ENERGY EFFICIENCY OPTIMIZATION OF MECHANICAL NUMERICAL CONTROL MACHINING PARAMETERS

Transcription

1 ENERGY EFFICIENCY OPTIMIZATION OF MECHANICAL NUMERICAL CONTROL MACHINING PARAMETERS Zpeng LI*, Ren SHENG Yellow Rver Conservancy Techncal Insttute, School of Mechancal Engneerng, Henan , Chna. Correspondng author: Zpeng LI Emal: ABSTRACT: In order to mprove the energy effcency of mechancal numercal control (NC) machng parameters, the man factors that affect the selecton of process parameters are put forward frst. Then, the energy effcency optmzaton method of NC machnng parameters s analyzed n detal. In addton, optmzaton test condtons of numercal control machnng based on Taguch method and mult-objectve optmzaton model of NC machnng parameters based on energy effcency and tme are dscussed n detal. Moreover, the model s solved on the bass of partcle swarm optmzaton. Fnally, the energy effcency optmzaton results of NC parameters are analyzed from three aspects. The results show that the proposed optmzaton method has a certan valdty and feasblty and can provde a certan theoretcal support for mprovng the energy effcency of computer numercal control (CNC) machne tools. KEYWORDS: Mechancal numercal control machnng; process parameters; energy effcency; optmzaton 1 INTRODUCTION The optmzaton of tradtonal process parameters s manly based on the maxmum proft, the hghest producton effcency, the roughness and cuttng force, and a large number of process parameter optmzaton methods have been accumulated(l et al., 2015; Pare et al., 2015). Wth the mprovement of energy savng awareness, some research on process parameter of CNC machnng systems whch consder energy consumpton has been gradually appeared, and some progress has been made. At present, there are three knds of research on energy savng optmzaton of CNC machnng process: the frst s to analyse the relatonshp between energy effcency and process parameters (Sahoo et al., 2017). The second s that some scholars carry out the research on the energy effcency optmzaton of the process parameters by buldng optmzaton model (Yıldız et al., 2017). The thrd method consders the dffculty of establshng the mathematcal model of energy consumpton. In order to smplfy the cumbersome calculaton process of formula, some scholars analyse process parameters and energy effcency by desgn optmzaton experment. The optmal combnaton of parameters s chosen or the optmzaton algorthm s used to further optmze the soluton (Gulat et al., 2016). To sum up, the research on the energy savng optmzaton of the process parameters n the process of NC machnng s developng rapdly. Moreover, the thrd method wth more lteratures are selected from the expermental data to select the optmal combnaton (Goswam et al., 2017). However, the optmal soluton may not be wthn the expermental combnaton. Moreover, the multple targets are assocated as an equaton, and the nteracton between the energy effcency targets and the other targets and the process parameters s not very clear. The technology of energy effcency optmzaton of NC machnng system needs to be further deepened. The optmzaton rules need to be further explored and summarzed. 2 METHODOLOGY 2.1 Analyss of the man factors affectng the selecton of process parameters The relatonshp between the process parameters and the ndependent varables and the nonndependent varables s shown n fgure 1. 76

