:
1) basic parameter of set algorithm: maximum iteration time is 150 times, population size is 500, and crossover probability is 0.8; Variation probability is 0.1;
2) initialize population, carries out individual non-dominated ranking and crowded angle value calculates;
3) interlace operation is selected: the total individuality of variation is that population size and crossover probability are long-pending: 400; Selecting binary competitive bidding match method to select two individualities, the individuality that wherein non-dominated ranking Rank value is minimum and crowding is the highest is preferentially chosen, and carries out binary POX intersection according to crossover probability;
4) mutation operation is selected: the total individuality of variation is that population size is long-pending with variation probability: 50; It is individual that same employing binary competitive bidding match method selects certain, and genes of individuals chain gene is according to variation probability random variation;
5) elitism strategy population retains: the new population produce intersection, variation and the initial population merging produced, and the non-dominated ranking and the crowding that carry out all individualities calculate, and retain front 500 excellent individual;
6) end condition detection: if it be all the Rank value of all individualities is 1 that the Rank value of current all individualities is front 19 iteration of 1, then termination of iterations; If not meeting, then check and whether reach iteration number of times 150: do not reach, then proceed to step 3), enter next iteration; Reach then termination of iterations;
7) iteration result is exported;
5-4) optimum solution is determined:
Solve due to ED-NSGA-II that to obtain result be optimal solution set, it is necessary to carry out the determination of optimum solution, therefore adopt the weight carrying out multiple target based on DEMATEL+ANP method to determine, carry out optimum solution and determine;
5-5) the generation of production planning:
By the optimum solution determined, obtain corresponding power consumption of polymer processing, decode the processing sequence of task on the machining tool determining every procedure and Ge Tai lathe simultaneously, and generate the result of corresponding Optimizing manufacture configuration.
Relative to prior art, the useful effect that the present invention has is:
The Discrete Manufacturing Process energy consumption optimization method of order-driven market provided by the invention, according to the NC code of the different operations of part to be processed, obtain the variable required for this operation of calculating processing, substitute into the power consumption values that energy consumption calculation model obtains this operation and processes on certain lathe, thus set up the consumption information storehouse of multiple part processing on different lathe, then the production planning that multi-objective optimization algorithm carries out discrete processing is designed, it is achieved the energy optimization of the course of processing. The step orderliness of this matching method is clear, level is clear and definite, and the energy optimization carrying out system layer for the Discrete Manufacturing Process of order-driven market provides reference. On the one hand, the energy consumption Forecasting Methodology based on course of processing parameter, NC code, lathe property and work material that the present invention proposes, obtain with traditional repetitive measurement the energy consumption of certain procedure experimental knowledge method compared with, can effectively reduce redundancy, thus simplify the structure in part manufacturing procedure consumption information storehouse. On the other hand, the present invention proposes the energy consumption optimization method of the Discrete Manufacturing Process in system level, consider power consumption of polymer processing, transport energy consumption, the multiple energy consumption index of machining gap energy consumption, and construct the switching on and shutting down decision model of lathe at machining gap to reduce energy consumption further; Improve multi-objective optimization algorithm NSGA-II and carry out resources of production distribution, ensure that completion date, reduce tooling cost and power consumption of polymer processing.
Accompanying drawing illustrates:
Fig. 1 is NC code parser internal work flow journey figure;
Fig. 2 is certain Milling Processes powertrace;
Fig. 3 is conditions of machine tool transforming relationship figure;
Fig. 4 is the algorithm flow figure of inventive design;
Fig. 5 is case gene strand decoding Gantt chart;
Fig. 6 is target value distribution plan;
Fig. 7 is binary POX intersection schematic diagram;
Fig. 8 is NSGA-II algorithm flow figure;
Fig. 9 is the NSGA-II algorithm flow figure of the improvement of inventive design;
Figure 10 is processing instance part information pattern; Wherein, Figure 10 (a) is parts drawing and the process sheet of spout seat, Figure 10 (b) is parts drawing and the process sheet of conductor 1, Figure 10 (c) is parts drawing and the process sheet of conductor 2, and Figure 10 (d) is parts drawing and the process sheet of FES housing;
Figure 11 is processing instance algorithm optimum result; Wherein, Figure 11 (a) is the convergence curve figure of optimum curved surface number of individuals, Figure 11 (b) is optimum curved surface figure;
Figure 12 is the Gantt chart optimizing example.
