CN107193258A - Towards the numerical control processing technology route and cutting parameter Optimized model and method of energy consumption - Google Patents
Towards the numerical control processing technology route and cutting parameter Optimized model and method of energy consumption Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- 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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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
Numerical control processing technology route and cutting parameter influence notable to numerical control power consumption of polymer processing.Compared with single optimization process route or single optimization cutting parameter, the present invention can further reduce digital control processing energy consumption by carrying out process route and cutting parameter integrated optimization.It first proposed the flow framework of the numerical control processing technology route and cutting parameter integrated optimization towards energy consumption, next analyzes process route and the integrated energy consumption characteristics of cutting parameter, 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 towards the numerical control processing technology route and cutting parameter of energy consumption
Optimized model and method.
Background technology
Digital-control processing system has a large capacity and a wide range, and its total energy consumption is huge, and energy-saving potential is very big.Numerical control processing technology route
It is notable to digital-control processing system energy consumption with cutting parameter.With single optimization process route or single optimization cutting parameter phase
Than by carrying out process route and cutting parameter integrated optimization, can further reduce digital control processing energy consumption.How to consider
Process energy consumption and conventional target (efficiency, lathe load, cost etc.), carry out numerical control processing technology route and cutting parameter
Integrated optimization, 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 crudy is met
Specific a certain kind raw material or semi-finished product are transformed into processing method, process route and the required manufacturing recourses species of product
Deng being planned and designed.For complex parts process planning problem, some scholars are based on part machining features recognition, feature
The technology such as mapping and feature processed in batches, it is proposed that based on computer assisted Process Planning Method.Other scholars then enclose
Around the problems such as the processing method of part machinery process is flexible, lathe is flexible, cutter is flexible, process sequence is flexible, to part
Mechanical processing technique route optimization problem has carried out research.Such as, Petrovic etc. considers lathe, cutter, direction of feed, processing
The flexibilities such as order, using time and cost as multiple target, establish flexible numerical machining process route Optimized model；Wang etc. with
Lathe selection, cutter, direction of feed and processing sequence are decision variable, and flexible numerical is established with the minimum target of totle drilling cost
Machining process route Optimized model；The existing research for process sequence planning is mainly focused on the tradition such as process time, cost
Optimization aim, have ignored influence relation of the process route to energy consumption.In the actual part process planning stage, by selecting to close
Processing method, manufacturing procedure, machining tool and cutter of reason etc., can effectively reduce the energy consumption of NC Machining Process.
Recently as the gradually enhancing of manufacturing industry environmental consciousness, around mechanical processing technique route energy optimization problem
Research gradually emerge in large numbers.Such as：Choi et al. establishes a kind of part process using a certain automated manufacturing system as research object
The energy consumption assessment model of route, to machining energy consumption, accessory system energy consumption, material transportation energy consumption of part manufacturing process etc.
Quantified.Zhang etc. considers that processing cost and material cut off the power consumption of process, it is proposed that a kind of Part's Process Route
Planing method flow, including processing method selection, lathe selection and processing sequence determine etc. link.Seminar is in early-stage Study
In, the carbon emission characteristic such as power consumption and cutter consumption of labor part machinery machining process route establishes part
Mechanical processing technique route high-efficiency low-carbon Optimized model.It is existing for Part's Process Route plan research, it is more around when
Between, the expansion of the conventional target such as cost, it is considered to the Research Literature quantity of the process sequence planning of power dissipation obj ectives is very limited, simultaneously
Still need further research for the energy consumption assessment model of flexible numerical machining process route.
Cutting parameter has some scholars and passes through experimental study as the key factor of influence part by numerical control power consumption of polymer processing
Disclose the mapping relations of cutting parameter and energy consumption；On this basis, some scholars are real by carrying out the processing such as turning, milling
Test fitting and obtain cutting parameter and the mapping relations model of energy consumption；Other scholars establish the detailed of cutting parameter and energy consumption
Thin correlation model, and optimal cutting parameter is solved using optimized algorithm.Such as, Bilga etc. is by carrying out turnery processing
Experiment, discloses material clearance and the action rule than energy, and further study feed speed, cutting depth to energy consumption
Influence.Camposeco-Negrete etc. is tested by carrying out milling and is used response phase method to establish Milling Parameters and energy consumption
Regression equation, best parameter group has been obtained by experimental analysis.
In summary, the research that existing process sequence planning and cutting parameter towards energy consumption optimizes, is single link
Independent optimization, have ignored the interaction relationship between two links.On the one hand, part by numerical control process energy consumption is simultaneously
Influenceed by process route and cutting parameter scheme；On the other hand, numerous flexible (lathe flexibility, the cutter flexibilities of process route
Deng), causing the cutting parameter of each operation to combine has diversity.With single optimization process route or single optimization cutting parameter
Compare, digital control processing energy consumption can be further reduced by carrying out process route and cutting parameter integrated optimization.But, technique road
The influence relation of line and the paired energy consumption of cutting parameter collection is complex, meanwhile, carry out integrated optimization when how to coordinate energy consumption with
The conflict relationship of conventional target (such as time, cost, lathe load), is the key scientific problems of a urgent need to resolve.
The content of the invention
The purpose of the present invention is simultaneously process route and cutting parameter to be carried out integrated optimization to reduce digital control processing energy
Consumption.
To realize that the technical scheme that the object of the invention is used is such, i.e., a kind of digital control processing work towards energy consumption
Skill route and cutting parameter Integrated Optimization Model and method.It comprises the following steps：
Step 1:Propose the flow framework of the numerical control processing technology route and cutting parameter integrated optimization towards energy consumption；
Step 2：Analysis process route and the integrated energy consumption characteristics of cutting parameter；
Step 3：Load that minimum multiple target sets up numerical control processing technology route and cutting parameter is more with total energy consumption and lathe
Target Integrated Optimization Model, and propose a kind of optimization method based on multi-target simulation annealing algorithm.