2 Non-ndependent varables Independent varable Cuttng force Precson Machne tool Maxmum rotary dameter, spndle power, etc. Energy consumpton Cuttng temperature Surface Qualty Processng effcency Tool Workpece Tool materal, tool geometry, etc. Workpece materal, workpece sze, etc.... Cuttng deformaton Processng costs Cuttng flud Dry cut, wet cut The best process parameters Fgure 1. Analyss of CNC machnng varables The selecton of process parameters s the most dynamc task n the actual processng, and t s not an ndependent decson makng process. In general, the selecton of process parameters s restrcted by ndependent varables and non-ndependent varables. Independent varables are factors that can be controlled ndependently n the NC process wthout causng changes n other varables except themselves. In the NC machnng process, t manly refers to the machne tool, work-pece and cuttng tool (Tryak et al., 2015). A non-ndependent varable s a factor that changes n response as ndependent varables change. The processng precson, the processng cost and the energy dsspaton are the man processes (Krshna et al., 2015). 2.2 Energy effcency optmzaton method for NC machnng process parameters based on expermental data Accordng to the power curve, the composton characterstcs of the energy consumpton n each perod of the NC machnng are dfferent. In order to establsh the energy consumpton model, the dfferent knds of power n each perod need to be subdvded (Suresh et al., 2017). But n fact, f the NC machnng system s regarded as black box problem, we only need to measure the total power of each perod under dfferent combnatons of process parameters, so that we can avod complcated power decomposton and get a more accurate relatonshp model between energy consumpton and process parameters. The energy effcency optmzaton method of process parameters based on expermental desgn takes the NC machnng system as the core dea. Ths method has the characterstcs of easy realzaton and hgh relablty. The optmal parameters can be obtaned by selectng only small parts of the same batch as samples. It s especally sutable for mass producton of parts. In vew of the Taguch method, the test can be systematcally planned and the optmal trend s ponted out by a few experments. The response surface method can be used to assocate the varables wth the response equaton n the case of the unknown two varables. The Taguch method s used for the test, and the relatonshp between the energy effcency of NC turnng and NC mllng process, as well as the processng tme and the process parameters are analyzed respectvely (Mandal et al., 2016). The mult objectve optmzaton model, whch takes the cuttng parameters as the optmzaton varable and the energy effcency and the regresson equaton of the processng tme as the objectve functon, s establshed. The mproved mult-objectve partcle swarm optmzaton algorthm s used to fnd the optmal soluton. The specfc method s shown n fgure 2. 77

3 Determne the expermental condtons and parameter levels Step1 : Expermental desgn and expermental analyss based on Taguch method Orthogonal experment desgn Sgnal to nose rato analyss Range analyss Step2: Model soluton based on mproved mult-objectve partcle swarm optmzaton Establsh mult-objectve optmzaton model Fnd Pareto solutons n the context of processng constrants Meet the maxmum number of teratons N Y Output the optmal soluton Fgure 2. Flow chart of optmzaton method based on expermental data 2.3 Optmzaton test condtons of NC machnng based on Taguch method The expermental research on the energy savng optmzaton of NC mllng process parameters takes the mllng plane of a part as an example. In the experment, the numercal control mllng machne of the Precse PL700 vertcal machnng center s used. The power of the man motor s 5.5/7.5Kw, and the spndle speed range s r/mn. The feed speed range s mm/mn, and the maxmum tool dameter s 75mm. The work-peces and processng methods are shown n table 1. The specfc parameters of the ntegral mllng cutter are shown n table 2. Table 1. Workpece and processng methods Workpece materal Workpece length Workpece wdth Mllng method Processng path 45# steel 70mm 12mm Mllng S type 78