Embodiment:
Below in conjunction with accompanying drawing and specific examples, the present invention is described in further detail.
The Discrete Manufacturing Process energy consumption optimization method of order-driven market provided by the invention, comprises the following steps:
1) according to the NC Code obtaining of certain procedure of part to be processed process the load time of this operation, dead time, number of changing knife, standby total time, start total time, load time the amount of feed, the speed of mainshaft, cutter model etc.;
2) according to step 1) in obtain load time the amount of feed, the speed of mainshaft, cutter model etc., substitute into load power and no-load power computation model, obtain load power and no-load power;
3) according to step 1) and step 2) variable that obtains and the intrinsic starting power of the selected lathe of this procedure, standby power and tool changing energy consumption, substitute into total operation energy consumption calculation model, calculate this process energy consumption value.
4) according to step 1), step 2) and step 3) process energy consumption Forecasting Methodology, obtain the power consumption values of certain procedure processing on different lathe of multiple different part in a production batch, build consumption information storehouse.
5) according to step 4) the consumption information storehouse that obtains, carry out the determination of processing tasks on the determination of every procedure machining tool and every platform lathe, the NSGA-II algorithm that design improves, ensures under the constraint of completion date, tooling cost so that power consumption of polymer processing is low.
Described step 1) be specifically operating as:
For Milling Process, build NC code parser, by C language programming realization, internal work flow journey as shown in Figure 1:
Number of changing knife N is obtained by T instructionc, the model simultaneously obtaining the cutter now used is to obtain milling width B; Obtained start time and the turn-off time of lathe by M instruction, thus obtain and calculate operation total time T; The speed of mainshaft n of lathe is obtained by S instruction; Amount of feed f is obtained by F instruction; Obtain coordinate position point by G instruction, calculate milling time T by being incorporated into gaugelWith empty milling time Tis, total time calculate standby time in conjunction with operation.
Described step 2) be specifically operating as:
First it is obtain milling steel fiber formula: with reference to mechanical processing technique handbook, when lathe, cutter and material are certain, between milling steel fiber and milling parameter, there is complicated power function relationship:
In formula, K: to workpiece material, cutter, coefficient that machine tool capability is relevant; N: the speed of mainshaft, unit is r/min; F: the amount of feed, unit mm/min;PlEquipment milling steel fiber, ap: milling depth, unit mm; B: milling width, unit mm; ��1����2����3������4For power exponent.
Design orthogonal test, adopts outstanding Buddhist nun to look into UMG-604 and configures energy consumption acquisition system, utilize software GridVis to carry out the statistics of power consumption of polymer processing information, by utilizing multiple linear regression analysis method (SPSS) process can obtain milling steel fiber formula:
When lathe, cutter and material are certain, empty milling steel fiber is only relevant to the amount of feed and rotating speed, and milling no-load power formula is such as formula shown in (3):
In formula, KisFor no-load power coefficient, relevant to workpiece material, cutter, machine tool capability; ��1����2For power exponent.
Utilize multiple linear regression analysis method (SPSS) process can obtain power exponent and no-load power coefficient, shown in (4).
Pis=6.143 �� 10-16n0.982f0.124(4)
The speed of mainshaft that step 1 obtained, the amount of feed, milling depth, milling width substitute into formula (2) and obtain milling steel fiber. The speed of mainshaft, the amount of feed are substituted into formula (4) and obtains sky milling steel fiber.
Described step 3) be specifically operating as:
Once the milling machine powertrace of the actual course of processing is as shown in Figure 2, the unloading phase of can being divided into, the standby stage, main shaft unloading phase, unloaded stage and load stage. Different at the power of each stage lathe, total energy consumption calculates by formula (5):
E=PsTs+PiTidt+PisTis+PlTl+NcEc(5)
In formula: Ps: device start power, Pi: device standby power, Pis: empty milling steel fiber, PlEquipment milling steel fiber, Ec: tool changing energy consumption, Ts: start total time, Ti: standby total time, Tis: empty milling total time, Tl: milling steel fiber, Nc: number of changing knife.
Empty milling steel fiber that only relevant to equipment device start power, device standby power, tool changing energy consumption and the first two step are obtained, cut power, number of changing knife, startup total time, standby total time, total loading time, empty milling total time substitute into the energy consumption that formula (5) obtains this operation and processes on certain lathe.