Preferably, in step 1, the numerical control processing technology route and cutting parameter integrated optimization obtained towards energy consumption
The process of flow framework be：
Part by numerical control machining process route represents that parts expect a series of digital control processing works of finished product from casting former material
Skill process.Because the machining feature of part is complicated, each part generally has multiple machining feature units.The numerical control of part adds
The planning of work process route, is directed not only to the processing of multiple features, at the same each machining feature be also faced with multiple manufacturing procedures, it is many
Operation resource (lathe and cutter etc.), a variety of direction of feeds, a variety of processing sequences, the selection of a variety of cutting parameters are planted, this is just
A variety of flexibilities of numerical control processing technology planning are caused, as shown in Figure 1.
Mapping relations model between part machining feature and process route and cutting parameter, can be expressed as follows：
Σ: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, fiRepresent i-th of machining feature of part, OPiThe processing method for representing i-th of machining feature；opi,jTable
Show 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.
The present invention, flexible numerical machining process route and cutting parameter integrated optimization problem towards energy consumption can be described as：
Based on each machining feature unit of part, it is determined that corresponding manufacturing procedure (opi,j), for the machining tool needed for each process is selected
(Mi,j) and cutter (Ti,j), determine the direction of feed (TAD of each operationi,j), the processing sequence (seq of each operationi,j) and it is each
The selection cutting parameter combination (P of processi,j) so that selected process planning scheme loads the two mesh in energy consumption and lathe
Put on and reach that coordination is optimal.In the present invention, a kind of flexible numerical machining process route and cutting parameter towards energy consumption is integrated
The flow framework of optimization, as shown in Figure 2.
The assumed condition of the present invention is described as follows：
(1) certain process sequence must need to be followed between all process steps of same part to constrain, such as benchmark constraint,
Material removes 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 up of one or several manufacturing procedures.Such as, drilling may have one of drilling work
Skill is constituted, it is also possible to be made up of drilling-fraising-bore hole three process.
(3) 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,
Tool changing again is needed to be processed.
Preferably, in step 2, the integrated energy consumption characteristics analysis process of the numerical control processing technology route and cutting parameter
For：
Process of each operation on lathe is mainly made up of six basic links：Clamping of the part on lathe is determined
Position, cutter clamping, cut-in without ball process, cutting process, the tool changing of cutter blunt and workpiece dismounting.Fig. 3 illustrates a certain work
The machine power curve of sequence process.Seven links based on more than, the NC Machining Process total energy consumption of part can be calculated such as
Under：
Etotal=Esetup+Eair+Ecutting+Etoolchange
(1) the clamping process energy consumption E of each operationsetup
Each operation clamping process energy consumption calculation is as follows：
Wherein, tsetup(opi,j) clamping workpiece, cutter clamping, the temporal summation of workpiece dismounting are represented, by process route side
Case influences, and specific calculate sees below formula.t1、t2、t3It is a fixed value, clamping workpiece time, cutter dress is represented respectively
Folder time, workpiece take-down time.PstRepresent lathe standby power.
(2) the cut-in without ball energy consumption E of each operationair
The cut-in without ball process energy consumption calculation of each operation is as follows：
Wherein, PaucRepresent the power of machine power association class accessory system；PuFor lathe no-load power, mainly passed by main
Dynamic system no-load power and feeding no-load power composition, are specifically calculated as follows shown in formula：
Pu=Pspindle+Pfeed
PspindleFor machine-tool spindle system no-load power, it is in quadratic function relation with speed of mainshaft n, is specifically calculated as follows
Shown in formula：
Pspindle=a0n+a1n2
PfeedFor feed system no-load power, with feed speed fvIt is relevant, specifically it is calculated as follows shown in formula：
Pfeed=b0fv+b1(fv)2
tair(opi,j) air cutting time is represented, with cut-in without ball path (Lair) and fvIt is relevant, specifically it is calculated as follows shown in formula：
(3) the machining energy consumption E of each operationcutting
The machining energy consumption calculation of part each operation is as follows：
PcFor material cutting power, meet：Pc=δ MRR, wherein δ are cutting ratio energy coefficient (J/mm3)；MRR is unit
Material clearance (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) relevant with cutting parameter, specific calculate sees below formula：
(4) the blunt tool changing energy consumption E of each operationtoolchange
It is continuously increased with machining time of each operation, tool wear is gradually aggravated, when tool wear is to certain
Degree needs tool changing again to carry out machining, thus produces blunt tool change time.The tool changing of cutter blunt is general in the standby shape of lathe
Carried out under state, therefore blunt tool changing energy consumption can be specific as follows：
Wherein, ttoolchangeRepresent the blunt tool change time of blunt, it is contemplated that for the cutting at one time time within the cutter life cycle
Share, be specifically calculated as follows shown in formula：
TL(Ti,j,k) cutter life is represented, it can be calculated and obtained according to Taylor's formula, shown in formula specific as follows：
CT, m, u, v represent cutter life coefficient；D represents tool diameter；d0Represent that the diameter before machining is carried out in hole,
D ' represents that the diameter after machining is carried out in hole.