4 Tool materal Hghspeed steel Table 2. Type of mllng tool and ts parameters Number of Cuttng Tool Front Clearanc Helx cuttng edges edge length dameter corner e angle angle 4 20mm 4mm The energy savng optmzaton of NC turnng process parameters takes the outer crcle of batch turnng as an example. In the experment, a CNC turnng machne wth a model of C2-360HK s used. The power of the man motor s 5.5Kw, and the spndle speed range s r/mn. The feed speed range s mm/mn. The maxmum allowable dameter of rotaton s 360mm. The nformaton of the work-peces and processng methods s shown n table 3. The specfc parameters of the external crcle tool are shown n table 4. Table 3. Workpeces and processng methods Workpece materal Workpece length Workpece dameter Turnng method 40Cr 64mm 30mm Cylndrcal processng Tool materal Carbde Table 4. Type of turnng tool and ts parameters Tool type Tool rake Cutter Tool man Arc radus of the tool angle angle declnaton Cylndrcal mm turnng 2.4 Mult objectve optmzaton model for NC machnng process parameters Varable optmzaton: In NC mllng, specfc energy and tme objectves are manly affected by the spndle speed n, feed per tooth fz, cuttng depth and a p and workng engagement a e. However, n NC machnng, specfc energy and tme objectves are manly affected by the cuttng speed v c, feed rate f and cuttng depth a p. Therefore, the four elements of mllng and the three factors of turnng are used as the optmzaton decson varables respectvely. Objectve optmzaton: The specfc energy regresson model s used as the objectve functon of the mllng process. The processng tme regresson model s used as the tme objectve functon of the mllng process. The specfc energy regresson model s used as the objectve functon of the turnng process. The processng tme regresson model s used as the tme objectve functon of the turnng process. Constrant condton: In the process of NC machnng, the decson varables should meet the constrants of varous processng condtons. Accordng to the actual cuttng process, the machne tools constrants, tool constrants and machnng qualty constrants are selected. Machne tool constrant: Any cuttng process needs to be carred out wthn the allowable range of the machne's rgdty, whch s the man constrant to lmt the processng. xmn x xmax( 1,,n) FC Fmax (1) PC 60Fcv c Pmax In the formula, x s an optmzed varable. F max s a cuttng force that s allowed by a machne tool. P max s the rated power of the machne tool. s the effcency of the machne tool. Tool constrants: The frequent knfe change wll affect the processng contnuty and precson. Its constrants should meet the followng expresson: T mn T (2) In the formula, T mn s the lower lmt of tool lfe. Constrants of processng qualty: Processng qualty can also be used as an optmzaton goal. However, n the optmzaton of other functons, the qualty of processng s the precondton of optmzaton. Formula (3) s used for plane mllng, and formula (4) s used for turnng. fz R a 318 [ R ] (3) a tg( L ) ctg( C ) a f R a [ R a ] (4) r In the formula, L a s the front angle of the tool. C a s the rear angle of the tool. [R a ] s the maxmum surface roughness allowed by the work-pece. r s the radus of the cutter's arc. To sum up, the mathematcal model of the multobjectve optmzaton model of the process a 79

5 parameters n the NC machnng process s as follows: mn F (mn T, mn SEC) xmn x x FC FCmax PC Pmax Tmn T Ra [ Ra] P max (5) 2.5 Mult objectve partcle swarm optmzaton algorthm based on cross method The mult-objectve partcle swarm optmzaton (MOPSO) algorthm wth hgh qualty and good robustness s used to solve the mult-objectve optmzaton model of NC mllng process parameters. In the optmzaton soluton process, all possble solutons wthn the feasble doman are consdered as a poston n the search space, that s, "partcle". Each partcle s characterzed by three ndcators: poston, speed and ftness. In ths case, each ndvdual n the partcle populaton represents a processng scheme for the optmzaton of NC mllng process parameters. The ftness value s the target value of the specfc energy and the target value of the processng tme. Because the decson varables are the cuttng elements, the decson varables for each processng scheme are stored n multdmensonal space accordng to the dfferent knds of process. The best poston t experences s P. The best partcle poston n the group s recorded as P gb. Durng the evolutonary process of partcle, ts speed and poston are updated as follows: V X (6) k 1 k 1 V X k k V c r( P k k X k ) c r( P 2 2 k gb X k ) In the formula, ω s an nerta weght. r 1 and r 2 are the random numbers between [0,1]. c 1 and c 2 are learnng factors. Although the partcle swarm optmzaton algorthm has strong generalty, t also has the dsadvantages such as precocous convergence and low effcency n later teraton. Based on ths, the followng two mproved partcle learnng strateges are used n ths paper. The frst s the adaptve nerta weght. The nerta weght adjustment strategy can better balance the global search and local search capablty of the algorthm. When ω takes a larger value, the global search capablty s mproved. When ω takes smaller value, the local search capablty s enhanced. In the current research, the nertal adjustment strategy s manly dvded nto three knds: lnear decreasng strategy, nonlnear decreasng strategy and adaptve adjustment strategy. The second s the cross method. After ntroducng the selectve cross operaton n the genetc algorthm, the algorthm jumps out the local optmal and speeds up the convergence speed. After mprovng the cross mechansm proposed by Lovbjerg and others, t s used n the soluton of MOPSO. By usng the optmal preservaton strategy, the ndvduals wth better ftness are retaned n the next generaton populaton at each operaton. The key step of partcle swarm optmzaton based on crossover s to classfy all the partcles n the populaton and calculate the crowdng dstance of each partcle. The populaton s dvded nto two subgroups. Partcles closer to the Pareto front and wth a greater crowded dstance are called partcles wth better ftness values. The next half partcles are selected drectly to the next generaton, and the latter half of the partcles are selected as the poston of the random cross partcles. In other words, the correspondng process parameters n the process parameter scheme represented by two partcles are nterchanged to execute the cross partcle generaton. The cross between the parent partcles and the progeny partcles s compared. Half of the partcles wth hgh ftness value enter the next generaton. The overall algorthm flowchart s shown n fgure 3. 80

