CN107193258B - Numerical control processing technology route and cutting parameter integrated optimization method towards energy consumption - Google Patents
Numerical control processing technology route and cutting parameter integrated optimization method towards energy consumption Download PDFInfo
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- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/19—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
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Abstract
Numerical control processing technology route and cutting parameter influence numerical control power consumption of polymer processing significant.Compared with single optimization process route or single optimization cutting parameter, the present invention can further decrease numerical control processing energy consumption by carrying out process route and cutting parameter integrated optimization.It first proposed the process frame of numerical control processing technology route and cutting parameter integrated optimization towards energy consumption, next analyzes the energy consumption characteristics that process route and cutting parameter integrate, then minimum multiple target is loaded with total energy consumption and lathe and establishes numerical control processing technology route and cutting parameter multiple target Integrated Optimization Model, and propose a kind of optimization method based on multi-target simulation annealing algorithm.
Description
Technical field
The present invention relates to field of machining, and in particular to numerical control processing technology route and cutting parameter collection towards energy consumption
At optimization method.
Background technique
Digital-control processing system has a large capacity and a wide range, and total energy consumption is huge, and energy-saving potential is very big.Numerical control processing technology route and
Cutting parameter is significant to digital-control processing system energy consumption.Compared with single optimization process route or single optimization cutting parameter,
By carrying out process route and cutting parameter integrated optimization, numerical control processing energy consumption can be further decreased.How processing is comprehensively considered
Process energy consumption and conventional target (efficiency, lathe load, cost etc.), development numerical control processing technology route and cutting parameter integrate excellent
Change, is the key scientific problems that need currently solve.
Process planning is right according to business goal (such as production efficiency, cost, quality) under the premise of meeting processing quality
Specific a certain kind raw material or semi-finished product are transformed into processing method, process route and the required manufacturing recourses type of product
Etc. being planned and designed.For complex parts process planning problem, some scholars are based on part machining features recognition, feature is reflected
Penetrate with the technologies such as feature processed in batches, propose based on computer assisted Process Planning Method.Other scholars then surround zero
The problems such as processing method of part mechanical processing process is flexible, lathe is flexible, cutter is flexible, process sequence is flexible, to part machinery
Machining process route optimization problem has carried out research.Such as, Petrovic etc. considers lathe, cutter, direction of feed, processing sequence etc.
Flexibility establishes flexible numerical machining process route Optimized model using time and cost as multiple target；Wang etc. is selected with lathe
Select, cutter, direction of feed and processing sequence be decision variable, flexible numerical processing technology is established with the minimum target of totle drilling cost
Route optimization model；Wen etc. considers that lathe, cutter, direction of feed and processing sequence are flexible, establishes work by target of totle drilling cost
Skill route optimization model, and propose a kind of optimization method based on ant colony algorithm.It is existing for process sequence planning
Research is mainly focused on traditional optimization aim such as process time, cost, has ignored influence relationship of the process route to energy consumption.In reality
The part process planning stage on border can be effective by selecting reasonable processing method, manufacturing procedure, machining tool and cutter etc.
Reduce the energy consumption of NC Machining Process.
Early in generation nineteen ninety, Univ California-Berkeley professor Srinivasan is just to mechanical processing technique route
Green performance assessment carried out a series of researchs.Recently as gradually increasing for manufacturing industry environmental consciousness, add around mechanical
The research of work process route energy optimization problem gradually emerges in large numbers.Such as: Choi et al. is research pair with a certain automated manufacturing system
As a kind of energy consumption assessment model of Part's Process Route being established, to machining energy consumption, the auxiliary system of part manufacturing process
Energy consumption, material transportation energy consumption etc. are quantified.Zhang etc. considers the power consumption of processing cost and material excision process, proposes
A kind of Part's Process Route planing method process, including processing method selection, lathe selection and processing sequence such as determine at the links.
Seminar is in early-stage study, the carbon emissions such as power consumption and cutter consumption of detailed analysis part machinery machining process route
Characteristic establishes part machinery machining process route high-efficiency low-carbon Optimized model.Existing grinding for Part's Process Route planning
Study carefully, it is more around the expansion of the conventional targets such as time, cost, consider that the Research Literature quantity of the process sequence planning of power dissipation obj ectives is non-
It is often limited, while still needing further to be studied for the energy consumption assessment model of flexible numerical machining process route.
An important factor for cutting parameter is as part by numerical control power consumption of polymer processing is influenced, has some scholars and is taken off by experimental study
The mapping relations of cutting parameter and energy consumption are shown；On this basis, some scholars are quasi- by carrying out the machining experiments such as turning, milling
Conjunction has obtained the mapping relations model of cutting parameter and energy consumption；Other scholars establish the detailed association of cutting parameter and energy consumption
Model, and optimal cutting parameter is solved using optimization algorithm.Such as, Bilga etc. is disclosed by carrying out turnery processing experiment
Material removal rate with than can action rule, and further study the influence of feed speed, cutting depth to energy consumption.
The recurrence side that Camposeco-Negrete etc. is tested by carrying out milling and response phase method is used to establish milling parameter and energy consumption
Journey has obtained best parameter group by experimental analysis.
In conclusion the research of existing process sequence planning and cutting parameter optimization towards energy consumption, is single link
Independent optimization, have ignored the interaction relationship between two links.On the one hand, part by numerical control process energy consumption simultaneously by
The influence of process route and cutting parameter scheme；On the other hand, numerous flexible (the lathe flexibilities, cutter flexibility of process route
Deng), causing the cutting parameter of each process to combine has diversity.With single optimization process route or single optimization cutting parameter phase
Than numerical control processing energy consumption can be further decreased by carrying out process route and cutting parameter integrated optimization.But process route and
The influence relationship of the pairs of energy consumption of cutting parameter collection is complex, meanwhile, how to coordinate energy consumption and tradition when carrying out integrated optimization
The conflict relationship of target (such as time, cost, lathe load), is the key scientific problems of a urgent need to resolve.