Described step 4) be specifically operating as:
Obtain the machining process of multiple part in certain batch, and according to workshop practical situation, determine the optional lathe of certain procedure for processing parts, according to NC code, part material and machine tool capability, adopt the front determined process energy consumption Forecasting Methodology of 3 steps, calculate the energy consumption predictor of part procedure in different machine tooling, build the consumption information storehouse that this batch of multiple part is processed on its optional lathe.
Described step 5) be specifically operating as:
One, planning problem describes:
1, n processing tasks, Ji, i=1,2,3...n;
2, m platform machining tool, kth platform lathe is Mk, k=1,2,3...m;
3, each processing tasks has multiple working procedure, OijRepresent the jth procedure of i-th task.
4, certain procedure can be processed on the lathe of multiple stage, the time of processing, cost difference, energy input difference on different lathe.
Comprise two subproblems:
Every procedure selects suitable lathe, i.e. machine assignment problem;
On every platform lathe, the manufacturing procedure of multiple task reasonably sorts.
Assume:
1, the t=0 moment, all machines are all available, and all workpiece all can be processed;
2, the time of the processing of all process steps in available machines used, cost is different, power consumption values is known, and ignores haulage time;
3, having successively process constraint between the operation of same workpiece, its processing sequence is predefined;
Constraint condition:
The same moment, same machine can only process a part;
Each workpiece at a time can only be processed on a machine, can not interrupt each operation halfway;
Successively constraint is had, not successively constraint between the operation of different workpieces between the operation of same workpiece
Different workpieces has identical priority
Target: completion date is the shortest, power consumption of polymer processing is minimum, and cost is minimum
Two, switching on and shutting down decision model builds:
In the course of processing, lathe during one manufacturing procedure, often takes the mode of unloaded wait under waiting, this can cause a large amount of power consumptions. For reducing the energy consumption of the course of processing, it is necessary to build the switching on and shutting down decision model of lathe at machining gap.
Lathe exists and normal runs (P), unloaded (I), cuts out (S) three states, transforms and need the regular hour and consume certain energy between state, transforming relationship as shown in Figure 3:
In Fig. 3, parameter illustrates: CI��CPIt is respectively the power of lathe under idle running and normal operating condition, ExyRepresent between state the energy consumption transformed, example EIPThe energy consumption transformed for idling up between normal operation, TxyRepresent between state the time transformed, example TIPFor idling up to the normal time running the consumption transformed.
Decision model is:
ifTSP+TPS>Tin
Then: keep lathe unloaded;
elseifESP+EPS>CITin
Then keeps lathe unloaded
Else closes lathe
If 1 transformation time being closed to operation and the time sum running to closedown are greater than the off time that lathe waits, then keep lathe unloaded, ensure that lathe switching on and shutting down do not affect completion date.
2, under the prerequisite being not more than the off time that lathe waits at the transformation time being closed to operation and the time sum that runs to closedown, if the conversion energy consumption being closed to operation and the energy consumption sum running to closedown are greater than the unloaded energy consumption of lathe, then keep lathe unloaded, ensure that energy consumption is minimum.
3, other situation then closes lathe. (illustrate: energy consumption factor mainly considered by this model, and other factors is not considered. Such as frequent switching on and shutting down are to the loss cost of lathe).
Three, mathematical model model construction:
The optimization aim chosen has power consumption of polymer processing, production cost and completion date:
(1) power consumption of polymer processing:
(2) production cost:
(3) completion date:
T=max (C1,C2...Cm)(8)
Constraint condition:
Ck=max (cijk) i=1,2 ..., n; J=1,2 ..., pi; K �� Mij(9)
cijk=sijk+tijkI=1,2 ..., n; J=1,2 ..., pi; K=1,2 ..., m (10)
sijk-ci(j-1)l��0(11)
Wherein, DijkThe jth procedure of expression task i selects the decision variable of machine k,The energy consumption of the processing of the jth procedure of expression workpiece i on machine k,Represent the transport energy consumption of jth procedure to next process of workpiece i, ekRepresent the energy input of machine k non-process period,Represent the tooling cost of the jth procedure of workpiece i on machine k, ckRepresent the completion date of lathe k, cijkThe completion date of the jth procedure of expression task i on machine k, piRepresent the operation sum of workpiece i, MijRepresent the optional lathe collection of operation j of workpiece i, sijkThe time opening of the jth procedure of expression task i on machine k, tijkThe process period of the jth procedure of expression task i on machine k, GijkThe jth procedure of expression task i selects the choice variable of machine k.
