Machining Quality Predictions: Comparative Analysis of Neural Network and Fuzzy Logic

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1 Internatonal Journal of Electrcal & Computer Scences IJECS-IJENS Vol:09 No:09 60 Machnng Qualty Predctons: Comparatve Analyss of Neural Network and Fuzzy Logc Svarao, Castllo and Taufk Abstract Surface fnsh s an mportant objectve functon n manufacturng engneerng. It holds the characterstc that could nfluence the performance of mechancal parts whch s also proportonal to producton cost. It s also an aspect for desgnng mechancal elements and frequently presented as a qualty and precson ndcator of manufacturng processes. Varous falures, sometmes catastrophc leadng to hgh cost have been attrbuted to the surface fnsh of the components whch left unanswered. Therefore, the qualty of surface roughness s essental feature of drllng operaton snce most of hole applcatons are assembly works, especally focused on the relatve movement and tght tolerance work. Hence, hgh standard qualty control needs to be ntroduced. The am of ths expermental and analytcal research s to dentfy the parameters whch enable the predcton of surface roughness n drllng. Two expert systems were used to analyze the best ft model n predctng the output of surface roughness for ths specfc drll job. The predcton accuracy s then compared to analyze whch model could gve better results so that t can be recommended for machne learnng and future work. From the fndngs, t s found that Sugeno Fuzzy model gves better the closest values as compared to the ANN model. Thus, the work condtons and Fuzzy envronment s selected for predctons of surface roughness n drllng. Index Term Sugeno Fuzzy, deep drllng, neural network, fuzzy logc, surface fnsh. I. INTRODUCTION In the nvestgaton of surface fnsh, mcro geometry of a worked part s consdered essental and crtcal. Optmum selecton of process condtons s extremely mportant as ths determnes the surface qualty of manufactured parts. Thus, n materal removal processes, mproper selecton of cuttng condtons cause surfaces wth hgh roughness. Manufacturng processes do not allow achevng the theoretcal surface roughness due to defects appearng on machned surfaces and manly generated by defcences and mbalances n the process. Due to these aspects, measurng procedures are necessary; as t permts one to establsh the real state of surfaces to manufacture parts wth hgher accuracy. To know the drlled surface qualty, t s necessary to employ theoretcal models makng t feasble to do predctons n functon of response parameters. The measurement and stochastc modelng of torque and thrust force n drllng has been the man nterest of many Ir. Svarao s a Professonal Engneer (P.Eng.) n the feld of Mechancal Engneerng who currently serves as a lecturer and researcher n the Faculty of Manufacturng engneerng, Unverst Teknkal Malaysa Melaka (UTeM). He s the correspondng author. (phone: , Fax: & emal: svarao@utem.edu.my or kpe_sva@yahoo.com). Wllan Jefferson Gonzalez Castllo s a research fellow n Federal Unversty of Santa Catarna, Mechancal Engneerng Department, Brasl. He specalzes n machnng, especally deep drllng of cast rons (emal castlo@lmp.ufsc.br) Taufk s a lecturer and researcher at Faculty of Manufacturng Engneerng, specalzng n qualty evaluaton of machned parts (emal: taufk@utem.edu.my). researchers as t was the best method of ndrect tool wear (flank wear) sensng [1]. The resurgence of nterest n Expert System over the past few decades has opened many new avenues n ts applcatons. Expert System leads to greater generalty and better rapport wth realty. It s drven by the need for methods of analyss and desgn, whch can come to grps wth the pervasve mprecson of the real world and explot the tolerance for mprecson to acheve tractablty, robustness and low cost soluton [2]. The use of Neural Network n machnng research has been extensve and multfaceted as well. These networks can be traned to recognze arbtrary relatons between sets of nput and output pars by adjustng weghts of the nterconnectons. Back propagaton Neural Network s most commonly used n related manufacturng research and Neural Network has been used extensvely n the past decade to montor the progress of tool condton montorng. Fuzzy modelng s based on the dea to fnd a set of local nput-output relatons descrbng a process. So, the method of Fuzzy modelng can express a non-lnear process better that any ordnary method. As more knowledge about the system s accumulated the uncertanty dmnshes the need for the Fuzzy Logc treatment and t can revert to a determnstc or statstcal one. The am of ths expermental and analytcal work s to dentfy sutable parameters, the montorng of whch enable the predcton of surface fnsh for drlled holes by two Expert Systems namely Sugeno-Fuzzy and Neural Network. Both have ther own ablty n determnng the output and decson makng n tool condton montorng whch determnes and mantan the qualty of drlled surface to ts hghest standard of the end product. Fnally, the best Expert System s to be recommended for ths specfc drll job as per ther lmtatons and advantageous upon carryng out the comparatve analyss among the expert systems wthn the range of expermental values. II. EXPERIMENTAL PROCEDURE Ths task nvolves drllng of 30 through holes per work pece wth 10 mm twst drlls onto EN31 Mld Steel dmensonng 16 cm X 16 cm 2.5 cm by usng pllar drllng machne. Total of eght work peces worked resultng 240 drlled holes n total. For the purpose of ths analyss, four out of eght experments were taken nto consderaton (only dry condtons) and the results of hole number 10, 20 and 30 of each experment has been dsplayed n the paper. Tool wears and s susceptble to breakage as holes are drlled. It s assumed that there s a lmt to the wear beyond whch the tool s unacceptable as t drectly affects the nternal surface roughness of the drlled hole. The spndle speed, feed and other machnng condtons for the job are as shown n table I.

