Zsolt J. VIHAROS*, László MONOSTORI*, Sándor MARKOS**

Transcription

1 Key words: Modelling nd monitoring o technicl processes, Mchine lerning nd serch techniues Zsolt J. VIHAROS*, László MONOSTORI*, Sándor MARKOS** *: Computer nd Automtion Reserch Institute, Hungrin Acdemy o Sciences Kende u. -7, H-, Budpest, Hungry **: Deprtment o Mnucturing Technology, Technicl Uniersity, Budpest Műegyetem rkprt, H-, Budpest, Hungry A FRAMEWORK FOR MODELLING, MONITORING AND OPTIMISATION OF MANUFACTURING PROCESSES AND PROCESS CHAINS BY USING MACHINE LEARNING AND SEARCH ALGORITHMS The pper presents noel pproch or generting multipurpose models o mchining opertions combining mchine lerning nd serch techniues. These models re intended to be pplicble t dierent engineering nd mngement ssignments. Simulted nneling serch is used or inding the unknown prmeters o the models in gien situtions. It is expected tht the deeloped block-oriented rmework will be luble tool or modelling, monitoring nd optimistion o mnucturing processes nd process chins. The pplicbility o the proposed solution is illustrted by the results o experimentl runs.. INTRODUCTION Relible process models re extremely importnt in dierent ields o computer integrted mnucturing. Diiculties in modelling mnucturing processes re mniold: the gret number o dierent mchining opertions, multidimensionl, non-liner, stochstic nture o mchining, prtilly understood reltions between prmeters, lck o relible dt, etc. A number o resons bck the reuired models: design, optimistion, control, monitoring nd dignostics, simultion o processes, nd design o euipment []. Artiicil neurl networks (ANNs), neuro-uzzy (NF) systems re generl, multirible, non-liner estimtors, thereore, oer ery eectie process modelling pproch. Such sot computing techniues oer especilly ible solutions or the lower leel o intelligent, hierrchicl control nd monitoring systems where bilities or rel-time unctioning, uncertinty hndling, sensor integrtion, nd lerning re essentil etures []. Successul ttempts were reported on in the literture [], [8], [], [[], [], [5], []. The pper summrises the irst results o the reserch ctiity iming t inding multipurpose model or set o ssignments which cn stisy the rious ccurcy reuirements. A method or utomtic genertion o ANN-bsed process models is described which re expected to be pplicble in dierent ssignments. The ppliction phse o the multipurpose process model is lso detiled. A noel techniue bsed on simulted nneling serch is introduced to ind the unknown prmeters o the model in gien situtions. The extension o the pproch to modelling nd optimistion o process chins is lso ddressed.

2 . AUTOMATIC INPUT-OUTPUT CONFIGURATION AND GENERATION OF MULTIPURPOSE ANN-BASED PROCESS MODELS The utomtic genertion o pproprite ANN-bsed process models, i.e. models, which re expected to work with the reuired ccurcy in dierent ssignments, consists o the ollowing steps [7]: - Determining the (mximum) number o output prmeters (No) rom the ilble N prmeters which cn be estimted by using the remining Ni = N - No input prmeters within the prescribed ccurcy. - Ordering the ilble prmeters into input nd output prmeter sets hing Ni nd No elements, respectiely. - Trining the network whose input-output conigurtion hs been determined in the preceding steps. The boe steps re perormed prllel, using the speed o the lerning process s n indictor or the ppropriteness o the gien ANN rchitecture to relise the reuired mpping. In order to ccelerte the serch or the ANN conigurtion, which complies with the ccurcy reuirements with the minimum number o input prmeters, seuentil orwrd serch (SFS) lgorithm is used. The pplicbility o the pproch ws tested by rtiicil dt (e.g. or hndling noninertble dependencies), using dt deried rom nlyticl descriptions or set o engineering ssignments (dierent leels o plnning, optimistion nd control), nd by experimentl mchining [7].. OPTIMISATION OF MACHINING PROCESSES BY USING THE MULTIPURPOSE MODEL Usully, some prmeters re known, nd using the multipurpose model generted ccording to the preious prgrph, the tsk is to determine the other prmeters while stisying some constrints. Becuse o the generl nture o the multipurpose model, lmost in eery cse, prt o the input nd prt o the output ribles o the model re known by the user nd the unknown prt o the inputs is to be determined by tking the boe mentioned constrints into ccount. Here, simulted nneling serch techniue is proposed or the ppliction phse o the multipurpose model. The serch process is guided by the ccurcy reuirements o the estimtion or the known output prmeters while holding the unknown input nd output prmeter(s) within its (their) rnge o ppliction boundries. During the serch process the unknown input prmeters re to be determined nd t the sme time three conditions re to be stisied:. Condition regrding the known output prmeters. This condition ssures tht only tht points o the serch spce cn be ccepted s result, which cn deutely estimte the known output prmeters by using orwrd clcultion (Error, on Figure ).