Formula (6), (7), (8), be respectively power dissipation obj ectives function, production cost function and completion date function.
Constraint (9): the completion date of guarantee lathe k is the time of last completion procedures on lathe i;
The completion date of the jth procedure of constraint (10): task i on lathe k is its time opening and activity time sum;
The machining sequence constraint of constraint (11): task i, ensured that the time opening of operation was after a upper procedure end time;
Constraint (12): ensure that the jth procedure of task i has multiple optional lathe;
Constraint (13): ensure that the jth procedure of task i only selects an optional lathe to process;
Four, algorithm design:
Adopting the multi-objective optimization algorithm NSGA-II improved to carry out solving of problem, as shown in Figure 4, algorithm design is as follows for algorithm flow:
1, coding and decoding:
Consider the feature of flexible scheduling, devise the two-dimensional encoded mode based on operation and lathe. Encoding scheme as:
Wherein the first behavior process sequence row: first 2 represents the first operation of workpiece 2, representing the first operation of workpiece 3 for first 3, first 1 represents the first operation of workpiece 1, and the 2nd 1 represents the second operation of workpiece 1, analogizing with this, the process sequence obtaining processing is [O21O31O11O12O22O32O33O23O13]. 2nd behavior lathe assigned sequence: first the 1 first operation representing corresponding workpiece 2 selects lathe 1 to process, first the 2 first operation representing corresponding workpiece 3 selects lathe 2 to process, and analogizes with this. Scheduling Gantt chart corresponding to this fragment gene chain is as shown in Figure 5.
2, non-dominant collection builds:
Definition 1 (the domination relation in solution space):
If piAnd pjFor any two Different Individual, if:
(1) to all sub-goals, piUnlike pjDifference, i.e. fk(pi)��fk(pj), k=(1,2 ..., n);
(2) at least there is a sub-goal so that piCompare pjIt is good,Make i.e. fq(pi)<fq(pj);
So then claim piDomination pj, can represent for pi> pj��
Definition 2 (Pareto is optimum): at all individuality { p1,p2,...pmIn, if for individual pi, there is not individual piMake: pj> pi, so claim piFor Pareto optimum individual.
Definition 3 (the optimum front end of Pareto or Pareto Optimal Boundary): the region (two-dimensional space is then curve, and three-dimensional space is curved surface) that the target value that namely all Pareto optimum individuals are corresponding is formed:
Fr={ f1(pi),f2(pi),...fn(pi); I=(1,2 ..., m)
Non-dominant collection building process is the process that all solutions are optimized distinguishing hierarchy. Assume that population has m individuality, if: setIn all individualities by other individual domination, be Pareto optimum individual, so the Rank=1 of this set correspondence, defining this set is:
So only by F in residue individuality1The Rank=2 of the individual corresponding individuality of middle individuality domination, the set of composition is:
So only by F during residue is individual2The Rank=3 of the individual corresponding individuality of middle individuality domination, the set of composition is:
Analogize with this, build each level set. Finally obtain the Rank value of all individualities, definition piRank value be Vi. The every Dai Junhui of NSGA-II algorithm carries out non-dominated ranking process, Rank value is algorithms selection operation and the individual foundation whether retained, algorithm iteration process is by selecting, intersect, make a variation the Rank value that progressively can make all individualities to be 1, namely all individualities are handkerchief tired holder optimization solution, thus form optimum front end.
3, crowding degree calculates
Concentration class is used for describing individual target value density. Crowding is mainly used in the trap queuing of the individuality of each level inside. For two optimization aim, with reference to Fig. 6;
If individual piThe value on kth sub-goal be pi.fk, then the crowding of individual i is:
pi.dist=(pi+1.f1-pi-1.f1)+(pi+1.f2-pi-1.f2)(14)
If fruit has m target value, the crowding of so individual i is:
In order to convenient, data are carried out stdn:
Illustrate:WithIt is respectively the maximum value of all individualities in kth target and most little finger of toe.
Crowding is the foundation of selection operation, and the individuality that crowding is more big, is considered as more excellent.