6 Start Cross operaton Intalze Partcle Sze, X, and V by the expermental range Non-domnated solutons were graded and the crowdng dstance of each partcle was calculated Meet the mllng constrants N Dvde the partcles nto two subgroups accordng to ther ftness Intalze Pareto set records P, Pg The last half of the partcles perform a cross operaton Update X, V Generate crossover-based partcle populatons Meet the mllng constrants N N Rebuld Pareto Set Update P, Pg Y Tmax s satsfed Output the optmal Pareto soluton set Fgure 3. Flow chart of the MOPSO algorthm based on cross operaton 3 RESULTS AND DISCUSSION 3.1 Results and analyss of energy effcency optmzaton of NC processng parameters based on expermental data The basc parameters are adjusted as follows: The value of learnng factors c 1 and c 2 s 1. The values of the nerta weght ω max and ω mn are 0.2 and 0.6. The number of populaton s 80. The number of teratons s 100. The speed Vmax and Vmn are 1.5 and -1.5, respectvely. The Matlab programmng s used to solve the model. Fgure 4 shows the convergence of the algorthm when t reaches the 70th generaton. The sold lne part s the convergent fronter of the mproved algorthm proposed n ths paper. The dotted lne s the convergent fronter of the standard partcle swarm optmzaton algorthm. It can be seen from the graph that the convergence speed of the optmzaton algorthm s faster and the convergence edge s closer to the real Pareto fronter after the mproved strategy. 81

7 SEC(J/mm 3 ) Standard MOPSO Modfed MOPSO Processng tme (s) Fgure 4. The convergence of algorthm The mult-objectve partcle swarm optmzaton and the sngle target partcle swarm optmzaton are used to solve the optmzaton model. There are 6 knds of optmzaton schemes: SECdr+ndr&Tp SEC dr+ndr &T p. Scheme 1: A mult-objectve optmzaton scheme wth SEC dr and processng tme as the optmzaton target; Scheme 2: A mult-objectve optmzaton scheme wth SEC dr+ndr and processng tme as the optmzaton target; Scheme 3: A sngle objectve optmzaton scheme wth SEC dr as the optmzaton target; Scheme 4: A sngle objectve optmzaton scheme wth SEC dr+ndr as the optmzaton target; Scheme 5: A sngle objectve optmzaton scheme wth the processng tme as the optmzaton target; Scheme 6: The sklled operator gves the experence process parameters on the spot. 3.2 Optmzaton results of mllng process parameters The results of the sx process parameters are summarzed n table 5. Table 5. The optmzaton results for mllng parameters Types N(r/mn) f z (mm/t) a p (mm) a e (mm) T p (s) SEC dr SEC dr+ndr Scheme Scheme Scheme Scheme Scheme Scheme Comparng mllng optmzaton scheme one and scheme two, t shows that when SEC dr and processng tme are selected as optmzaton objectves, although the SEC dr value s 0.7% hgher than the SEC dr that s optmzed alone, the tme value s reduced by 8.7%. Smlarly, the frst scheme s compared wth the ffth scheme, though the tme value of NC mllng process parameter s 8.1% hgher than sngle optmzaton processng tme, the SEC dr value s reduced by 14.1%. The problems that the optmzed technologcal parameters of the SECdr target are hgher, the tool lfe s shorter and the tool shared n a certan process has more energy are consdered. As a result, 82