Preferably, it is described that numerical control processing technology road is set up with total energy consumption and the minimum multiple target of lathe load in step 3
The process of line and cutting parameter multiple target Integrated Optimization Model is：
(1) decision variable
In the present invention, towards the numerical control processing technology route and the decision variable of cutting parameter integrated optimization problem of energy consumption,
Including：1) it is each operation selection machining tool (Mi,j)；2) it is each operation selection process tool (Ti,j)；3) selected for each operation
Direction of feed (TADi,j)；4) the processing sequence seq (op of each operation are determinedi,j)；5) cutting parameter (P of each operation is determinedi,j)。
(2) object function
1) power dissipation obj ectives function
Analyzed according to energy consumption characteristics, part by numerical control processing total energy consumption is made up of four parts：Clamping energy consumption, cut-in without ball energy consumption, cut
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 is loaded in numerical control (NC) Machining Workshop
Equilibrium situation.W (k) is made to represent the processing load of kth platform lathe in workshop.The present invention is using two kinds of form calculus w (k)：
①w1(k) mainly it is made up of process time (air cutting time and cutting time) of the part on lathe, it is specific to calculate
See below formula：
②w2(k) it is made up of clamping time, air cutting time, machining time, blunt tool change time, it is specific to calculate such as
Shown in lower formula：
Numerical control (NC) Machining Workshop lathe load balancing degrees are represented, specific calculate sees below formula.It is smaller, then it represents that numerical control adds
The processing load of each lathe in work workshop is more balanced；Conversely,It is more big, represent that the processing of each lathe loads more uneven, workshop
Resource bottleneck it is more notable.
(3) constraints
The relevant constraint of the present invention is described as follows：
1) certain tight preceding relation constraint, such as locating clip tight constraint, benchmark are must comply between each manufacturing procedure of part
Constraint, material remove constraint etc..Define matrix PRE=[prei,j]M×MRepresent that the tight preceding constraint between each manufacturing procedure of part is closed
System.Wherein, M represents the manufacturing procedure sum of part；prei,jFor a binary variable, if prei,j=1 represents i-th
Manufacturing procedure need to carry out prior to j-th of process processes；If prei,j=0 represents between i-th of process and j-th of process
In the absence of tight preceding restriction relation.
2) the machining tool selection of each operation and cutting tool choice, mainly influence the range of choice of each cutting parameter.
①nmax≤n≤nmin nmaxAnd nminIt is lathe highest and minimum speed respectively
②fvmax≤fv≤fvmin fvmaxAnd fvminIt is lathe most fast and minimum feed speed respectively
③Pc≤ξ·Pmaxξ is lathe effective power coefficient, PmaxIt is lathe peak power
④Fc≤Fcmax FcmaxIt is the maximum cutting force of lathe
Based on above-mentioned analysis, the numerical control processing technology route and cutting parameter Integrated Optimization Model towards energy consumption, tool are set up
Body is as follows：
min f(Mijk, Tijk, seqijk, TADiJk,Pijk)=(min Etotal, min θ)
Preferably, it is described to propose a kind of mistake of the optimization method based on multi-target simulation annealing algorithm in step 3
Cheng Wei：
Simulated annealing (Simulated Annealing, SA) is a kind of based on Monte-Carlo iterative plans
Random optimizing algorithm slightly, because its unique Optimization Mechanism and versatility, flexibility have obtained extensively should in Combinatorial Optimization field
With.Traditional SA algorithms are 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 many in a minimum
In objective optimisation problems, if known two solutions RsAnd Rq, and there is f to each (k ∈ 1,2 .., K) object functionk(Rq)
≥fk(Rs), then claim solution RsBranch is assigned in solution Rq, or solution RqBy solution RsDominate.Archive be used to storing algorithm generation each is non-
Inferior solution.HL represents the memory span of Noninferior Solution Set.
In MOSA iterative process each time, based on current solution RqProduce an adjacent solution Rs.If adjacent solution RsBranch is assigned in
Current solution Rq, then R is usedsReplace Rq, while updating Archive；If RsDo not prop up assigned in Rq, then received with certain Probability p rob
Adjacent solution RsAnd replace Rq.Acceptance probability prob calculation is as follows：
Wherein, T represents temperature, and is constantly reduced with iterations；E(Rq, T) and E (Rs, T) and solution R is represented respectivelysAnd 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 end condition, MOSA algorithms just stop computing and export optimal result.
MOSA algorithm flow is as shown in Figure 4.
According to the characteristics of multiple target integrated optimization problem of the present invention, the committed step in algorithm is improved, specifically such as
Under：
(1) form of expression solved in MOSA
In view of process route and five decision variables of cutting parameter integrated optimization problem, therefore using a matrix A
=[at,w]8×WProcess route and cutting parameter solution are represented, it is specific as shown in Figure 5.Matrix A the first row represents that process is numbered
(opi,j)；Columns where each operation numbering represents the processing sequence of the process.Matrix A second and third, four rows represent each respectively
Lathe, cutter and direction of feed selected by process.If a certain process is processed for drilling, the row of matrix A the five to six is respectively
The speed of mainshaft and feed speed；If a certain process is turnery processing, the row of matrix A the five to seven is respectively the speed of mainshaft, feeding
Speed, back engagement of the cutting edge；If a certain process is Milling Process, the row of matrix A the five to eight is respectively the speed of mainshaft, feeding speed
Degree, back engagement of the cutting edge and working engagement of the cutting edge.
(2) the feasible processing sequence generation of tight preceding relation constraint is met
In MOSA algorithm iteration process each time, for the processing sequence solution of each random generation, 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 it is not satisfied, then repeating work
Sequence is sequentially generated process, until generation one meets the processing sequence solution of tight preceding relation constraint.In algorithm iteration mistake each time
Cheng Zhong, generation one meets the probability of the processing sequence solution of all tight preceding relation constraints, calculates as formula is as follows.