2 Internatonal Journal of Electrcal & Computer Scences IJECS-IJENS Vol:09 No:09 61 T ABLE I MACHINING CONDITION Process Parameters Cuttng speed & m/mn Feed 0.2 & mm/rev Number of holes 30 per work pece Materal / Dameter Pont angle Helx angle Clearance angle Web thckness Hardness Eght experments were carred out based on 2 k factoral desgn wth 10 mm twst drll n dry and wet cuttng condtons to test the stablty of the tool wear whch drectly affects the nternal surface of drlled holes. The 2 ndcates level (hgh and low), where k ndcates three factors (speed, feed and cuttng flud). Thrust force and torque were measured usng mechancal drll dynamometer wth ndependent dgtal output. The nternal surface roughness of drlled holes was measured wth Mtutoyo Surf Test SJ-400 wth 800 mcrons of measurng resolutons. The surface roughness of the drlled holes were measured at three ponts (top, bottom and mddle) along a straght lne. The recorded surface roughness values were the average of three readngs taken whle measurng. Ths s a norm of measurng drlled holes and known as Straght Lne Average (SLA) method. III. EXPERTT SYSTEMS A. Artfcal Neural Work Recent research actvtes n artfcal neural networks (ANNs) have shown that ANNs have powerful pattern classfcaton and pattern recognton capabltes. ANNs are well suted for problems whose solutons requre knowledge that s dffcult to specfy but for whch there are enough data or observatons. They learn from examples (tranng data) and capture subtle functonal relatonshps among the data even f the underlyng relatonshps are unknown or hard to descrbe. ANNs are unversal functonal approxmators [3]. It has been shown that a network can approxmate any contnuous functon to any desred accuracy by many researchers [4], [5]. Neural network whch uses back propagaton algorthms for modelng has been developed usng machnng process parameters; speed and feed as nputs and surface roughness as output. Makng connectons from the nput layer to the output layer mproves the learnng effcency. The complete expermental data of 240 drlled holes were used to tran the network. The learnng rate used was 0.02, and no s moothng factor was used. The ntal weghts were assgned randomly from 0.1 to 0.6. The learnng process was stopped after 10,000 teratons. The number of neurons n the hdden layer were selected from 1, 2,.., 20 for 6x20x1 ANN structure [6], [7], [8]. The frst step of the calculaton s to normalze all the raw nput data to values between 0.2 and 0.6 as shown n the equaton (1). 0.4 x ( d dmn ) 0.2 d d (1) max Tool Specfcaton mn HSS 10 mm 118 o 32 o 10 o 1.2 mm 57 Rc The d max and d mn are the maxmum and mnmum nputs and d s th nput. Input of th neuron on hdden layer I y, calculated by I M w x (2) y xy 1 M s number of neurons n nput layer and w xy s numercal weght value of the connecton between the two neurons. x s th normalzed output from the nput layer. The output of the th neuron on hdden layer y s to be calculated by applyng an actvaton functon to the summed nput of that neuron. The output of th neuron on hdden layer then appear as, 1 y f ( I ) (3) y s( Iy ) 1 e The s s the slope of the sgmod functon and the values receved by the output layer I are outputs of the hdden and z nput layers. M Iz w x w y N (4) xz yz 1 1 M and N are the numbers of neurons n the nput and hdden layers. w and w are correspondng weghts from the xz yz nput to the output layer and from hdden layer to output layer. The actual output n the output layer s calculated by applyng the same sgmod functon as appled for hdden layer. z f ( Iz ) (5) Error between the desred and actual output n the output layer s gven by f ' ( I )( T Z ) (6) Where, z z T s the th tranng nput to the neuron and ' f s the dervatve of the sgmod functon. For each neuron on the hdden layer, the error, s y y y z yz 1 L f ( I ) w (7) The L s number of neurons n the output layer. Matlab 7.0 was used to run the ANN n ths fndng. The 240 expermental tranng data wth condtons has been used to tran the neural network. The predcton of surface roughness numercal by Neural Network s shown n table II. B. Fuzzy System Fuzzy logc has a lot of applcatons n the real world. Bascally the system wll accept the nput or some nputs and then pass the nputs to a process called fuzzfcaton. In the fuzzfcaton process, the nput data (can be dgtal, precse/mprecse) wll undergo some translaton nto lngustc quantty such as low, medum, hgh of physcal propertes. The translated data wll be sent to an nference mechans m that wll apply the predefned rules. The nference mechans m wll generate the output n lngustc form. The lngustc output wll go through defuzzfcaton process to be n numercal fo rm (the normal data form). Defuzzfcaton s defned as the converson of a fuzzy quantty represented by a membershp functon to precse or