8 the thrd scheme s 8.2% hgher than the SEC dr+ndr n frst scheme. The techncal parameters formulated by the operator are more conservatve. Because the value of the process parameters s small, the energy consumpton of the machne tool s ncreased durng the cuttng perod. Therefore, both tme and specfc energy are hgher. Compared wth the experence parameter scheme, the processng tme value of the optmzaton scheme one s reduced by 13.9% and the SEC dr value s reduced by 19.4%. Compared wth the scheme two and scheme four n mllng optmzaton, t shows that when SEC dr+ndr and processng tme are selected as the optmzaton objectves, although the SEC dr+ndr value s 6.4% hgher than the SEC dr+ndr that s optmzed alone, the tme value s reduced by 10.4%. The comparson between scheme two and scheme fve shows that the tme value s 2.4% hgher than the NC mllng process parameter optmzed by sngle processng tme, but the SEC dr+ndr value s reduced by 25.2%. after comparng the scheme one and scheme two, t shows that the rato of the ndrect energy consumpton can be nfluenced by the nternal energy of the tool, and the process parameter s smaller than the optmzaton scheme one. However, compared to the values of the two specfc energy, although the SEC dr of the scheme two s 9.1% hgher than the scheme one, the SEC dr+ndr value s reduced by 15.7%. Therefore, the energy effcency mult-objectve optmzaton, consderng the ndrect energy consumpton, can greatly reduce the generaton of ndrect energy consumpton n the processng process. Therefore, the energy effcency of the NC machnng system has acheved the effect of energy savng and emsson reducton. 3.3 Optmzaton results of processng parameters n turnng The results of the sx process parameters are summarzed n table 6. Table 6. The optmzaton resultsfor turnng process Types v c (m/mn) f(mm/r) a p (mm) T p (s) SEC dr SEC dr+ndr Scheme Scheme Scheme Scheme Scheme Scheme The optmzed combnaton result of turnng and mllng at each target shows that the results of turnng and mllng are bascally the same. Compared wth the scheme one and scheme three, when optmzng the processng parameters of SEC dr and processng tme, the SEC dr value s 13.2% hgher than SEC dr that s optmzed alone, but the tme value s reduced by 14.1%. Smlarly, compared scheme one wth scheme fve, the processng tme value of the scheme one s 8% hgher than the scheme fve, but the SEC dr value s reduced by 13.5%. Compared wth turnng scheme two and scheme four, when SSEC dr+ndr and processng tme are taken as optmzaton objectves, the SEC dr+ndr value s 10.6% hgher than SEC dr+ndr that s optmzed alone, but the tme value s reduced by 13.4%. In scheme two, compared wth the scheme fve wth the sngle processng tme, although the tme value s ncreased by 3.9%, the SEC dr+ndr s reduced by 32.3%. Because the target functon of scheme two consders the nternal energy consumpton of the materal, the selecton of the process parameters s lower than the scheme one. At ths tme, MRR s located n the regon two, and the processng tme ncreases wth the ncrease of MRR. Although the SEC dr of scheme two s 7.1% hgher than the scheme one, both tme and SEC dr+ndr have been reduced. The tme s reduced by 3.8%, and SEC dr+ndr s reduced by 21.8%. When the processng tme requrement s not severe, the energy effcency and mult-objectve optmzaton plan consderng ndrect energy consumpton s better than energy effcency and mult-objectve optmzaton wthout consderng drect energy consumpton. Compared wth the experence parameter scheme, the processng tme value of the optmzaton scheme s reduced by 22.3% and the SEC dr+ndr value s reduced by 42.3%. The optmzed results of mllng processng show that the proposed method can balance the two target. Thus, the optmzaton results are 83