In above formula, N represents the tight preceding relation sum between each operation；ρ (i) represents that the processing sequence solution generated at random is met
The probability of i-th of tight preceding relation constraint.As N increases (for example, working as N>15), generate one and meet all tight preceding relation constraints
The probability of processing sequence decrease, thus MOSA iterations and run time increase.Therefore, for quickly generate can
Capable processing sequence solution, the present invention proposes a kind of processing sequence solution generation method for meeting tight preceding relation constraint, specifically such as
Under：
1) it is each process (op for given one tight preceding relation constraint matrix PREi,j) determine one it is tight before about
Beam rankThe process belonged under same rank is placed into the corresponding other process set (b of confinement levelk) in.
Set b0In all process steps do not influenceed by relation constraint before tight；SetIn all process steps must comply with
Relation constraint before process is tight, and it is tight before relation constraint rank it is smaller, the process in process set corresponding to the rank plus
Work priority is higher.For example, process set b5In process must be prior to b6In process processing.The process of each generation
SetIt is stored in matrix B={ b0,b1,…,bk,….,bKIn.Based on matrix generator matrix PRE generations
The program in machine code of matrix B, as shown in Figure 6.
2) according to tight preceding constraint rankOrder from small to large, successively by process set bkIt is put into
In queue.
MOSA algorithms are 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 solutions are generated
In the present invention, the generation step of MOSA initial solutions is as follows：
Step 1：A processing sequence initial solution is generated at random：
A) for each process set in matrix BTo the process numbering of its insideCarry out random alignment combination.For example, to b2={ 2,4,16 } are carried out after random alignment, are obtained：b2=4,
16,2}。
B) queue={ b are updated1,…,bk,….,bK}。
If c) process set b0It is not sky, then its internal K process numbering is subjected to random alignment combination, then
Process after permutation and combination is put into queue using plug hole method, while updating queue.
D) the process numbering in queue is put in order according to it, be sequentially placed into the first row of matrix A.
Step 2：It is that each process randomly chooses a machine in optional lathe set in the second row of matrix A
Bed.
Step 3：It is that each process randomly chooses a knife in optional cutter set in the third line of matrix A
Tool.
Step 4：It is that each process randomly chooses one in optional direction of feed set in the fourth line of matrix A
TAD。
Step 5：It is that each process is randomly choosed in technological parameter restriction range in the five to eight row of matrix A
One parameter value.
In initial solution generation phase, constantly repeat said process to generateIndividual initial solution is while more
New Archive.A solution is randomly choosed in the initial solution of generation as current solution.
(4) the adjacent solution generations of MOSA
Based on each current solution, adjacent solution is generated using the adjacent solution generting machanism of one kind.The present invention is using five kinds of modes
The adjacent solution of generation：
Mode 1：Using the adjacent solution of two ways generation processing sequence.
A) a process set is randomly choosed in matrix BRandomly choose bkIn any two work
Sequence simultaneously changes it and put 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：In the second row of matrix A, randomly choose in an element and optional lathe set corresponding to it
A lathe numbering is redistributed for it, adjacent solution is selected to generate a lathe.
Mode 3：In the third line of matrix A, randomly choose in an element and optional cutter set corresponding to it
A cutter numbering 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
A TAD is redistributed for it, to generate the adjacent solutions of a TAD.
Mode 5：A cutting parameter is arbitrarily selected in the five to eight row of matrix A, with a ratio generated at random
Increase or reduce the parameter, to generate the adjacent solution of a cutting parameter.
In view of every kind of adjacent solution generating mode to energy consumption and the influence difference of lathe load target, therefore, in order to improve
MOSA convergence rate, adjacent solution generation is carried out present invention introduces a kind of study mechanism.That is, each adjacent solution generating mode
Selected probabilityIt is not fixed, but as MOSA iterative process constantly learns change.According to i-th kind of phase
Neighbour's solution generating mode is generated after an adjacent solution, and the adjacent solution dominates current solution, and (energy consumption and lathe load target of adjacent solution are equal
Better than current solution), then update the select probability of the adjacent solution generating mode；Conversely, the selected probability of the adjacent solution is maintained not
Become, specific calculate sees below formula.With the continuous iteration of MOSA algorithms, performance is preferably adjacent to solve the selected general of generating mode
Rate gradually increases, and the selected probability of the adjacent solution generating mode of poor-performing is constantly reduced, and thus improves the empty in solution of MOSA
Interior speed of searching optimization.
In above formula, xiRepresent the performance value of i-th kind of adjacent solution generating mode；ziAnd yiI-th kind of expression is adjacent respectively
Solve performance value of the generating mode on two object functions of total energy consumption and lathe load balancing；x0=0.1 is every kind of adjacent
Solve the performance initial value of generating mode.
Brief description of the drawings
A variety of flexibilities of Fig. 1 numerical control processing technology routes
Process routes and cutting parameter integration and optimization framework of the Fig. 2 towards energy consumption
The machine power curve of a certain process process of Fig. 3
The code flow of Fig. 4 MOSA algorithms
The form of the solution of Fig. 5 process routes and cutting parameter integrated optimization
Program in machine codes of the Fig. 6 based on matrix PRE generator matrixes B
Fig. 7 parts 1：Electric baseboard
Fig. 8 parts 2：Base
Fig. 9 total energy consumptions and lathe load balancingThe changing rule increased with a
The changing rule that all kinds of energy consumptions of Figure 10 digital control processings increase with a
Figure 11 total energy consumptions and lathe load balancingThe changing rule increased with a
Embodiment
The invention will be further described with reference to the accompanying drawings and examples, but should not be construed above-mentioned master of the invention
Topic scope is only limitted to following embodiments.Without departing from the idea case in the present invention described above, according to ordinary skill
Knowledge and customary means, make various replacements and change, all should 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 processing based on Fig. 7-8 is special
Levy, analysis has obtained feasible manufacturing procedure, lathe, cutter, direction of feed information, as shown in table 1.The tight preceding pass of each operation
It is restraint condition, it is specific as shown in table 2.