4, selection operation
The selection mode designed herein is championship method, and random generation two individual i and j, are selected by the Rank value and crowding comparing them. Chosen process is as follows:
If:Vi<Vj
Then:ChooseVi
Elseif:Vi=Vj&&pi.dist>pj.dist
Then:ChooseVi
Else:
Then:ChooseVj
5, interleaved mode
Interleaved mode is binary POX interleaved mode, as shown in Figure 7, selects two parent individualities by selection operation, and the gene that the position that generation exchange arranges arbitrarily carries out these row exchanges, and produces new individuality.
6, mutation operation
The object of mutation operation is to ensure the diversity separated, and the variation probable value of general setting is smaller, if variation probable value arranges excessive, then algorithm becomes random algorithm, reduces convergence and optimizing speed. The mutation operation of this algorithm design is random variation, and for the gene of certain certain position individual, the random value produced between 0-1, if this value is less than the variation probable value of setting, then this position produces new gene at random.
7, population retention mechanism and improvement thereof
Elite's retention strategy of traditional NSGA-II as shown in Figure 8, PiBeing the individuality that after evolving for i-th time, elitism strategy retains, number is N, RiBeing the new individuality that after evolving for i-th time, cross and variation produces, number is set as M, both is merged, produces new population. Then new population is carried out the structure of non-dominant and the calculating of the inner individual crowding of each level, N number of elite individuality composition elite of future generation individuality collection P before then selectingi+1. As shown in the figure, it is possible to F1��F2In individuality retain completely, PiIn the higher individuality of part crowding retained. For keeping the diversity of algorithm solution and improve the optimizing ability of algorithm, carry out correspondingly improving to algorithm, as shown in Figure 9:
PiBeing the individuality that after evolving for i-th time, elitism strategy retains, number is N.
Step1: new population carries out the structure of non-dominant and the calculating of the inner individual crowding of each level, and main purpose provides individual Rank value for cross and variation and assembles angle value.
Step2:PiIn all individualities copy, with the new population that the individual generation new individual (quantity is M) of cross and variation forms.
Step3: new population carries out the calculating building the inner individual crowding with each level of non-dominant, the individual composition of N number of elite elite of future generation individuality collection P before selectingi+1��
Five, the determination of DEMATEL+ANP index weight
The Pareto optimization solution that ED-NSGA-II algorithm is tried to achieve is for optimizing disaggregation, it is necessary to carries out the determination of last solution, therefore proposes the determination carrying out index weight based on DEMATEL+ANP method, carries out multi objective index normalization method.
DEMATEL (DecisionMakingTrialandEvaluationLaboratory) is called " decision experiments and evaluation experimental method ", the method, by logic relation and direct interact relation between each key element in analytical system, calculates influence degree and the degree of being affected of each factor and other factors. Step is as follows:
Step1: the determination of the relation that influences each other between index, can refer to table 1 and quantizes.
Table 1 influences each other relation Quantitative marking table
Step2: initial matrix A stdn obtains stdn matrix D:
Step3: entire effect calculates:
T=D (I-D)-1(17)
Step4: the valve value based on Largest Mean entropy algorithm (themaximummeande-entropy (MMDE) algorithm (Li&Tzeng, 2009)) calculates.
The important degree relation w that ANP method is used between agriculture productsf, between index, important degree scoring can refer to table 2; Table 2 important degree relation Quantitative marking table
Entire effect matrix is:
W=T �� wf(18)
Final weight is determined as follows:
Six, case analysis
, there is higher requirement in certain high-tension switch gear focus development research and production enterprise for the energy-saving and emission-reduction in manufacturing processed.Adding workshop for the said firm's machine, there are 6 lathes in this workshop, has 4 part production tasks in certain production batch, and part information is as shown in Figure 10.