3 . Boundry conditions regrding the unknown input prmeters. This condition is determined by the lidity o the ANN model. This lidity is usully speciied by the dt set used or the trining.. Condition regrding the unknown output prmeters. The third condition reltes lso to the lidity o the ANN. Vlues o the unknown input prmeters re only cceptble i the estimted lues o the unknown output prmeters re within their llowed rnge (Error, on Figure ). The serch lgorithm is terminted i ll o the three conditions boe re met. An error lue is ordered to ll isited points o the serch spce. In the deeloped lgorithm this lue is the mximum o Error nd Error presented boe. The lgorithm serches or the minimum error point. As result, this lgorithm gies one solution or gien ssignment o the user. To look or lrger number o solutions the serch hs to be repeted. T unknown κ ( ) estimted Error ( ) unknown κ r ε known r ε T F c P Fc P R Error estimted Fc P R known Figure : The generted ANN model nd its ppliction or the tsk presented in mchining [7] (control o the cutting process with mesured monitoring prmeters) There re dierent pproches to optimise gien process or process chin [9]. A block-oriented sotwre ws deeloped nmed ProcessMnger to optimise opertions nd/or production chins orm rious points o iew t the sme time. Multiple o objecties cn be hndled by the usul weighting techniue. The pplicbility o the progrm system is illustrted here through the optimistion o the plte turning ssignment. Figure nd Figure illustrte the ppliction o ProcessMnger or the threeold optimistion o the iewpoints o the customer (minimistion o the surce roughness), owner o the compny (proit/productiity mximistion) nd the employed engineer (mximistion o process stbility through the / rtio).

4 Chin building Externl Models Plte turning PROCESSMANAGER Intensity = Chip orm (/) Trgets o optimistion ( / ) = Externl connections Figure : Chin model or optimistion o the plte turning opertion with optimistion criteri / / / / / / / Figure : Prmeters resulted by the threeold optimistion o the plte turning opertion Figure shows the building up phse o ProcessMnger, where the model o the plte turning is relised by n ANN nd the other ribles to be optimised, e.g. cutting intensity nd / or stbility, re gien by eutions. Prmeters resulted by the optimistion o the plte turning opertion re illustrted by D-plots in Figure. Rtios o the weighting ctors o the three ribles to be optimised re represented long the xes. The surces re to be used together, i.e. the moing long the plne mrked by nd / occurs on ech o the digrms t the sme time. The corner mrked by indictes the position, where the iewpoint o the compny owner is the most importnt nd by moing long the xes nd / represents tht the iewpoints o the customer nd the engineer become more nd more importnt with respect to.. MODELLING AND OPTIMISATION OF PROCESS CHAINS

5 As it ws pointed out in [], it is not enough to concentrte on the inl tolernces usully deined by design. The inl tolernces re determined not only by the inishing opertions, but re the results o the initil tolernces o the workpieces nd the intermedite tolernces reched by the elements o the process chin resulting in the inished prt. The output o one opertion is the input o nother one or it is eture o the end product. The inl prt o the pper dels with the problem o modelling nd optimistion o process chins through the extension o the modelling nd serch techniues introduced or single processes. The ProcessMnger block-oriented rmework or modelling, monitoring nd optimistion o mnucturing processes nd process chins reerred boe incorportes (Figure ): - deinition o the elements o the chin, - determintion o the process models by integrting nlyticl eutions, expert knowledge nd exmple-bsed lerning, - connecting the single models into process chin by coupling input-output model prmeters not limited to models o successie processes in the chin, - deinition o eligible interls or limits or the process prmeters nd monitoring indices, - deinition o cost unction to be optimised, etc. Production chin Chin building Trgets o optimistion Elution, optimistion Opertion Opertion p Elution Elution N Prmeter n Prmeter n Prmeter n p Prmeter n n PROGRAM - PROCESSMANAGER Model Prmeter o Externl connections Model Prmeter o n Model m Prmeter o Prmeter n Externl Models Prmeter n n Model m n Prmeter o n Figure : Errors o prmeter estimtions long the whole production chin 5. CONCLUSIONS