Digit Control Machine Tool realtime power is measured using lathe energy efficiency monitoring system, by installing HC33C3 in Machine Tool Electric Appliance cabinet
The acquisition of type power sensing type is the total voltage and total current of lathe, then obtains the real-time work(of lathe by digital filtering and calculating
Rate signal, related power coefficient is obtained after power information is handled by nonlinear regression and fitting.The power letter of each machining tool
Breath is 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, loaded with total energy consumption minimum and lathe equal
Weigh as the necessity of multiple target integrated optimization, devise 5 cases and carry out Optimization Solutions, as shown in table 5.Wherein, fixed cutting
During parameter, the cutting parameter of each operation takes the intermediate value in optional scope；During technique for fixing route, the lathe of each operation, cutter,
Direction of feed, processing sequence are generated at random.
The information such as manufacturing procedure, lathe, cutter, the direction of feed of the part of table 1
Tight preceding relation constraint between each manufacturing procedure of the part of table 2
The machining tool information of table 3
The process tool information of table 4
The case comparative analysis of table 5
(1) necessity of process route and cutting parameter integrated optimization
1) as shown in Table 5, during optimization aim minimum with total energy consumption, process route and cutting parameter are carried out integrated excellent
Change (case 3.1), compared with single optimization process route (case 1.1), the former energy consumption reduces 31%；Process route with
Cutting parameter integrated optimization (case 3.1), compared with single optimization cutting parameter (case 2.1), the former energy consumption is reduced
16%.
2) when as shown in Table 5, using lathe load balancing as optimization aim, process route and cutting parameter are carried out integrated
Optimize (case 3.2), compared with single optimization process route (case 1.2), the former lathe load reduction 29%；Technique
Route and cutting parameter integrated optimization, compared with single optimization cutting parameter (case 2.2), the former lathe load reduction
20%.
In summary, compared with single optimization process route or single optimization cutting parameter, by carry out process route with
Cutting parameter integrated optimization, can further reduce digital-control processing system total energy consumption, while energy efficient balance numerical control (NC) Machining Workshop is each
The processing load of lathe, so as 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 with cutting parameter integrated optimization, with the minimum optimization mesh of total energy consumption
Mark (case 3.1), with using lathe load balancing, compared with optimization aim (case 3.2), the former total energy consumption reduces by 53%, machine
Bed load increase by 47%.As can be seen here, when carrying out numerical control processing technology route and cutting parameter integrated optimization, total energy consumption with
Lathe load collides with each other relation in the presence of certain.
2) relation is collided with each other between total energy consumption and lathe load in order to further verify, with total energy consumption (Etotal)
Minimum optimization aim, while considering lathe load balancing constraint (formula 29), carries out numerical control processing technology route and cutting is joined
Number integrated optimization (case 4).
θminα (1-10%)≤θ1≤θmin·α
In above formula, α ∈ (1, x) represent that the width of lathe load balancing constraint puts coefficient, wherein, x=θmax/θmin；θminWith
θmaxThe minimum lathe load balancing degrees of numerical control (NC) Machining Workshop, maximum lathe load balancing degrees are represented respectively.It is equal with lathe load
When weighing as single goal development integrated optimization, θ is obtained by MOSA Algorithm for Solvingmin=0.3067, θmax=0.9823；Thus count
Obtain：X=3.202.
Total energy consumption, clamping energy consumption, cut-in without ball energy consumption, machining energy consumption, blunt tool changing energy are obtained by SA Algorithm for Solving
Consumption, lathe loadWithThe changing rule gradually increased with α, as shown in figs. 9-11.The α of each in Fig. 9-11 ∈ (1, x)
Corresponding data point, is the minimum total energy consumption value and corresponding clamping energy consumption, cut-in without ball energy for taking 5 SA algorithm simulatings to obtain
Consumption, machining energy consumption, blunt tool changing energy consumption, lathe loadWith
As shown in Figure 9：As α from 1 is gradually increased to x, digital 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 due to：
1. as α=1, influence of the lathe load restraint to Integrated Optimization Model is the most notable.Now, SA algorithms are necessary
The minimum solution of total energy consumption is found on the premise of lathe load restraint is met, is mainly realized by two kinds of approach：
Approach one：Larger cutting parameter is chosen, to reduce the machining time of each operation, each lathe is thus reduced
Processing load and realize lathe load balancing in workshop.On the one hand, larger cutting parameter is selected, each operation is reduced
The machining time, machining energy consumption is caused also to reduce (Figure 10 c) therewith)；On the other hand, larger cutting ginseng is selected
Number, causes tool wear to be aggravated, thereby increases blunt tool change time and blunt tool changing energy consumption (Figure 10 d)).Because blunt is changed
The increase of knife energy consumption is more more notable 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, the lathe selected by each operation is allowed to be in dispersity, to realize workshop
Lathe load balancing.Simultaneously as the selection of each operation lathe is scattered, thus lathe changes number of times and clamping energy consumption increases
(Figure 10 a)).
2. as α from 1 constantly increases to x, influence of the lathe load restraint to Integrated Optimization Model gradually weakens.Now,
MOSA constantly reduces digital control processing total energy consumption using two kinds of approach：
Approach one：Less cutting parameter is constantly selected, to realize the coordination of blunt tool changing energy consumption and machining energy consumption
It is optimal.Meanwhile, the increase of machining time, it is in increasing trend to cause lathe load.
Approach two：In the process route optimization stage, lathe selected by each operation is allowed gradually to be changed from dispersity to concentration.
On the one hand, with the concentration that each operation lathe is selected, thus the clamping time of each operation and clamping energy consumption reduce (Figure 10 a))；
On the other hand, the concentration of each operation lathe selection, exacerbates the lathe load imbalance of numerical control (NC) Machining Workshop, thus causes machine
Bed loadIn increasing trend (Fig. 9).