9 tme(s) tme(s) coordnated between tme and energy effcency targets. The choce of mult-objectve energy effcency optmzaton s superor to the sngle pursut of the hghest energy effcency or the lowest tme n the cuttng process. In addton, from the energy loss of the NC system, the multobjectve optmzaton process of ndrect energy effcency s consdered Because of the energy consumpton n the process of drect and ndrect energy consumpton, the effect of energy effcency and emsson reducton s mproved compared wth the result of energy effcency mult-objectve optmzaton whch consders the drect energy consumpton. In order to further explan the optmzaton results, the test data n the case are drawn and the trend lnes are added. The tme analyss of the NC machnng process s shown n fgures 5 and fgure 6. The analyss of energy consumpton s shown n fgure Cuttng tme Tool change tme Total tme Empty cuttng tme MRR(mm 3 /mn) Fgure 5. Tme as a functon of MRR for mllng case Tool change tme Empty cuttng tme Cuttng tme Total tme MRR(mm 3 /mn) Fgure 6. Tme as a functon of MRR for turnng case 84

10 Energy consumpton(j) Fgure 5 and fgure 6 show the changng trend of the total tme, cuttng tme, knfe change tme and space cuttng tme wth MRR n NC mllng and NC turnng. The comparson trend lne shows that the changng rule of tme wth MRR s more consstent whether t s NC mllng or NC turnng. The tme of each part s analyzed one by one: Accordng to the graph, the proporton of ar cuttng tme to total tme s the smallest, and the ar cuttng tme of NC mllng decreases wth the ncrease of MRR. The ar cuttng process conssts of two parts. One s the tme from cuttng pont to the edge of the workpece, and the other s the cuttng tme that the tool exceeds the workpece contour durng the cuttng process. In order to mprove the processng effcency, the operator avods the larger ar cuttng path as far as possble when desgnng the tool path. Therefore, the ar cuttng tme s very short. The total processng tme s manly nfluenced by the cuttng tme and the changng tme. It can be seen that when the tool path length s fxed, the cuttng tme decreases wth the ncrease of MRR. However, the changng tme s manly controlled by the tool lfe. When the MRR s larger, the tool wear s aggravated and the tool lfe s shorter. At ths tme, the changng tme s the man factor affectng the processng tme. Therefore, the total tme s gradually rsng after the B pont. Comparng wth the above fgure, t shows that NC cuttng has larger cuttng rate, hgher tool wear and shorter tool lfe than NC mllng, and the trend of total tme s more obvous SEC (dr + ndr) Total energy consumpton (dr + ndr) Cuttng tme energy consumpton SEC (dr) Tool ncluded energy Tool change perod energy consumpton Empty cuttng energy consumpton C 200 D 400 E MRR(mm 3 /mn) Fgure 7. SEC as a functon of MRR for mllng case Fgure 7 shows the trend of total processng energy consumpton, cuttng energy consumpton, knfe exchange energy consumpton and no-load energy consumpton, and the ncrease of tool nternal energy wth the ncrease of materal removal rate. When the materal removal rate s small, the energy consumpton decreases wth the ncrease of the materal removal rate. However, wth the ncrease of MRR, the trend gradually weakens untl t becomes constant. Therefore, the total processng energy consumpton s manly nfluenced by the cuttng energy consumpton, the energy consumpton of the cutter and the nternal energy of the tool. From the pont C to pont D, the cuttng energy consumpton decreases sgnfcantly wth the ncrease of the cuttng tme because of the sharp cuttng tme. Although the energy consumpton of the cutter and the nternal energy of the tool have a certan rsng trend, the total energy consumpton s manly controlled by the cuttng energy consumpton. After t s over D pont, as the MRR contnues to rse, the decreasng trend of cuttng energy consumpton becomes slow. In ths area, although the MRR ncreases, the cuttng tme 85