The first step: adopting energy consumption predictive model to build power consumption of polymer processing information table, obtain process period, tooling cost information simultaneously, the machining information table obtained is as shown in table 3:
Table 3 machining information table
Part needs the production carrying out next process on lower a machine tool after having produced certain procedure, ignore pts wt to the impact of energy consumption, only considers the distance between lathe, transports consumption information table as shown in table 4 between acquisition lathe:
Consumption information table is transported between table 4 lathe
Lathe is standby certain energy consumption, and standby power information table is as shown in table 5:
Table 5 lathe standby power information table
2nd step: adopt the NSGA-II algorithm improved to carry out resources of production planning and configuration. Knowing by information table, the operation in certain road of part is power consumption of polymer processing value, tooling cost, process period on different lathe different, adopts multi-objective optimization algorithm, ensures that energy consumption, completion date, tooling cost are collaborative optimum. The parameter setting of algorithm is as shown in table 6:
Table 6 algorithm parameter information table
As shown in figure 11, by DEMATEL+ANP agriculture products weight, it is determined that the correspondingly acquisition that optimum individual generates is dispatched shown in Gantt chart 12, the result of Optimizing manufacture configuration is for algorithm convergence figure and optimum curved surface:
The job sequence of lathe 1 is: O11��O42��O34��O35;
The job sequence of lathe 2 is: O31��O43��O22��O23��O24;
The job sequence of lathe 3 is: O32��O33��O14��O25;
The job sequence of lathe 4 is: O41��O12��O44
The job sequence of lathe 5 is: O21��O13��O45��O15
Wherein OijRepresent the jth procedure of i-th workpiece.
Correspondingly target value is: completion date is 24min, and power consumption values is 54.3kw.h, and tooling cost is 105 yuan.
Above content is in conjunction with concrete production case further description made for the present invention; it is mainly the exactness proving present method in actual applications; can not assert that the specific embodiment of the present invention is only limitted to this; for general technical staff of the technical field of the invention; without departing from the inventive concept of the premise; some simple deduction or replace can also be made, all should be considered as belonging to the present invention and determine scope of patent protection by the claim book submitted to.
Claims (5)
1. the Discrete Manufacturing Process energy consumption optimization method of order-driven market, it is characterised in that, comprise the following steps:
1) according to the NC Code obtaining of certain procedure of part to be processed process the load time of this operation, dead time, number of changing knife, standby total time, start total time, load time the amount of feed, the speed of mainshaft and cutter model;
2) according to step 1) in obtain load time the amount of feed, the speed of mainshaft, cutter model, substitute into load power and no-load power computation model, obtain load power and no-load power;
3) according to step 1) load time that obtains, dead time, number of changing knife, standby total time, start total time, step 2) load power that obtains and no-load power, and the intrinsic starting power of the selected lathe of this procedure, standby power and tool changing energy consumption, substitute into total operation energy consumption calculation model, calculate this process energy consumption value;
4) according to step 1), step 2) and step 3) process energy consumption Forecasting Methodology, obtain the power consumption values of same operation processing on different lathe of multiple different part in a production batch, build consumption information storehouse;
5) according to step 4) the consumption information storehouse that obtains, the NSGA-II algorithm that design improves, carry out the determination of processing tasks order on the determination of every procedure machining tool and every platform lathe, for ensureing under the constraint of completion date, tooling cost so that power consumption of polymer processing is low.
2. the Discrete Manufacturing Process energy consumption optimization method of order-driven market according to claim 1, it is characterised in that, step 1) specific implementation step as follows:
1-1) adopt C language programming to build NC code parser, wherein, obtain number of changing knife N by T instructionc, the model simultaneously obtaining the cutter now used is to obtain milling width B; Obtained start time and the turn-off time of lathe by M instruction, thus obtain and calculate operation total time T; The speed of mainshaft n of lathe is obtained by S instruction; Amount of feed f is obtained by F instruction; Obtain coordinate position point by G instruction, calculate milling time T by being incorporated into gaugelWith empty milling time Tis, total time calculate standby time in conjunction with operation;
1-2) by the NC code of part to be processed input NC code parser, with automatically obtain the load time of certain procedure of processing, dead time, number of changing knife, standby total time, start total time, load time the amount of feed, the speed of mainshaft and cutter model.
3. the Discrete Manufacturing Process energy consumption optimization method of order-driven market according to claim 2, it is characterised in that, step 2) in load power PlComputation model is as follows:
In formula, KlFor load power coefficient, relevant to workpiece material, cutter, machine tool capability; F is amount of feed during load, and unit is r/min; apFor milling depth, unit mm; ��1����2����3������4It is power exponent;
No-load power PisComputation model is as follows:
In formula, KisFor no-load power coefficient, relevant to workpiece material, cutter, machine tool capability; ��1����2It is power exponent.