As far as modelng and approxmaton s concerned, one can say that the man nterest s towards ts applcatons.

3 Internatonal Journal of Electrcal & Computer Scences IJECS-IJENS Vol:09 No:09 62 crsp quantty [9], [10]. Fuzzy modelng and approxmaton are the most nterestng felds where fuzzy theory can be effectvely appled. As far as modelng and approxmaton s concerned, one can say that the man nterest s towards ts applcatons. When we ntend to apply fuzzy modelng and approxmaton to an ndustral process, one of the key problems to be solved s to fnd fuzzy rules. SUGENO TYPE FUZZY INFERENCE The most commonly known or used fuzzy nference methodology s Mamdan. But, ths paper dscuss the socalled Sugeno, or Takag-Sugeno-Kang, method of fuzzy nference. The man dfference between Ma mdan and Sugeno s that the Sugeno output membershp functons are ether lnear or constant but can be excellently suted for modelng nonlnear systems by nterpolatng between multple lnear models. A typcal rule n a Sugeno fuzzy model has the form; If Input 1 = x, Input 2 = y, then output s z = ax + by + c For a zero-order Sugeno model, The output level z s a constant, (a=b =0). The output level z of each rule s weghted by the frng strength w of the rule. For example, for an AND rule wth Input 1 = x and Input 2 = y, the frng strength s w = And Method [ F 1 (x), F 2 (y) ] where F 1, F 2 are the membershp functons for Inputs 1 and 2. The fnal output of the system s the weghted average of all rule outputs, computed as shown by equaton (8) and the Sugeno rule operates as shown n Fg. 1. s shown n Fg. 2, where, L,M & H denotes Low, Medum and Hgh respectvely. Fg. 2. Membershp functon of Sugeno-Fuzzy Upon developng the membershp functon, precse rules have been fed nto the system relatng the FIS nput-output varables. Each of these rules plays an mportant role n generatng the fuzzy logc controller model and the accuracy of the numercal output. Few rules fed to the FIS based on theoretcal study and experence to obtan the hgher accuracy output. The rules fed are shown n Fg. 3. The L, M and H denotes low, medum and hgh range of nput parameters whch determnes the qualty of surface output whether they are fne, moderate and coarse. (8) Fg. 3. Seven rules whch appled to Sugeno fuzzy system Upon the rules determnaton, the fuzzy logc controller wll smulate the FIS varables wth respectve fed rules and modelng of the controller tool box wll take place. The model controller toolbox to the system s shown n Fg.5 and the complete structure of the Sugeno-Fuzzy predctve model s shown n Fg. 5. Fg. 1. Sugeno fnal output rule Due to the lnear dependence of each rule on the nput varables of the system, Sugeno method s deal for actng as an nterpolatng supervsor of multple lnear controllers that are to be appled, respectvely, to dfferent operatng condtons of a dynamc nonlnear system. A Sugeno fuzzy nference system s extremely well suted to the task of smoothly nterpolatng the lnear gans that would be appled across the nput space; t s a natural and effcent gan scheduler. Smlarly, a Sugeno system s suted for modelng nonlnear systems by nterpolatng between multple lnear models [10]. The plot of membershp functon and nputoutput varables fed nto the Fuzzy Inference System (FIS) Fg. 4. Controller tool box for each rule Fg. 5. Fuzzy logc controller tool box of the model