In summary, it can be illustrated by Fig. 9-10：Total energy consumption is loaded with latheBetween there is obvious mutually punching
Tip out system.Therefore, it need to carry out integrated excellent towards energy consumption and the numerical control processing technology route and cutting parameter multiple target of lathe load
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 calculations
One group of multiple target integrated optimization Pareto solutions that method is obtained.The parameter setting of MOSA algorithms is as follows：
Tmax=200, Tmin=10^ (- 5), HL=30, iter=75, a=0.9.Table 6 illustrates Pareto solutions most
Excellent process route and cutting parameter.
As shown in Table 5：It is minimum and lathe load balancing is multiple target development integrated optimization (case 5) using total energy consumption, 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 reduction by 35%, lathe are loaded
Increase by 17%.It is indicated above：By carrying out numerical control processing technology route and cutting parameter integrated optimization, total energy consumption can be realized
The coordination of minimum and two targets of lathe load balancing is optimal.
Total energy consumption (Etotal) is loaded with latheBetween conflict relationship, as shown in figure 11.As shown in Figure 11：Lathe
LoadAs α increase shows increased trend after first reduction, the changing rule with total energy consumption Etotal is more consistent.
This is due to：Lathe is loadedInfluenceed simultaneously by clamping time, air cutting time, machining time and blunt tool change time；
Because total energy consumption (Etotal) is made up of clamping energy consumption, cut-in without ball energy consumption, machining energy consumption and the part of blunt tool changing energy consumption four,
Therefore total energy consumption is also influenceed by clamping time, air cutting time, machining time and blunt tool change time simultaneously.Although, lathe
LoadSimilar to the changing rule of total energy consumption, still, α values corresponding to both minimum point values are different.Therefore can be with
Explanation：Lathe is loadedWith total energy consumption relation is collided with each other there is also certain.
The optimum process route of table 6 and cutting parameter scheme
Claims (5)
1. towards the numerical control processing technology route and cutting parameter Optimized model and method of energy consumption, it is characterised in that including following step
Suddenly：
Step 1:Propose the flow framework of the numerical control processing technology route and cutting parameter integrated optimization towards energy consumption；
Step 2：Analysis process route and the integrated energy consumption characteristics of cutting parameter；
Step 3：Minimum multiple target is loaded with total energy consumption and lathe and sets up 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.
2. the numerical control processing technology route according to claim 1 towards energy consumption and cutting parameter Optimized model and method,
It is characterized in that：The stream of a kind of numerical control processing technology route towards energy consumption and cutting parameter integrated optimization is obtained in step 1
The process of journey framework is：
Flexible numerical machining process route and cutting parameter integrated optimization problem in the present invention towards energy consumption can be described as：It is based on
Each machining feature unit of part, it is determined that corresponding manufacturing procedure (opi, j), for the machining tool needed for each process is selected
(Mi,j) and cutter (Ti,j), determine the direction of feed (TAD of each operationi,j), the processing sequence (seq of each operationi,j) and each work
The selection cutting parameter combination (P of sequencei,j) so that selected process planning scheme loads the two targets in energy consumption and lathe
On reach coordination it is optimal.
The assumed condition of the present invention is described as follows：
(1) must need to follow certain process sequence constraint between all process steps of 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 up of one or several manufacturing procedures.
(3) 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 identicals.
(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.
3. the numerical control processing technology route according to claim 1 towards energy consumption and cutting parameter Optimized model and method,
It is characterized in that：The process of the integrated energy consumption characteristics of analyzing numerically controlled machining process route and cutting parameter is in step 2：
The NC Machining Process total energy consumption of part can be calculated as follows：
Etotal=Esetup+Eair+Ecutting+Etoolchange
Wherein, EsetupFor the clamping process energy consumption of each operation, EairFor the cut-in without ball energy consumption of each operation, EcuttingFor cutting for each operation
Cut power consumption of polymer processing, EtoolchangeFor the blunt tool changing energy consumption of each operation.
4. the numerical control processing technology route according to claim 1 towards energy consumption and cutting parameter Integrated Optimization Model and side
Method, it is characterised in that：Minimum multiple target is loaded in step 3 with total energy consumption and lathe to set up numerical control processing technology route and cut
The process for cutting parameter multiple target Integrated Optimization Model is：
(1) decision variable
Including：1) it is each operation selection machining tool (Mi,j)；2) it is each operation selection process tool (Ti,j)；3) selected for each operation
Select direction of feed (TADi,j)；4) the processing sequence seq (op of each operation are determinedi,j)；5) cutting parameter of each operation is determined
(Pi,j)。
(2) object function
1) power dissipation obj ectives function
Analyzed according to energy consumption characteristics, part by numerical control processing total energy consumption is made up 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 made to represent the processing load of kth platform lathe in workshop.The present invention is using two kinds of form calculus w (k)：
①w1(k) mainly it is made up of process time (air cutting time and cutting time) of the part on lathe, specific calculate sees below public affairs
Formula：
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Numerical control (NC) Machining Workshop lathe load balancing degrees are represented, specific calculate sees below formula.
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(3) constraints
1) certain tight preceding relation constraint is must comply between each manufacturing procedure of part.
PRE=[prei,j]M×MRepresent the tight preceding restriction relation between each manufacturing procedure of part.Wherein, M represents the processing work of part
Sequence sum；prei,jFor a binary variable, if prei,j=1 represents that i-th of manufacturing procedure need to be opened prior to j-th of process
Exhibition processing；If prei,j=0 represents that tight preceding restriction relation is not present between i-th of process and j-th of process.
2) the machining tool selection of each operation and cutting tool choice, mainly influence the range of choice of each cutting parameter.