Summary of the invention
The purpose of the present invention is carry out integrated optimization to process route and cutting parameter simultaneously to reduce numerical control processing energy
Consumption.
To realize the present invention purpose and the technical solution adopted is that such, i.e. the numerical control processing technology route towards energy consumption
With cutting parameter integrated optimization method.It the following steps are included:
Step 1: proposing the process frame of numerical control processing technology route and cutting parameter integrated optimization towards energy consumption；
Step 2: the energy consumption characteristics that analysis process route and cutting parameter integrate；
Step 3: with total energy consumption and the minimum multiple target of lathe load establishes numerical control processing technology route and cutting parameter is more
Target Integrated Optimization Model, and propose a kind of optimization method based on multi-target simulation annealing algorithm.
Preferably, in step 1, numerical control processing technology route and cutting parameter integrated optimization of the acquisition towards energy consumption
The process of process frame are as follows:
Part by numerical control machining process route indicates that components expect a series of numerical control processing works of finished product from casting former material
Skill process.Since the machining feature of part is complicated, each part usually has multiple machining feature units.The numerical control processing of part
The planning of process route is directed not only to the processing of multiple features, while each machining feature is also faced with multiple manufacturing procedures, Duo Zhongjia
The selection of wage source (lathe and cutter etc.), a variety of direction of feed, a variety of processing sequences, a variety of cutting parameters, this is resulted in
A variety of flexibilities of numerical control processing technology planning, as shown in Figure 1.
Mapping relations model between part machining feature and process route and cutting parameter, can be expressed as follows:
∑:F→PP
F={ f1,f2,,…,fi}
PP={ OP1,OP2,…,OPi}
OPi={ opi,1,opi,2,…,opi,j}
opi,j={ Mi,j,Ti,j,TADi,j,seqi,j,Pi,j}
In above formula, fiIndicate i-th of machining feature of part, OPiThe processing method for indicating i-th of machining feature；opi,jIt indicates
J-th of manufacturing procedure of i-th of processing method.N is the speed of mainshaft (r/min)；vcFor cutting speed (m/min)；F is every turn
Feed speed (mm/r)；fzFor feed engagement (mm/t)；apAnd aeRespectively back engagement of the cutting edge and working engagement of the cutting edge.
Flexible numerical machining process route and cutting parameter integrated optimization problem towards energy consumption can be described as: be based on part
Each machining feature unit determines corresponding manufacturing procedure (opi,j), for machining tool (M needed for the selection of each processi,j) and knife
Has (Ti,j), determine the direction of feed (TAD of each processi,j), the processing sequence (seq of each processi,j) and each process selection
Cutting parameter combines (Pi,j), so that selected process planning scheme loads in energy consumption and lathe and reaches association in the two targets
It adjusts optimal.The process frame of flexible numerical machining process route and cutting parameter integrated optimization towards energy consumption, as shown in Figure 2.
The assumed condition description of flexible numerical machining process route and cutting parameter integrated optimization problem towards energy consumption is such as
Under:
(1) certain process sequence constraint must need to be followed between all process steps of the same part, such as benchmark constraint, material
Material removal constraint, process structure constraint etc..Between the process of different parts, it is not required to defer to process sequence constraint.
(2) each processing method is made of one or several manufacturing procedures.Such as, drilling may have one of drilling technique
Composition, it is also possible to be made of drilling-fraising-bore hole three process.
(3) the same process may be cut by multiple tracks work step and be completed, and the cutter path and cutting parameter of each work step are identical
's.
(4) if the lathe of adjacent two-step is different, clamping workpiece again is needed；If two neighboring process direction of feed is not
Together, clamping workpiece again is needed；If the cutter of adjacent two-step is different, need to carry out tool changing operation.If blunt occurs for cutter, need
Again tool changing is processed.
Preferably, in step 2, the integrated energy consumption characteristics analytic process of the numerical control processing technology route and cutting parameter
Are as follows:
Process of each process on lathe is mainly made of six basic links: clamping of the part on lathe is fixed
Position, cutter clamping, cut-in without ball process, cutting process, the tool changing of cutter blunt and workpiece disassembly.Fig. 3 illustrates a certain process
The machine power curve of process.Based on above seven links, the NC Machining Process total energy consumption of part can calculate as follows:
Etotal=Esetup+Eair+Ecutting+Etoolchange
(1) the clamping process energy consumption E of each processsetup
Each process clamping process energy consumption calculation is as follows:
Wherein, tsetup(opi,j) indicate the temporal summation that clamping workpiece, cutter clamping, workpiece are dismantled, by process route side
Case influences, and specific calculate sees below formula.t1、t2、t3It is a fixed value, respectively indicates clamping workpiece time, cutter clamping
Time, workpiece take-down time.PstIndicate lathe standby power.