Internatonal Journal of Electrcal & Computer Scences IJECS-IJENS Vol:09 No:09 63 IV. RESULT AND DISCUSSIONS Fg.

6 wth the output (surface roughness) plotted aganst thrust force and torque, ndcates that at begnnng stage of drllng there was runnng n acton n the operaton and gradually ncreases.

After 23 rd hole, the surface roughness ncreases rapdly agan showng that the tool starts to wear and t gets severe as t reaches the 30 th hole whch may lead to bad surface roughness or tool breakage.

7. The numercal output of every 10 th hole for all the dry cuttng of observed and expert system analyss s shown n table II.

Sugeno-Fuzzy surface model (speed and feed) Fg. 8. Worked samples and drll tools used Fg. 9. Actual and expert system comparatve output Fg.

4 Internatonal Journal of Electrcal & Computer Scences IJECS-IJENS Vol:09 No:09 63 IV. RESULT AND DISCUSSIONS Fg. 6 and 7 are the Sugeno-Fuzzy based surface model showng an excellent relatonshp between the two sets of nput varables; speed & feed and thrust force & torque. Fg. 6 wth the output (surface roughness) plotted aganst thrust force and torque, ndcates that at begnnng stage of drllng there was runnng n acton n the operaton and gradually ncreases. In later stages, the surface roughness has shown steady condton tll reachng 20th hole. After 23 rd hole, the surface roughness ncreases rapdly agan showng that the tool starts to wear and t gets severe as t reaches the 30 th hole whch may lead to bad surface roughness or tool breakage. Increase of speed wth low & medum feed rate produces good surface fnsh. Whereas, low & medum speed wth hgh feed rate produces bad surface fnsh of the drlled holes. Ths phenomenon s clearly shown n Fg. 7. The numercal output of every 10 th hole for all the dry cuttng of observed and expert system analyss s shown n table II. The four worked samples drlled wth 30 holes wth ther respectve 10 mm drll tools s shown n Fg. 8. T ABLE II Numercal output - result Fg. 6. Sugeno-Fuzzy surface model (thrust force & torque) Fg. 7. Sugeno-Fuzzy surface model (speed and feed) Fg. 8. Worked samples and drll tools used Fg. 9. Actual and expert system comparatve output Fg. 9 shows the comparatve output of the surface roughness by the expert systems aganst the observed values. From the numercal and also comparatve hstograms, t s clear that, Sugeno-Fuzzy system has produced closer output as compared to ANN observed values. It has been studed that, Fuzzy has the ablty of predctng the future (forecastng) based on the membershp functon of the nput and output varables, lmts and rules fed. For 6x20x1 ANN structure, the tranng has also generated closer outputs as compared to the observed values. Anyway, ANN s correlaton was not consstent as compared to Fuzzy. Although ts values are not the best, but t also matches closely to the actual values due to ts tranng capablty. CONCLUSIONS Sugeno-Fuzzy has shown the capablty of generalzaton and predcton of surface roughness n drllng wthn the range of expermental data. The maxmum devaton observed and estmated by Fuzzy s very mnmal and wthn the recommended regons of surface roughness analyss n drllng. The present work can be extended wth dfferent dameter of drll tools, process parameters, materal thckness and type to test the ablty of the expert systems n predcton of the output whch the fndngs can be appled for ndrect tool condton montorng n unmanned manufacturng system. The ntutons and experences of a sklled machnst can be replaced by a set of fuzzy rules for non crtcal areas of machnng as t has the forecastng ablty mprovement.