①nmax≤n≤nmin nmaxAnd nminIt is lathe highest and minimum speed respectively
②fvmax≤fv≤fvmin fvmaxAnd fvminIt is lathe most fast and minimum feed speed respectively
③Pc≤ξ·Pmaxξ is lathe effective power coefficient, PmaxIt is lathe peak power
④Fc≤Fcmax FcmaxIt is the maximum cutting force of lathe
Based on above-mentioned analysis, the numerical control processing technology route and cutting parameter Integrated Optimization Model towards energy consumption are set up, specifically such as
Under：
min f(Mijk,Tijk,seqijk,TADijk,Pijk)=(min Etotal,min θ)
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5. the numerical control processing technology route according to claim 1 towards energy consumption and cutting parameter Optimized model and method,
It is characterized in that：Propose that a kind of process of the optimization method based on multi-target simulation annealing algorithm is in step 3：
According to the characteristics of multiple target integrated optimization problem of the present invention, the committed step in simulated annealing is improved, had
Body is as follows：
(1) form of expression solved in MOSA
In view of process route and five decision variables of cutting parameter integrated optimization problem, therefore using a matrix A=[at,
w]8×WRepresent process route and cutting parameter solution.Matrix A the first row represents that process numbers (opi,j)；Columns where each operation numbering
Represent the processing sequence of the process.Matrix A second and third, four rows represent lathe, cutter and feed side selected by each operation respectively
To.If a certain process is processed for drilling, the row of matrix A the five to six is respectively the speed of mainshaft and feed speed；If a certain process
For turnery processing, then the row of matrix A the five to seven is respectively the speed of mainshaft, feed speed, back engagement of the cutting edge；If a certain process is milling
Processing, then the row of matrix A the five to eight 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 generation of tight preceding relation constraint is met
The present invention proposes a kind of processing sequence solution generation method for meeting tight preceding relation constraint, specific as follows.
1) it is each process (op for given one tight preceding relation constraint matrix PREi,j) determine a tight preceding confinement level
NotThe process belonged under same rank is placed into the corresponding other process set (b of confinement levelk) in.Set
b0In all process steps do not influenceed by relation constraint before tight；SetIn all process steps must comply with process
Relation constraint before tight, and tight preceding relation constraint rank is smaller, the processing of the process in process set corresponding to the rank is preferential
Level is higher.
2) according to tight preceding constraint rankOrder from small to large, successively by process set bkIt is put into queue.
(3) MOSA initial solutions are generated
It is each process (op for given one tight preceding relation constraint matrix PREi,j) determine a tight preceding constraint rankThe process belonged under same rank is placed into the corresponding other process set (b of confinement levelk) in.
According to tight preceding constraint rankOrder from small to large, successively by process set bkIt is put into queue.
MOSA algorithms are based on matrix B and queue, raw feasible processing sequence initial solution or adjacent in iterative process each time
Solution.
Step 1：A processing sequence initial solution is generated at random：
A) for each process set in matrix BTo the process numbering of its insideEnter
Row random alignment is combined.
B) queue={ b are updated1,…,bk,….,bK}。
If c) process set b0It is not sky, then its internal K process numbering is subjected to random alignment combination, then will arrangement
Process after combination is put into queue using plug hole method, while updating queue.
D) the process numbering in queue is put in order according to it, be sequentially placed into the first row of matrix A.
Step 2：It is that each process randomly chooses a lathe in optional lathe set in the second row of matrix A.
Step 3：It is that each process randomly chooses a cutter in optional cutter set in the third line of matrix A.
Step 4：It is that each process randomly chooses a TAD in optional direction of feed set in the fourth line of matrix A.
Step 5：It is that each process randomly chooses one in technological parameter restriction range in the five to eight row of matrix A
Parameter value.
In initial solution generation phase, constantly repeat said process to generateIndividual initial solution updates simultaneously
Archive.A solution is randomly choosed in the initial solution of generation as current solution.
(4) the adjacent solution generations of MOSA
Based on each current solution, adjacent solution is generated using the adjacent solution generting machanism of one kind.The present invention is generated using five kinds of modes
Adjacent solution：
Mode 1：Using the adjacent solution of two ways generation processing sequence.
A) 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：In the second row of matrix A, it is it to randomly choose in an element and optional lathe set corresponding to it
A lathe numbering is redistributed, adjacent solution is selected to generate a lathe.
Mode 3：In the third line of matrix A, it is it to randomly choose in an element and optional cutter set corresponding to it
A cutter numbering is redistributed, to generate the adjacent solution of a cutting tool choice.
Mode 4：In the fourth line of matrix A, randomly choose in an element and optional TAD set corresponding to it as its is heavy
A TAD is newly distributed, to generate the adjacent solutions of a TAD.
Mode 5：A cutting parameter optionally is selected in the row of matrix A the five to eight, increases or subtracts with a ratio generated at random
Few parameter, to generate the adjacent solution of a cutting parameter.
In order to improve MOSA convergence rate, adjacent solution generation is carried out present invention introduces a kind of study mechanism.That is, each is adjacent
Solve the selected probability of generating modeIt is not fixed, but as MOSA iterative process constantly learns change.If adopting
Generated with i-th kind of adjacent solution generating mode after an adjacent solution, the adjacent solution dominates current solution, then update the adjacent solution generation side
The select probability of formula；Conversely, the selected probability of the adjacent solution remains unchanged, specific calculate sees below formula.With MOSA algorithms
Continuous iteration, the selected probability of the preferably adjacent solution generating mode of performance gradually increases, the adjacent solution generation side of poor-performing
The selected probability of formula is constantly reduced, and thus improves the MOSA speed of searching optimization in solution space.