(2) the cut-in without ball energy consumption E of each processair
The cut-in without ball process energy consumption calculation of each process is as follows:
Wherein, PaucIndicate the power of machine power association class auxiliary system；PuFor lathe no-load power, mainly by main biography
Dynamic system no-load power and feeding no-load power composition, specifically calculate shown in following formula:
Pu=Pspindle+Pfeed
PspindleIt is in quadratic function relation with speed of mainshaft n for machine-tool spindle system no-load power, it is specific to calculate following public affairs
Shown in formula:
Pspindle=a0n+a1n2
PfeedFor feed system no-load power, with feed speed fvIt is related, it specifically calculates shown in following formula:
Pfeed=b0fv+b1(fv)2
tair(opi,j) indicate air cutting time, with cut-in without ball path (Lair) and fvIt is related, it specifically calculates shown in following formula:
(3) the machining energy consumption E of each processcutting
The machining energy consumption calculation of part each process is as follows:
PcFor material cutting power, meet: Pc=δ MRR, wherein δ is cutting ratio energy coefficient (J/mm3)；MRR is unit
Material removal rate (the mm of time3/ s), directly related with cutting parameter, specific calculate sees below formula:
In above formula, vcFor cutting speed (m/min)；F turns feed speed (mm/r) to be every；apAnd aeRespectively back engagement of the cutting edge
And working engagement of the cutting edge；fzFor feed engagement (mm/t)；Z is cutter tooth number；D is the diameter in hole to be processed.
PaFor additional load loss power, meet: Pa=c0·Pc, a0For additional load loss factor.tcuttingFor cutting
Process time, with cutting path (Lw) related with cutting parameter, specific calculate sees below formula:
(4) the blunt tool changing energy consumption E of each processtoolchange
As machining time of each process is continuously increased, tool wear is gradually aggravated, when tool wear is to certain
Degree needs tool changing again to carry out machining, thus generates blunt tool change time.The tool changing of cutter blunt is generally in the standby shape of lathe
It is carried out under state, therefore blunt tool changing energy consumption can be specific as follows:
Wherein, ttoolchangeIndicate the blunt tool change time of blunt, it is contemplated that be the cutting at one time time within the cutter life period
Share, specifically calculate shown in following formula:
TL(Ti,j,k) indicate cutter life, it can be calculated according to Taylor's formula, shown in formula specific as follows:
CT, m, u, v indicate cutter life coefficient；D indicates cutter diameter；d0Indicate that the diameter before machining, d ' are carried out in hole
Indicate that the diameter after machining is carried out in hole.
Preferably, described that numerical control processing technology route is established with total energy consumption and the minimum multiple target of lathe load in step 3
With the process of cutting parameter multiple target Integrated Optimization Model are as follows:
(1) decision variable
In the present invention, the decision variable of numerical control processing technology route and cutting parameter integrated optimization problem towards energy consumption,
It include: 1) to select machining tool (M for each processi,j)；2) process tool (T is selected for each processi,j)；3) for each process select into
Knife direction (TADi,j)；4) the processing sequence seq (op of each process is determinedi,j)；5) cutting parameter (P of each process is determinedi,j)。
(2) objective function
1) power dissipation obj ectives function
It is analyzed according to energy consumption characteristics, part by numerical control processing total energy consumption is made of four parts: clamping energy consumption, is cut at cut-in without ball energy consumption
Cut power consumption of polymer processing and blunt tool changing energy consumption.
2) lathe load target function
When carrying out numerical control processing technology route and parameter integrated optimization, it need to consider that lathe load is equal in numerical control (NC) Machining Workshop
Weigh situation.W (k) is enabled to indicate the processing load of kth platform lathe in workshop.The present invention uses two kinds of form calculus w (k):
1. process time of the w1 (k) by part on lathe, i.e. air cutting time and cutting time form, specific calculate is seen below
Formula:
②w2(k) it is made of clamping time, air cutting time, machining time, blunt tool change time, specific calculating is as follows
Shown in formula:
θiIndicate numerical control (NC) Machining Workshop lathe load balancing degrees, specific calculate sees below formula.θiIt is smaller, then it represents that numerical control adds
The processing load of each lathe in work workshop is more balanced；Conversely, θiIt is more big, indicate that the processing load of each lathe is more uneven, workshop
Resource bottleneck is more significant.
(3) constraint condition
Relevant constraint of the invention is described as follows:
1) it must comply with certain tight preceding relation constraint between each manufacturing procedure of part, such as locating clip tight constraint, benchmark
Constraint, material removal constraint etc..Define matrix PRE=[prei,j]M×MIndicate that the tight preceding constraint between each manufacturing procedure of part is closed
System.Wherein, M indicates the manufacturing procedure sum of part；prei,jFor a binary variable, if prei,j=1 indicates i-th to add
Work process need to carry out processing prior to j-th of process；If prei,jIt is not deposited between=0 expression i-th of process and jth process
The constraint relationship before tight.
2) the machining tool selection of each process and cutting tool choice, influence the range of choice of each cutting parameter.
①nmin≤n≤nmax, nmaxAnd nminIt is the highest and lowest revolving speed of lathe respectively
②fvmin≤fv≤fvmax, fvmaxAnd fvminIt is that lathe is most fast and minimum feed speed respectively
③Pc≤ξ·Pmax, ξ is lathe effective power coefficient, PmaxIt is lathe maximum power
④Fc≤Fcmax, FcmaxIt is the maximum cutting force of lathe
Based on above-mentioned analysis, numerical control processing technology route and cutting parameter Integrated Optimization Model towards energy consumption, tool are established
Body is as follows:
min f(Mijk,Tijk,seqijk,TADijk,Pijk)=(min Etotal,minθ)
Preferably, in step 3, the process for proposing a kind of optimization method based on multi-target simulation annealing algorithm
Are as follows:
Simulated annealing (Simulated Annealing, SA) is a kind of based on Monte-Carlo iterative solution plan
Random optimizing algorithm slightly, because its unique Optimization Mechanism and versatility, flexibility have obtained answering extensively in Combinatorial Optimization field
With.Traditional SA algorithm is solved just for single optimization aim.Some scholars are for multi-objective optimization question in recent years
Solve characteristic, have devised multi-target simulation annealing algorithm (Multi-objective Simulated Annealing,
MOSA)。
MOSA introduces " dominating (dominate) " and the concept of " Noninferior Solution Set (Archive) ".It is more in a minimum
In objective optimisation problems, if known two solutions RsAnd Rq, and there is f to each (k ∈ 1,2 .., K) objective functionk(Rq)
≥fk(Rs), then claim to solve RsBranch is assigned in solution Rq, or solution RqBy solution RsIt dominates.Archive be used to store algorithm generation each is non-
Inferior solution.The memory span of HL expression Noninferior Solution Set.