4. the Discrete Manufacturing Process energy consumption optimization method of order-driven market according to claim 2, it is characterised in that, step 3) in total process energy consumption E computation model as follows:
E=PsTs+PiTidt+PisTis+PlTl+NcEc(5)
In formula, Ps: device start power, Pi: device standby power, Pis: empty milling steel fiber, PlEquipment milling steel fiber, Ec: tool changing energy consumption, Ts: start total time, Ti: standby total time, Tis: empty milling total time, Tl: milling steel fiber, Nc: number of changing knife.
5. the Discrete Manufacturing Process energy consumption optimization method of order-driven market according to claim 3, it is characterised in that, step 5) specific implementation step as follows:
5-1) machining information input: machining information comprises processing tasks process information, carry out certain procedure of part processes the transport consumption information between alternative machining tool, process period of every procedure processing on different lathe, power consumption of polymer processing, the standby power of lathe, lathe;
Build the switching on and shutting down decision model of lathe, as follows:
ifTSP+TPS>Tin
Then: keep lathe unloaded;
elseifESP+EPS>CITin
Then keeps lathe unloaded
Else closes lathe
In model: TSPFor equipment is from the transformation time being closed to normal operation; TPSFor equipment is from the transformation time normally running to closedown; TinFor the machining gap waiting time of equipment; ESPFor equipment is from the conversion energy consumption being closed to normal operation; EPSFor equipment is from the conversion energy consumption normally running to closedown; CIFor the no-load power of equipment;
5-2) build the mathematical model of planning problem, wherein, optimization aim is power consumption of polymer processing, production cost and completion date, calculation formula is respectively such as formula, shown in (6), (7), (8), constraint condition is such as formula shown in (9)��(13):
Power consumption of polymer processing, comprises production energy consumption, machining gap energy consumption and transport energy consumption:
Production cost:
Completion date:
T=max (C1,C2...Cm)(8)
Constraint condition:
Ck=max (cijk) i=1,2 ..., n; J=1,2 ..., pi;K �� Mij(9)
cijk=sijk+tijkI=1,2 ..., n; J=1,2 ..., pi; K=1,2 ..., m (10)
sijk-ci(j-1)l��0(11)
Wherein, DijkThe jth procedure of expression task i selects the decision variable of machine k,The energy consumption of the processing of the jth procedure of expression workpiece i on machine k,Represent the transport energy consumption of jth procedure to next process of workpiece i, ekRepresent the energy input of machine k non-process period,Represent the tooling cost of the jth procedure of workpiece i on machine k, ckRepresent the completion date of lathe k, cijkThe completion date of the jth procedure of expression task i on machine k, piRepresent the operation sum of workpiece i, MijRepresent the optional lathe collection of operation j of workpiece i, sijkThe time opening of the jth procedure of expression task i on machine k, tijkThe process period of the jth procedure of expression task i on machine k, GijkThe jth procedure of expression task i selects the choice variable of machine k;
Formula (6), (7), (8), be respectively power dissipation obj ectives function, production cost function and completion date function;
Constraint condition (9): the completion date of guarantee lathe k is the time of last completion procedures on lathe i;
The completion date of the jth procedure of constraint condition (10): task i on lathe k is its time opening and activity time sum;
Constraint condition (11): the machining sequence constraint of task i, ensured that the time opening of operation was after a upper procedure end time;
Constraint condition (12): ensure that the jth procedure of task i has multiple optional lathe;
Constraint condition (13): ensure that the jth procedure of task i only selects an optional lathe to process;
5-3) the design of NSGA-II algorithm:
(1) the multi-objective optimization algorithm ED-NSGA-II improved is adopted to solve:
1) coding and decoding design: design is based on the two-dimensional encoded mode of operation and lathe;
2) individual superior and inferior evaluating: adopt and carry out individual trap queuing based on non-dominated ranking value and crowded angle value;
3) selection mode: algorithm of tournament selection method;
4) interleaved mode: binary POX intersects;
5) mutation operation: random variation;
6) population retention mechanism: based on the population retention mechanism of elitism strategy;
(2) computation process of algorithm is:
1) basic parameter of set algorithm: maximum iteration time is 150 times, population size is 500, and crossover probability is 0.8; Variation probability is 0.1;
2) initialize population, carries out individual non-dominated ranking and crowded angle value calculates;