6 The pper presented noel pproch or generting multipurpose models o mchining opertions which combines mchine lerning nd serch techniues. Simulted nneling serch ws used or inding the unknown prmeters o the multipurpose model in gien situtions. It is expected tht the deeloped ProcessMnger will be luble tool or modelling, monitoring nd optimistion o mnucturing processes nd process chins. Tking the globlistion issues nd the incresing role o irtul enterprises into ccount, the distributed ersion o the system will show up urther beneits. ACKNOWLEDGEMENTS This work ws prtilly supported by the Ntionl Reserch Foundtion, Hungry, Grnt Nos. F nd T8. A prt o the work ws coered by the Nt. Comm. or Techn. De., Hungry Grnts (EU-9-B-5 nd EU-97-A-99) promoting Hungrin reserch ctiity relted to the ESPRIT LTR Working Groups (IiMB 8 nd IMS 995). REFERENCES [] BARSCHDORFF, D., MONOSTORI, L., KOTTENSTEDE, T., WARNECKE, G., MÜLLER, M., 99, Wer estimtion nd stte clssiiction o cutting tools in turning i rtiicil neurl networks, Tooldig'9, Interntionl Conerence on Fult Dignosis, April 5-7, Toulouse, Frnce, pp [] CHRYSSOLOURIS, G., GUILLOT, M., DOMROESE, M., An pproch to intelligent mchining, Proc. o the 987 Americn Control Con., Minnepolis, MN, June -, pp. 5-. (987) [] MONOSTORI, L., A step towrds intelligent mnucturing: Modelling nd monitoring o mnucturing processes through rtiicil neurl networks, CIRP Annls,, No., pp (99) [] MONOSTORI, L., BARSCHDORFF, D., Artiicil neurl networks in intelligent mnucturing, Robotics nd Computer-Integrted Mnucturing, Vol. 9, No., Pergmon Press, pp. -7. (99) [5] MONOSTORI, L., Hybrid AI pproches or superision nd control o mnucturing processes, Key-note pper, Proc. o the AC'95, IV Int. Con. on Monitoring nd Automtion Superision in Mnucturing, Miedzeszyn, Polnd, Aug. 8-9, pp (995) [] MONOSTORI, L., MÁRKUS, A., VAN BRUSSEL, H., WESTKÄMPER, E., Mchine lerning pproches to mnucturing, CIRP Annls, Vol. 5, No., pp (99) [7] MONOSTORI, L., VIHAROS, ZS. J.: Multipurpose modelling nd optimistion o production processes nd process chins by combining mchine lerning nd serch techniues, Proceedings o The nd CIRP Interntionl Seminr on Mnucturing Systems, New Supporting Tools or Designing Products nd Production Systems, Leuen, Belgium, My -, 999, pp [8] RANGWALA, S.S., DORNFELD, D.A., Lerning nd optimistion o mchining opertions using computing bilities o neurl networks, IEEE Trns. on SMC, Vol. 9, No., Mrch/April, pp (989) [9] TÓTH, T., ERDÉLYI, F., The inner structure o computer ided process plnning hing regrd to concurrent engineering, nd Interntionl Workshop on Lerning in Intelligent Mnucturing Systems, April -, Budpest, Hungry, pp. -7. (995) [] VAN LUTTERVELT, C.A., CHILDS, T.H.C., JAWAHIR, I.S., KLOCKE, F., VENUVINOD, P.K., Present sitution nd uture trends in modelling o mchining opertions, CIRP Annls, Vol. 7, No., (998) [] WARNECKE, G., KLUGE, R., Control o tolernces in turning by predictie control with neurl networks, Proceedings o The Second World Congress on Intelligent Mnucturing Processes & Systems, June -, 997, Budpest, pp. -7. nd Journl o Intelligent Mnucturing, Vol. 9, No., August 998, Specil Issue on Sot Computing Approches to Mnucturing, Chpmn & Hll, pp [] WESTKÄMPER, E., Superision o ulity in process chins by mens o lerning process models, Proc. o the Second Int. Workshop on Lerning in IMSs, Budpest, Hungry, April -, pp (995)