In the iterative process each time of MOSA, based on current solution RqGenerate an adjacent solution Rs.If adjacent solution RsBranch, which is assigned in, to be worked as
Preceding solution Rq, then R is usedsReplace Rq, while updating Archive；If RsIt does not prop up assigned in Rq, then received with certain Probability p rob adjacent
Solve RsAnd replace Rq.The calculation of acceptance probability prob is as follows:
Wherein, T indicates temperature, and constantly reduces with the number of iterations；E(Rq, T) and E (Rs, T) and respectively indicate solution RsAnd Rq
Energy value under temperature T-shaped state.As each iteration terminates, just cooled rate α lowers temperature T, and then receiving one is adjacent
Solve RsProbability also decrease.When algorithm meets termination condition, MOSA algorithm just stops operation and exports optimal result.
The algorithm flow of MOSA is as shown in Figure 4.
According to the present invention the characteristics of multiple target integrated optimization problem, the committed step in algorithm is improved, specifically such as
Under:
(1) form of expression solved in MOSA
In view of five decision variables of process route and cutting parameter integrated optimization problem, therefore use a matrix A
=[at,w]8×WProcess route and cutting parameter solution are indicated, specifically as shown in Fig. 5.Matrix A the first row indicates process number
(opi,j)；Columns where each process number indicates the processing sequence of the process.Matrix A second and third, four rows respectively indicate each work
Lathe selected by sequence, cutter and direction of feed.If a certain process is drilling processing, the five to six row of matrix A is respectively main shaft
Revolving speed and feed speed；If a certain process is turnery processing, the five to seven row of matrix A is respectively the speed of mainshaft, feeding speed
Degree, back engagement of the cutting edge；If a certain process is Milling Process, the five to eight row of matrix A is respectively the speed of mainshaft, feed speed, back
Bite and working engagement of the cutting edge.
(2) the feasible processing sequence for meeting tight preceding relation constraint generates
In the algorithm iteration process each time of MOSA, for the processing sequence solution that each is generated at random, it need to check that it is
It is no to meet tight preceding relation constraint matrix PRE.If meeting constraint, then the processing sequence solution is exported；If not satisfied, then repeating process
It is sequentially generated process, until generating the processing sequence solution for meeting tight preceding relation constraint.During algorithm iteration each time,
The probability for meeting the processing sequence solution of all tight preceding relation constraints is generated, is calculated as formula is as follows.
In above formula, N indicates the tight preceding relationship sum between each process；ρ (i) indicates that the processing sequence solution that generates at random meets the
The probability of i tight preceding relation constraints.As N increases (for example, working as N > 15), generates one and meet all tight preceding relation constraints
The probability of processing sequence decreases, thus the number of iterations and runing time of MOSA increases.Therefore, feasible to quickly generate
Processing sequence solution, the invention proposes it is a kind of meet it is tight before relation constraint processing sequence solution generation method, it is specific as follows:
1) preceding relation constraint matrix PRE tight for given one, is each process (opi,j) determine one it is tight before about
Beam rankThe process belonged under the same rank is placed into the corresponding other process set (b of confinement levelk) in.
Set b0In all process steps do not influenced by relation constraint before tight；SetIn all process steps must comply with
Process tightly before relation constraint, and it is tight before relation constraint rank it is smaller, the processing of the process in process set corresponding to the rank
Priority is higher.For example, process set b5In process must be prior to b6In process processing.Each process collection generated
It closesIt is stored in matrix B={ b0, b1,…,bk,….,bKIn.Square is generated based on matrix generator matrix PRE
The program in machine code of battle array B, as shown in Figure 6.
2) according to tight preceding constraint rankSequence from small to large, successively by process set bkIt is put into
In queue.
MOSA algorithm is based on matrix B and queue, raw feasible processing sequence initial solution in iterative process each time
Or adjacent solution, it is specifically described as follows.
(3) MOSA initial solution generates
In the present invention, the generation step of MOSA initial solution is as follows:
Step 1: a processing sequence initial solution is generated at random:
A) for each of matrix B process setTo the process number of its insideCarry out random alignment combination.For example, to b2After={ 2,4,16 } carry out random alignment, obtain: b2=4,16,
2}。
B) queue={ b is updated1,…,bk,….,bK}。
If c) process set b0It is not sky, then its K internal process number is subjected to random alignment combination, then will
Process after permutation and combination is put into queue using plug hole method, while updating queue.
D) process number in queue is put in order according to it, is sequentially placed into the first row of matrix A.
Step 2: in the second row of matrix A, a lathe is randomly choosed in optional lathe set for each process.
Step 3: in the third line of matrix A, a cutter is randomly choosed in optional cutter set for each process.
Step 4: in the fourth line of matrix A, one is randomly choosed in optional direction of feed set for each process
TAD。
Step 5: in the five to eight row of matrix A, being randomly choosed in technological parameter restriction range for each process
One parameter value.
In initial solution generation phase, constantly repeat the above process to generateA initial solution updates simultaneously
Archive.A solution is randomly choosed in the initial solution of generation as current solution.
(4) the adjacent solution of MOSA generates
Based on each current solution, adjacent solution is generated using a kind of adjacent solution generting machanism.The present invention uses five kinds of modes
Generate adjacent solution:
Mode 1: the adjacent solution of processing sequence is generated using two ways.