3) interlace operation is selected: the total individuality of variation is that population size and crossover probability are long-pending: 400; Selecting binary competitive bidding match method to select two individualities, the individuality that wherein non-dominated ranking Rank value is minimum and crowding is the highest is preferentially chosen, and carries out binary POX intersection according to crossover probability;
4) mutation operation is selected: the total individuality of variation is that population size is long-pending with variation probability: 50; It is individual that same employing binary competitive bidding match method selects certain, and genes of individuals chain gene is according to variation probability random variation;
5) elitism strategy population retains: the new population produce intersection, variation and the initial population merging produced, and the non-dominated ranking and the crowding that carry out all individualities calculate, and retain front 500 excellent individual;
6) end condition detection: if it be all the Rank value of all individualities is 1 that the Rank value of current all individualities is front 19 iteration of 1, then termination of iterations; If not meeting, then check and whether reach iteration number of times 150: do not reach, then proceed to step 3), enter next iteration; Reach then termination of iterations;
7) iteration result is exported;
5-4) optimum solution is determined:
Solve due to ED-NSGA-II that to obtain result be optimal solution set, it is necessary to carry out the determination of optimum solution, therefore adopt the weight carrying out multiple target based on DEMATEL+ANP method to determine, carry out optimum solution and determine;
5-5) the generation of production planning:
By the optimum solution determined, obtain corresponding power consumption of polymer processing, decode the processing sequence of task on the machining tool determining every procedure and Ge Tai lathe simultaneously, and generate the result of corresponding Optimizing manufacture configuration.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106475908A (en) * | 2016-11-08 | 2017-03-08 | 上海大学 | Follow grinding process lathe operation energy consumption Forecasting Methodology based on standard G code |
| CN106959675A (en) * | 2017-03-21 | 2017-07-18 | 山东大学 | A kind of multi-objective scheduling optimization method towards Flow Shop |
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| CN108803495A (en) * | 2018-07-30 | 2018-11-13 | 山东理工大学 | Numerically controlled lathe energy consumption prediction technique when a kind of execution turnery processing program |
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| CN115755821A (en) * | 2022-12-30 | 2023-03-07 | 中建科技集团有限公司 | Prefabricated part production control method and system and related equipment |
| CN116165968A (en) * | 2023-04-24 | 2023-05-26 | 成都航利航空科技有限责任公司 | Numerical control procedure processing parameter recording method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120150324A1 (en) * | 2010-12-08 | 2012-06-14 | Matthew Brand | Method for Solving Control Problems |
| US20140278327A1 (en) * | 2013-03-14 | 2014-09-18 | Mark Hauenstein | Methods and Systems Architecture to Virtualize Energy Functions and Processes into a Cloud Based Model |
| CN104615077A (en) * | 2015-01-07 | 2015-05-13 | 重庆大学 | Efficient energy-saving optimizing method for numerical control milling processing process parameters based on Taguchi method |
| CN104751275A (en) * | 2015-03-11 | 2015-07-01 | 江南大学 | Dynamic configuration method for energy-consumption-oriented discrete manufacturing system resources |
| CN104880991A (en) * | 2015-03-18 | 2015-09-02 | 重庆大学 | Energy-efficiency-oriented multi-step numerical control milling process parameter multi-objective optimization method |
-
2015
- 2015-12-04 CN CN201510882936.6A patent/CN105652791B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120150324A1 (en) * | 2010-12-08 | 2012-06-14 | Matthew Brand | Method for Solving Control Problems |
| US20140278327A1 (en) * | 2013-03-14 | 2014-09-18 | Mark Hauenstein | Methods and Systems Architecture to Virtualize Energy Functions and Processes into a Cloud Based Model |
| CN104615077A (en) * | 2015-01-07 | 2015-05-13 | 重庆大学 | Efficient energy-saving optimizing method for numerical control milling processing process parameters based on Taguchi method |
| CN104751275A (en) * | 2015-03-11 | 2015-07-01 | 江南大学 | Dynamic configuration method for energy-consumption-oriented discrete manufacturing system resources |
| CN104880991A (en) * | 2015-03-18 | 2015-09-02 | 重庆大学 | Energy-efficiency-oriented multi-step numerical control milling process parameter multi-objective optimization method |
Non-Patent Citations (2)
| Title |
|---|
| 李聪波 等: "面向高效低碳的机械加工工艺路线多目标优化模型", 《机械工程学报》 * |
| 陈青艳 等: "SPEA2算法的加工精度与能耗多工序车削优化", 《机械设计与研究》 * |
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