11 decreases. The ncrease of the cuttng area leads to the larger deformaton force and frcton force, and the cuttng force ncreases. Therefore, the cuttng energy consumpton s not as severe as before. From pont D to pont E, the tool wear and the tool changng tme ncrease because of the hgher MRR. Therefore, the energy consumpton of the cutter ncreases exponentally. In addton, the sharp ncrease n the nternal energy consumpton of the tool makes the total energy consumpton ncrease obvously. Although the total energy consumpton ncreases wth MRR durng ths perod, SEC dr and SEC dr+ndr are stll declnng. When MRR ncreases to pont E, SEC dr+ndr s down to the lowest. Snce then, the nfluence of the SEC dr+ndr on the nternal energy of the tool has an obvous upward trend. 3.4 Sgnal to nose rato analyss and range analyss The dfference analyss n the specfc energy of the NC turnng s shown n table 7. Table 7. Range analyss for SEC n CNC turnng process Level v c (r/mn) f(mm/r) a p (mm) SEC dr SEC dr+ndr SEC dr SEC dr+nd SEC dr SEC dr+ndr Extremely poor Rankng r As shown n the table, n the processng condtons, the parameter effect of SEC dr CNC turnng process s the cuttng depth, feed rate and cuttng speed accordng to the effect of the sze of the order. The process parameters of SEC dr+ndr NC turnng process s cuttng speed, cuttng depth and feed rate accordng to the effect of the sze of the order. Among them, the feed rate has the greatest effect on the tme target n the process of CNC turnng, followed by the cuttng speed and cuttng depth. Therefore, when the optmzaton target s SEC dr and the processng tme, the larger feed rate and medum cuttng speed can be selected to control the cuttng depth. When the optmzaton target s SEC dr+ndr and the processng tme, the larger feed rate and moderate-to-low cuttng speed can be selected to control the cuttng depth. 4 CONCLUSION In the study, the man factors that affect the selecton of process parameters are analyzed. Then, the energy effcency optmzaton method of NC machnng process parameters based on the expermental data, the expermental condtons of energy savng optmzaton based on Taguch method, and the mult-objectve optmzaton model of NC machnng process parameters and the soluton model of mult-objectve partcle swarm optmzaton algorthm based on crossover method are proposed. After ntroducng the related model method, the results of the energy effcency optmzaton of the numercal control process parameters are analyzed. The optmzaton results of the mllng process parameters, 86 the optmzaton results of the turnng process parameters and the SNR analyss and range analyss are dscussed. The results show that the proposed optmzaton method has a certan valdty and feasblty and can provde a certan theoretcal method for mprovng the energy effcency of CNC machne tools. 5 REFERENCES L, C., Tang, Y., Cu, L., L, P. (2015). A quanttatve approach to analyze carbon emssons of CNC-based machnng systems. Journal of Intellgent Manufacturng, 26(5), Pare, V., Agnhotr, G., Krshna, C. (2015). Selecton of optmum process parameters n hgh speed CNC end-mllng of composte materals usng meta heurstc technques a comparatve study. Strojnšk vestnk-journal of Mechancal Engneerng, 61(3), Sahoo, P., Pratap, A., Bandyopadhyay, A. (2017). Modelng and optmzaton of surface roughness and tool vbraton n CNC turnng of Alumnum alloy usng hybrd RSM-WPCA methodology. Internatonal Journal of Industral Engneerng Computatons, 8(3), Yıldız, B. S., Yıldız, A. R. (2017). Moth-flame optmzaton algorthm to determne optmal machnng parameters n manufacturng processes. Materals Testng, 59(5), Gulat, V., Aryal, A., Katyal, P., Goswam, A. (2016). Process parameters optmzaton n sngle pont ncremental formng. Journal of The

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