A) process set is randomly choosed in matrix BRandomly choose bkIn any two work
Sequence simultaneously changes it and puts in order.Update the first row of queue and matrix A.
If b) process set b0It is not sky, randomly chooses b0In any one process, and rearrange the process and exist
Position in queue.Update the first row of queue and matrix A.
Mode 2: it in the second row of matrix A, randomly chooses in an element and optional lathe set corresponding to it
A lathe numbering is redistributed for it, selects adjacent solution to generate a lathe.
Mode 3: it in the third line of matrix A, randomly chooses in an element and optional cutter set corresponding to it
A cutter number is redistributed for it, to generate the adjacent solution of a cutting tool choice.
Mode 4: in the fourth line of matrix A, randomly choose an element and corresponding to it optional TAD set in be
It redistributes a TAD, to generate the adjacent solution of a TAD.
Mode 5: a cutting parameter, the ratio generated at random with one are arbitrarily selected in the five to eight row of matrix A
Increase or reduce the parameter, to generate the adjacent solution of a cutting parameter.
In view of every kind of adjacent solution generating mode is to the influence difference of energy consumption and lathe load target, therefore, in order to improve
The convergence rate of MOSA is carried out adjacent solution present invention introduces a kind of study mechanism and is generated.That is, each adjacent solution generating mode
By select probabilityIt is unfixed, as MOSA iterative process constantly learns variation.According to i-th kind of adjacent solution
After generating mode generates an adjacent solution, which dominates current solution, and (energy consumption and lathe load target of adjacent solution are superior to
Current solution), then update the select probability of the adjacent solution generating mode；Conversely, the adjacent solution is remained unchanged by select probability, tool
Body calculating sees below formula.With the continuous iteration of MOSA algorithm, performance preferably it is adjacent solution generating mode by select probability gradually
Increase, the poor adjacent solution generating mode of performance is constantly reduced by select probability, thus improves seeking in solution space for MOSA
Excellent speed.
In above formula, xiIndicate the performance value of i-th kind of adjacent solution generating mode；ziAnd yiRespectively indicate i-th kind of adjacent solution
Performance value of the generating mode on two objective functions of total energy consumption and lathe load balancing；x0=0.1 is every kind of adjacent solution
The performance initial value of generating mode.
Detailed description of the invention
A variety of flexibilities of Fig. 1 numerical control processing technology route
Process route of the Fig. 2 towards energy consumption and cutting parameter integration and optimization framework
The machine power curve of a certain process process of Fig. 3
The code flow of Fig. 4 MOSA algorithm
The form of the solution of Fig. 5 process route and cutting parameter integrated optimization
Program in machine code of the Fig. 6 based on matrix PRE generator matrix B
Fig. 7 part 1: electric baseboard
Fig. 8 part 2: pedestal
Fig. 9 total energy consumption and lathe load balancingThe changing rule increased with a
The changing rule that all kinds of energy consumptions of Figure 10 numerical control processing increase with a
Figure 11 total energy consumption and lathe load balancingThe changing rule increased with a
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples, but should not be construed the above-mentioned theme of the present invention
Range is only limitted to following embodiments.Without departing from the idea case in the present invention described above, known according to ordinary skill
Knowledge and customary means, make various replacements and change, should all include within the scope of the present invention.
Present case is that object carries out application verification with two parts (Fig. 7 and Fig. 8).Part based on Fig. 7-8 is processed special
Sign, analysis have obtained feasible manufacturing procedure, lathe, cutter, direction of feed information, as shown in table 1.The tight preceding relationship of each process
Restraint condition, specifically as shown in table 2.
Numerically-controlled machine tool realtime power is measured using lathe energy efficiency monitoring system, by installing HC33C3 type in Machine Tool Electric Appliance cabinet
The acquisition of power sensing type is the total voltage and total current of lathe, and using digital filtering and lathe realtime power letter is calculated
Number, related power coefficient is obtained by nonlinear regression and fitting after power information is handled.The power information of each machining tool is such as
Shown in table 3.Shown in process tool information and table 4.
In order to verify the necessity of process route and cutting parameter integrated optimization, with total energy consumption minimum and lathe load balancing
For the necessity of multiple target integrated optimization, devises 5 cases and carry out Optimization Solution, as shown in table 5.Wherein, fixed cutting ginseng
When number, the cutting parameter of each process takes the intermediate value in optional range；When technique for fixing route, the lathe of each process, cutter, feed
Direction, processing sequence generate at random.
The information such as manufacturing procedure, lathe, cutter, the direction of feed of 1 part of table
Tight preceding relation constraint between each manufacturing procedure of 2 part of table
3 machining tool information of table
4 process tool information of table
5 case comparative analysis of table
(1) necessity of process route and cutting parameter integrated optimization
1) as shown in Table 5, when optimization aim minimum with total energy consumption, integrated optimization is carried out to process route and cutting parameter
(case 3.1), compared with single optimization process route (case 1.1), the former energy consumption reduces 31%；Process route with cut
Parameter integrated optimization (case 3.1) is cut, compared with single optimization cutting parameter (case 2.1), the former energy consumption reduces
16%.
2) when as shown in Table 5, using lathe load balancing as optimization aim, process route and cutting parameter development are integrated excellent
Change (case 3.2), compared with single optimization process route (case 1.2), the former lathe load reduction 29%；Technique road
Line and cutting parameter integrated optimization, compared with single optimization cutting parameter (case 2.2), the former lathe load reduction
20%.
In conclusion compared with single optimization process route or single optimization cutting parameter, by carry out process route with
Cutting parameter integrated optimization, can further decrease digital-control processing system total energy consumption while energy efficient balance numerical control (NC) Machining Workshop is each
The processing of lathe loads, to realize that the overall performance of digital-control processing system is optimal.