5 Internatonal Journal of Electrcal & Computer Scences IJECS-IJENS Vol:09 No:09 64 The predcted values of ANN output can be further precsely predcted wth the change of structure and the weghts to the system by ncreasng the number of experments. REFERENCES [1] Rot Berg and Ber, Methods for drllng parameters evaluaton appled for drll pont development, Annuals of CIRP, vol. 36, pp , [2] Matsumura, T. and Obkawa, T., On the development of expert system for selectng the optmum cuttng, Journal of the Japan Socety of Precson Engneerng, 58, pp , [3] Kuo, R. J., Intellgent tool wear system through artfcal neural networks and fuzzy modelng, Journal of Artfcal Intellgence n Engneerng, 5, pp , [4] Choudhury, S.K. and Jan. V.K., Onlne montorng of tool wear n turnng usng a neural network, Internatonal Journal of Machne tools and Manufacturng, 39, pp , [5] Rahaman, M., and Zhou, Q., Onlne cuttng state recognton usng a neural network, Internatonal Journal of Advanced Manufacturng technology, vol. 2, pp , [6] Eshma, T. and Shbasaka, Estmaton of cuttng tool lfe by processng tool mage data wth neural network, CIRP Annuals, vol. 42, pp , [7] Masory, Montorng machnng processes usng multsensor readngs fused by artfcal neural networks, 7th Internatonal Conference on Computer aded Producton Engneerng, vol. 28, pp , [8] Rangwala, S., and. Dornfeld, Sensor ntegraton usng neural networks for ntellgent tool condton montorng, ASME Journal of Engneerng and Industry, 112, pp , [9] L P.G. and S.M.Wu, Montorng-drllng wear states by a Fuzzy pattern recognton technque, Journal of Engneerng for Industry, 110, pp , [10] Nguyen. T, Nadpuram Prasad., Fuzzy Modelng and Control - Selected Works of M.Sageno, CRC Press, New York, pp , BIOGRAPHIES Ir. Svarao s a professonal engneer n the feld of mechancal engneerng and currently he serves Unverst Teknkal Malaysa Melaka as a researcher n the feld of manufacturng engneerng, specalzng n precson machnng and artfcal ntellgence. He has publshed hs fndngs n more than 60 reputated nternatonal journals and conference proceedngs. He s also an actve revewer for Journal of Engneerng Manufacturer (UK), Journal of Mechancal Engneerng Scence (UK), IJMPT (specal ssue) and JEEER together wth few Internatonal Conferences. To date he has been awarded few research grants totalng up to 200K and he has a product patented and commercalzed. He also won eght medals n varous nnovatve product desgn compettons ncludng the one held n Geneva n year He s also an actve member of few professonal assocatons ncludng Board of Engneers Malaysa, Academy of Malaysan SMEs, The Insttute of Engneers Malaysa, Malaysan Inventon and Desgn Socety and Internatonal Assocaton of Engneers, UK. Wllan Jefferson Gonzalez Castllo s a research fellow n Federal Unversty of Santa Catarna, Mechancal Engneerng Department, Brasl. He specalzes n machnng, especally deep drllng of cast metals. He has publshed 10ths of nternatonal journals and about 50 conference proceedngs. He s an actve researcher who solves many ndustry problems related to cuttng tools and also qualty evaluatons. Taufk s an expatrate from Indonesan who currently serves Faculty of Manufacturng Engneerng, Unverst Teknkal Malaysa Melaka (UTeM).

Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network

Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network Internatonal Journal of Engneerng & Technology IJET-IJENS Vol:09 No:09 24 Modelng, Testng and Expermental Valdaton of Laser Machnng Mcro Qualty Response by Artfcal Neural Network Svarao, Peter Brevern,