In above formula, xiRepresent the performance value of i-th kind of adjacent solution generating mode；ziAnd yiI-th kind of adjacent solution generation is represented respectively
Performance value of the mode on two object functions of total energy consumption and lathe load balancing；x0=0.1 is every kind of adjacent solution generation
The performance initial value of mode.
Priority Applications (1)
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108320049A (en) * | 2018-01-11 | 2018-07-24 | 山东科技大学 | Numerically controlled lathe multi-station turning knife rest automatic tool changer energy consumption Accurate Prediction method |
| CN108393744A (en) * | 2018-04-11 | 2018-08-14 | 温州大学 | A kind of more sensor monitoring methods of cutting tool state |
| CN108628272A (en) * | 2018-06-28 | 2018-10-09 | 上海电力学院 | The process parameter optimizing method that optimum seeking method based on cost is coupled with law of planning |
| CN109299567A (en) * | 2018-10-22 | 2019-02-01 | 重庆大学 | One kind is towards energy-efficient numerically controlled lathe main transmission design optimization method |
| CN109799706A (en) * | 2019-01-24 | 2019-05-24 | 重庆大学 | The turning process parameter adaptive efficiency optimization method of knowledge based driving |
| CN110673557A (en) * | 2019-09-27 | 2020-01-10 | 南京大学 | Intelligent chemical system based on process condition selection |
| CN111158313A (en) * | 2020-01-14 | 2020-05-15 | 上海交通大学 | Method for modeling energy consumption and optimizing machining process of numerical control machine tool |
| CN111522297A (en) * | 2020-05-09 | 2020-08-11 | 湖南工学院 | Numerical control machining control method and device based on energy consumption optimization and electronic equipment |
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| CN112948994A (en) * | 2021-01-29 | 2021-06-11 | 重庆大学 | Multi-objective optimization and decision method for hobbing technological parameters |
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| CN114660994A (en) * | 2022-05-25 | 2022-06-24 | 中科航迈数控软件(深圳)有限公司 | Decision optimization method, system and related equipment for machining process of numerical control machine tool |
| CN115291529A (en) * | 2022-10-10 | 2022-11-04 | 深圳大学 | Numerical control batch machining cutting parameter optimization method responding to cutter wear time-varying characteristic |
| CN116679614A (en) * | 2023-07-08 | 2023-09-01 | 四川大学 | Multi-feature cutter comprehensive adaptation method based on evolution game |
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005081434A (en) * | 2003-09-11 | 2005-03-31 | Fanuc Ltd | Numerical control apparatus |
| CN103235554A (en) * | 2013-03-29 | 2013-08-07 | 重庆大学 | Numerically controlled lathe processing workpiece energy consumption acquiring method based on NC (numerical control) codes |
| CN103971019A (en) * | 2014-05-23 | 2014-08-06 | 武汉科技大学 | Method for predicting workpiece machining energy consumption based on geometrical characteristics |
| CN104267693A (en) * | 2014-09-22 | 2015-01-07 | 华中科技大学 | Method for optimizing cutting parameters considering machining energy efficiency |
| CN104517033A (en) * | 2014-12-17 | 2015-04-15 | 重庆大学 | Multi-target optimization method for numerical control machining technological parameters facing energy efficiency |
| 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 |
| 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 |
| CN105785912A (en) * | 2016-03-22 | 2016-07-20 | 重庆大学 | Energy-consumption-oriented cavity numerical control milling cutter combination optimization method |
| CN105929689A (en) * | 2016-04-22 | 2016-09-07 | 江南大学 | Machine tool manufacturing system processing and energy saving optimization method based on particle swarm algorithm |
-
2017
- 2017-06-22 CN CN201710480344.0A patent/CN107193258B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005081434A (en) * | 2003-09-11 | 2005-03-31 | Fanuc Ltd | Numerical control apparatus |
| CN103235554A (en) * | 2013-03-29 | 2013-08-07 | 重庆大学 | Numerically controlled lathe processing workpiece energy consumption acquiring method based on NC (numerical control) codes |
| CN103971019A (en) * | 2014-05-23 | 2014-08-06 | 武汉科技大学 | Method for predicting workpiece machining energy consumption based on geometrical characteristics |
| CN104267693A (en) * | 2014-09-22 | 2015-01-07 | 华中科技大学 | Method for optimizing cutting parameters considering machining energy efficiency |
| CN104517033A (en) * | 2014-12-17 | 2015-04-15 | 重庆大学 | Multi-target optimization method for numerical control machining technological parameters facing energy efficiency |
| 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 |
| 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 |
| CN105785912A (en) * | 2016-03-22 | 2016-07-20 | 重庆大学 | Energy-consumption-oriented cavity numerical control milling cutter combination optimization method |
| CN105929689A (en) * | 2016-04-22 | 2016-09-07 | 江南大学 | Machine tool manufacturing system processing and energy saving optimization method based on particle swarm algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 何彦: "一种面向机械车间柔性工艺路线的加工任务节能", 《机械工程学报》 * |
| 李聪波，等: "面向能耗的多工艺路线柔性作业车间", 《机械工程学报》 * |
| 陈行政，等: "面向能效的多工步数控铣削工艺参数多目标优化模型", 《计算机集成制造系统》 * |
Cited By (24)
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|---|---|---|---|---|
| CN108320049A (en) * | 2018-01-11 | 2018-07-24 | 山东科技大学 | Numerically controlled lathe multi-station turning knife rest automatic tool changer energy consumption Accurate Prediction method |
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| CN108393744A (en) * | 2018-04-11 | 2018-08-14 | 温州大学 | A kind of more sensor monitoring methods of cutting tool state |
| CN108393744B (en) * | 2018-04-11 | 2023-07-18 | 嘉兴南湖学院 | Multi-sensing monitoring method for cutter state |
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