(2) using total energy consumption minimum and lathe load balancing as the necessity of multiple target integrated optimization
1) as shown in Table 5: when carrying out process route and cutting parameter integrated optimization, with the minimum optimization aim of total energy consumption
(case 3.1), with using lathe load balancing, compared with optimization aim (case 3.2), it is negative that the former total energy consumption reduces by 53%, lathe
It carries and increases by 47%.It can be seen that total energy consumption and lathe are negative when carrying out numerical control processing technology route and cutting parameter integrated optimization
It carries and conflicts with each other relationship in the presence of certain.
2) relationship is conflicted with each other between total energy consumption and lathe load in order to further verify, most with total energy consumption (Etotal)
Small is optimization aim, while considering that lathe load balancing constrains (formula 29), carries out numerical control processing technology route and cutting parameter
Integrated optimization (case 4).
θminα (1-10%)≤θ1≤θmin·α
In above formula, α ∈ (1, x) indicates that the width of lathe load balancing constraint puts coefficient, wherein x=θmax/θmin；θminWith
θmaxRespectively indicate minimum lathe load balancing degrees, the maximum lathe load balancing degrees of numerical control (NC) Machining Workshop.With lathe load balancing
When carrying out integrated optimization for single goal, solve to obtain θ by MOSA algorithmmin=0.3067, θmax=0.9823；Thus it calculates
It arrives: x=3.202.
By SA algorithm solve to obtain total energy consumption, clamping energy consumption, cut-in without ball energy consumption, machining energy consumption, blunt tool changing energy consumption,
Lathe loadWithThe changing rule being gradually increased with α, as shown in figs. 9-11.Each α ∈ (1, x) institute in Fig. 9-11
Corresponding data point is the minimum total energy consumption value for taking 5 SA algorithm simulatings to obtain and corresponding clamping energy consumption, cut-in without ball energy consumption, cuts
Cut power consumption of polymer processing, blunt tool changing energy consumption, lathe loadWith
As shown in Figure 9: as α is gradually increased to x from 1, numerical control processing total energy consumption is in increased trend after first reducing；Together
When, the lathe load of numerical control (NC) Machining WorkshopIn increasing trend.This is because:
1. influence of the lathe load restraint to Integrated Optimization Model is the most significant as α=1.At this point, SA algorithm must be
The smallest solution of total energy consumption is found under the premise of meeting lathe load restraint, main to be realized by two kinds of approach:
Approach one: choosing biggish cutting parameter, to reduce the machining time of each process, thus reduces each lathe
Processing loads and realizes lathe load balancing in workshop.On the one hand, biggish cutting parameter is selected, the cutting of each process is reduced
Process time causes machining energy consumption also to reduce (Figure 10 c) therewith)；On the other hand, biggish cutting parameter is selected, is caused
Tool wear aggravation, thereby increases blunt tool change time and blunt tool changing energy consumption (Figure 10 d)).Due to blunt tool changing energy consumption
Increase is more more significant than the reduction of machining energy consumption, consequently leads to the increase (Fig. 9) of total energy consumption.
Approach two: in the process route optimization stage, lathe selected by each process is allowed to be in dispersity, to realize workshop
Lathe load balancing.Simultaneously as the dispersion of each process lathe selection, thus lathe replacement number and clamping energy consumption increase (figure
10a))。
2. influence of the lathe load restraint to Integrated Optimization Model gradually weakens as α constantly increases to x from 1.At this point,
MOSA constantly reduces numerical control processing total energy consumption using two kinds of approach:
Approach one: constantly selecting lesser cutting parameter, to realize the coordination of blunt tool changing energy consumption and machining energy consumption
It is optimal.Meanwhile the increase of machining time, cause lathe load in increasing trend.
Approach two: in the process route optimization stage, lathe selected by each process is allowed gradually to be changed from dispersity to concentration.One
Aspect, with the concentration that each process lathe selects, thus the clamping time of each process and clamping energy consumption reduce (Figure 10 a))；It is another
Aspect, the concentration of each process lathe selection, exacerbates the lathe load imbalance of numerical control (NC) Machining Workshop, thus lathe is caused to loadIn increasing trend (Fig. 9).
In conclusion can be illustrated by Fig. 9-10: total energy consumption and lathe loadBetween there is apparent mutually punching
Tip out system.Therefore, need to carry out towards energy consumption and lathe load numerical control processing technology route and cutting parameter multiple target integrate it is excellent
Change.
(3) as shown in Figure 9: region 1 and region 2 are process route and cutting parameter multiple target integrated optimization (case 5)
Pareto disaggregation generating region；Region 3 is the inferior solution generating region of multiple target integrated optimization problem.Table 5 illustrates MOSA algorithm
Obtained one group of multiple target integrated optimization Pareto solution.The parameter setting of MOSA algorithm is as follows: Tmax=200, Tmin=10^ (-
5), HL=30, iter=75, a=0.9.Table 6 illustrates the optimum process route and cutting parameter of Pareto solution.
As shown in Table 5: carry out integrated optimization (case 5) by multiple target of total energy consumption minimum and lathe load balancing, with
The minimum single goal integrated optimization (case 3.1) of total energy consumption is compared, the former lathe load reduction 22%, total energy consumption increase
40%；With using lathe load balancing, compared with single goal integrated optimization (case 3.2), total energy consumption reduces the load of 35%, lathe and increases
Add 17%.It is indicated above: by carrying out numerical control processing technology route and cutting parameter integrated optimization, can be realized total energy consumption most
Small and two targets of lathe load balancing coordinations are optimal.
Total energy consumption (Etotal) and lathe loadBetween conflict relationship, as shown in figure 11.As shown in Figure 11: lathe is negative
It carriesWith the increase of α show first reduce after increased trend, it is more consistent with the changing rule of total energy consumption Etotal.This is
Due to: lathe loadIt is influenced simultaneously by clamping time, air cutting time, machining time and blunt tool change time；Due to total
Energy consumption (Etotal) is made of clamping energy consumption, cut-in without ball energy consumption, machining energy consumption and four part of blunt tool changing energy consumption, therefore total energy
Consumption is also influenced by clamping time, air cutting time, machining time and blunt tool change time simultaneously.Although lathe loads
Similar to the changing rule of total energy consumption, still, α value corresponding to the minimum point value of the two is different.It can be said that bright: lathe is negative
It carriesThere is also certain to conflict with each other relationship with total energy consumption.
6 optimum process route of table and cutting parameter scheme
Claims (1)
1. numerical control processing technology route and cutting parameter integrated optimization method towards energy consumption, it is characterised in that including following step
It is rapid:
Step 1: proposing the process frame of numerical control processing technology route and cutting parameter integrated optimization towards energy consumption；
Step 2: the energy consumption characteristics that analysis process route and cutting parameter integrate；
Step 3: minimum multiple target being loaded with total energy consumption and lathe and establishes numerical control processing technology route and cutting parameter multiple target
Integrated Optimization Model, and propose a kind of optimization method based on multi-target simulation annealing algorithm；
The process of the process frame of numerical control processing technology route and cutting parameter integrated optimization towards energy consumption is obtained in step 1
Are as follows:
Flexible numerical machining process route and cutting parameter integrated optimization problem towards energy consumption can be described as: respectively be added based on part
Work feature unit determines corresponding manufacturing procedure (opi, j), for machining tool (M needed for the selection of each processi,j) and cutter
(Ti,j), determine the direction of feed (TAD of each processi,j), the processing sequence (seq of each processi,j) and the selection of each process cut
Cut parameter combination (Pi,j), so that selected process planning scheme loads in energy consumption and lathe and reaches coordination in the two targets
It is optimal；
Assumed condition is described as follows:
(1) must need to follow certain process sequence constraint between all process steps of the same part, the processes of different parts it
Between, it is not required to defer to process sequence constraint；
(2) each processing method is made of one or several manufacturing procedures；
(3) the same process may be cut by multiple tracks work step and be completed, and the cutter path and cutting parameter of each work step are identical；
(4) if the lathe of adjacent two-step is different, clamping workpiece again is needed；If two neighboring process direction of feed is different, need
Again clamping workpiece；If the cutter of adjacent two-step is different, need to carry out tool changing operation；If blunt occurs for cutter, need again
Tool changing is processed；
The process for the energy consumption characteristics that analyzing numerically controlled machining process route and cutting parameter integrate in step 2 are as follows:
The NC Machining Process total energy consumption of part can calculate as follows:
Etotal=Esetup+Eair+Ecutting+Etoolchange
Wherein, EsetupFor the clamping process energy consumption of each process, EairFor the cut-in without ball energy consumption of each process, EcuttingFor cutting for each process
Cut power consumption of polymer processing, EtoolchangeFor the blunt tool changing energy consumption of each process；
Minimum multiple target is loaded with total energy consumption and lathe in step 3 and establishes numerical control processing technology route and the more mesh of cutting parameter
Mark the process of Integrated Optimization Model are as follows:
(1) decision variable
It include: 1) to select machining tool (M for each processi,j)；2) process tool (T is selected for each processi,j)；3) it is selected for each process
Select direction of feed (TADi,j)；4) the processing sequence seq (op of each process is determinedi,j)；5) cutting parameter of each process is determined
(Pi,j)；
(2) objective function
1) power dissipation obj ectives function
It is analyzed according to energy consumption characteristics, part by numerical control processing total energy consumption is made of four parts: clamping energy consumption, cut-in without ball energy consumption, cutting add
Work energy consumption and blunt tool changing energy consumption；
2) lathe load target function
When carrying out numerical control processing technology route and parameter integrated optimization, lathe load balancing feelings in numerical control (NC) Machining Workshop need to be considered
Condition；W (k) is enabled to indicate the processing load of kth platform lathe in workshop；The calculating of w (k) uses two kinds of forms:
①w1(k) process time by part on lathe, i.e. air cutting time and cutting time composition, specific calculate see below formula:
②w2(k) it is made of clamping time, air cutting time, machining time, blunt tool change time, specifically calculates following formula
It is shown:
θiIndicate numerical control (NC) Machining Workshop lathe load balancing degrees, specific calculate sees below formula；
(3) constraint condition
1) it must comply with certain tight preceding relation constraint between each manufacturing procedure of part；PRE=[prei,j]M×MIndicate that part is each
Tight preceding the constraint relationship between manufacturing procedure；Wherein, M indicates the manufacturing procedure sum of part；prei,jBecome for a binary system
Amount, if prei,j=1 indicates that i-th of manufacturing procedure need to carry out processing prior to j-th of process；If prei,j=0 indicates i-th
There is no tight preceding the constraint relationships between a process and j-th of process；
2) the machining tool selection of each process and cutting tool choice, influence the range of choice of each cutting parameter；
①nmin≤n≤nmax, nmaxAnd nminIt is the highest and lowest revolving speed of lathe respectively
②fvmin≤fv≤fvmax, fvmaxAnd fvminIt is that lathe is most fast and minimum feed speed respectively
③Pc≤ξ·Pmax, ξ is lathe effective power coefficient, PmaxIt is lathe maximum power
④Fc≤Fcmax,FcmaxIt is the maximum cutting force of lathe
Based on above-mentioned analysis, numerical control processing technology route and cutting parameter Integrated Optimization Model towards energy consumption are established, specifically such as
Under:
min f(Mijk,Tijk,seqijk,TADijk,Pijk)=(min Etotal,min θ)
A kind of process of optimization method based on multi-target simulation annealing algorithm is proposed in step 3 are as follows:
Simulated annealing (Simulated Annealing, SA) be it is a kind of based on Monte-Carlo iterative solution strategy with
Machine optimizing algorithm the characteristics of according to multiple target integrated optimization problem, improves the committed step in algorithm, specific as follows:
(1) form of expression solved in MOSA
In view of five decision variables of process route and cutting parameter integrated optimization problem, therefore use a matrix A=[at,
w]8×WIndicate process route and cutting parameter solution；Matrix A the first row indicates process number (opi,j)；Columns where each process number
Indicate the processing sequence of the process；Matrix A second and third, four rows respectively indicate lathe selected by each process, cutter and feed side
To；If a certain process is drilling processing, the five to six row of matrix A is respectively the speed of mainshaft and feed speed；If a certain process
For turnery processing, then the five to seven row of matrix A is respectively the speed of mainshaft, feed speed, back engagement of the cutting edge；If a certain process is milling
Processing, then the five to eight row of matrix A is respectively the speed of mainshaft, feed speed, back engagement of the cutting edge and working engagement of the cutting edge；
(2) the feasible processing sequence for meeting tight preceding relation constraint generates
The processing sequence solution generation method for meeting tight preceding relation constraint is specific as follows:
1) preceding relation constraint matrix PRE tight for given one, is each process (opi,j) determine a tight preceding confinement level
NotThe process belonged under the same rank is placed into the corresponding other process set (b of confinement levelk) in；Set b0
In all process steps do not influenced by relation constraint before tight；SetIn all process steps to must comply with process tight
Preceding relation constraint, and tight preceding relation constraint rank is smaller, the processing priority of the process in process set corresponding to the rank
It is higher；
2) according to tight preceding constraint rankSequence from small to large, successively by process set bkIt is put into queue；
(3) MOSA initial solution generates
Relation constraint matrix PRE before tight for given one, is each process (opi,j) determine a tight preceding constraint rankThe process belonged under the same rank is placed into the corresponding other process set (b of confinement levelk) in；
According to tight preceding constraint rankSequence from small to large, successively by process set bkIt is put into queue；
MOSA algorithm is based on matrix B and queue, raw feasible processing sequence initial solution or adjacent in iterative process each time
Solution；
Step is 1.: a processing sequence initial solution is generated at random:
A) for each of matrix B process setTo the process number of its inside
Carry out random alignment combination；
B) queue={ b is updated1,…,bk,….,bK}；
If c) process set b0It is not sky, then its K internal process number is subjected to random alignment combination, it then will arrangement
Process after combination is put into queue using plug hole method, while updating queue；
D) process number in queue is put in order according to it, is sequentially placed into the first row of matrix A；
Step is 2.: in the second row of matrix A, a lathe is randomly choosed in optional lathe set for each process；
Step is 3.: in the third line of matrix A, a cutter is randomly choosed in optional cutter set for each process；
Step is 4.: in the fourth line of matrix A, a TAD is randomly choosed in optional direction of feed set for each process；
Step is 5.: in the five to eight row of matrix A, one is randomly choosed in technological parameter restriction range for each process
Parameter value；
In initial solution generation phase, constantly repeat the above process to generateA initial solution updates simultaneously
Archive；A solution is randomly choosed in the initial solution of generation as current solution；
(4) the adjacent solution of MOSA generates
Based on each current solution, adjacent solution is generated using a kind of adjacent solution generting machanism；The generating mode of adjacent solution is five kinds:
Mode 1: the adjacent solution of processing sequence is generated using two ways；
A) process set is randomly choosed in matrix BRandomly choose bkIn any two processes and change
Change puts in order；Update the first row of queue and matrix A；
If b) process set b0It is not sky, randomly chooses b0In any one process, and rearrange the process in queue
Position；Update the first row of queue and matrix A；
Mode 2: it in the second row of matrix A, randomly chooses in an element and optional lathe set corresponding to it as it
A lathe numbering is redistributed, selects adjacent solution to generate a lathe；
Mode 3: it in the third line of matrix A, randomly chooses in an element and optional cutter set corresponding to it as it
A cutter number is redistributed, to generate the adjacent solution of a cutting tool choice；
Mode 4: it in the fourth line of matrix A, randomly chooses in an element and optional TAD set corresponding to it as its is heavy
Newly one TAD of distribution, to generate the adjacent solution of a TAD；
Mode 5: optionally selecting a cutting parameter in the five to eight row of matrix A, is increased or is subtracted with a ratio generated at random
Few parameter, to generate the adjacent solution of a cutting parameter；
In order to improve the convergence rate of MOSA, introduces a kind of study mechanism and carry out adjacent solution generation；That is, each adjacent solution generates
Mode by select probabilityIt is unfixed, as MOSA iterative process constantly learns variation；According to i-th
After kind adjacent solution generating mode generates an adjacent solution, which, which dominates, currently solves, then updates the adjacent generating mode that solves
Select probability；Conversely, the adjacent solution is remained unchanged by select probability, specific calculate sees below formula；It is continuous with MOSA algorithm
Iteration, performance preferably adjacent solution generating mode are gradually increased by select probability, the poor adjacent solution generating mode of performance
It is constantly reduced by select probability, thus improves the speed of searching optimization in solution space of MOSA；
In above formula, xiIndicate the performance value of i-th kind of adjacent solution generating mode；ziAnd yiI-th kind of adjacent solution is respectively indicated to generate
Performance value of the mode on two objective functions of total energy consumption and lathe load balancing；x0=0.1 generates for every kind of adjacent solution
The performance initial value of mode.
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