JJMIE Jordan Journal of Mechanical and Industrial Engineering

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1 JJMIE Jordan Journal of Mechancal and Industral Engneerng Volume 3, Number 4, December 2009 ISSN Pages Predcton of Surface Roughness n Turnng Usng Adaptve Neuro-Fuzzy Inference System B. Sdda Reddy a, *, J. Suresh Kumar b, K. Vjaya Kumar Reddy b a Department of Mechancal Engneerng, R. G. M. Engneerng College, Nandyal A.P, Inda. b Department of Mechancal Engneerng, J.N.T.U. College of Engneerng, Hyderabad. A.P, Inda. Abstract Due to the extensve use of hghly automated machne tools n the ndustry, manufacturng requres relable models for the predcton of output performance of machnng processes. The predcton of surface roughness plays a very mportant role n the manufacturng ndustry. The present work deals wth the development of surface roughness predcton model for machnng of alumnum alloys, usng adaptve neuro-fuzzy nference system (ANFIS). The expermentaton has been carred out on CNC turnng machne wth carbde cuttng tool for machnng alumnum alloys coverng a wde range of machnng condtons. The ANFIS model has been developed n terms of machnng parameters for the predcton of surface roughness usng tran data. The Expermental valdaton runs were conducted for valdatng the model. To judge the accuracy and ablty of the model percentage devaton and average percentage devaton has been used. The Response Surface Methodology (RSM) s also appled to model the same data. The ANFIS results are compared wth the RSM results. Comparson results showed that the ANFIS results are superor to the RSM results. Keywords: Adaptve Neuro-Fuzzy; Surface Roughness Predcton; Turnng Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved. Introducton * The alumnum alloys are used n varous engneerng applcatons lke structural, cryogenc, food processng, ol and gas process ndustres etc. because of lght weght and hgh tensle strength. The qualty of the surface plays a very mportant role n the performance of the turnng as a good qualty turned surface sgnfcantly mproves fatgue strength, corroson resstance, or creep lfe. Surface roughness also affects several functonal attrbutes of parts, such as contact causng surface frcton, wearng, lght reflecton, heat transmsson, ablty of dstrbutng and holdng a lubrcant, load bearng capacty, coatng or resstng fatgue. Therefore the desred fnsh surface s usually specfed and the approprate processes are selected to reach the requred qualty []. To acheve the desred surface fnsh, a good predctve model s requred for stable machnng. The number of surface roughness predcton models avalable n lterature s very lmted [2]. Most surface predcton models are emprcal and are generally based on experments n the laboratory. In addton, t s very dffcult n practce, to keep all factors under control as requred to obtan reproducble results [3]. Taraman [4] used Response Surface Methodology for Predcton of surface roughness. Hasegawa et al., [5] conducted 3 4 factoral desgns to conduct experments for the surface roughness predcton model. They found that the surface roughness ncreased wth an ncrease n cuttng * Correspondng author. bsrrgmcet@gmal.com. speed. Sundaram and Lambert [6-7] consdered sx varables.e. speed, feed, depth of cut, tme of cut, nose radus and type of tool to montor surface roughness. Mtal and Mehta [8] conducted a survey of surface roughness predcton models developed and factors nfluencng surface roughness. They found that most of the surface roughness predcton models developed for steels. Generally these models have a complex relaton shp between surface roughness and operatonal parameters, work materals and chp breaker types. Salah Gasm Ahmed [9] developed an emprcal surface roughness model for commercal alumnum, based on metal cuttng results from factoral experments. The model ncludes the feed, depth of cut and spndle speed. Dlbag Sngh and P. Venkateswara Rao [0] conducted experments to determne the effects of cuttng condtons and tool geometry on the surface roughness n the fnsh hard turnng of the bearng steel (AISI 5200) usng mxed ceramc nserts made up of alumnum oxde and ttanum carbde wth dfferent nose radus and dfferent effectve rake angles as cuttng tools. They found that the feed s the most domnant factor determnng the surface fnsh followed by nose radus and cuttng velocty. L Zhanje [] used Radal Bass Functon network to predct surface roughness and compared wth measured value and the result from regresson analyss. Chen Lu and Jean-Phlppe Costes [2] consdered three varables.e., cuttng speed, depth of cut and feed rate to predct the surface profle n turnng process usng Radal Bass Functon (RBF). They found that the RBF networks have the advantage over Back Propagaton Networks (BPN). In the present work

2 Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved - Volume 3, Number 4 (ISSN ) the adaptve neuro-fuzzy model has been developed for the predcton of surface roughness. The predcted and measured values are farly close to each other. The developed model can be effectvely used to predct the surface roughness n the machnng of alumnum alloys wthn the ranges of varables studed. The ANFIS results are compared wth the RSM results. Comparson results showed that the ANFIS results are superor to the RSM results. 2. Alumnum Alloy Materal The work materal used for the present nvestgaton s alumnum alloy 6082 cylndrcal work peces. The chemcal composton and physcal propertes of the materal used n ths work s gven n Table and Table Adaptve Neuro Fuzzy Inference Method The fuzzy logc and fuzzy nference system (FIS) s an effectve technque for the dentfcaton and control of complex non-lnear systems. Fuzzy logc s partcularly attractve due to ts ablty to solve problems n the absence of accurate mathematcal models [3]. Surface roughness modelng n turnng s consdered complex process, so usng the conventonal technques to model the surface roughness n turnng results n sgnfcant dscrepances between smulaton results and expermental data. Thus, ths complex and hghly tme-varable process fts wthn the realm of neuro-fuzzy technques. The applcaton of a neuro-fuzzy nference system s used for predcton and overcomes the lmtatons of a fuzzy nference system such as the dependency on the expert for fuzzy rule generaton and desgn of the non- adaptve fuzzy set. 3.. Structure of The Adaptve Neuro-Fuzzy Inference System Adaptve neuro-fuzzy nference system s a fuzzy nference system mplemented n the framework of an adaptve neural network. By usng a hybrd learnng procedure, ANFIS can construct an nput-output mappng based on both human-knowledge as fuzzy f-then rules and approxmate membershp functons from the stpulated nput-output data pars for neural network tranng. Ths procedure of developng a FIS usng the framework of adaptve neural networks s called an adaptve neuro fuzzy nference system (ANFIS). There are two methods that ANFIS learnng employs for updatng membershp functon parameters: ) backpropagaton for all parameters (a steepest descent method), and 2) a hybrd method consstng of backpropagaton for the parameters assocated wth the nput membershp and least squares estmaton for the parameters assocated wth the output membershp functons. As a result, the tranng error decreases, at least locally, throughout the learnng process. Therefore, the more the ntal membershp functons resemble the optmal ones, the easer t wll be for the model parameter tranng to converge. Human expertse about the target system to be modeled may ad n settng up these ntal membershp functon parameters n the FIS structure [4-5]. The general ANFIS archtecture s shown n Fg. Fve network layers are used by ANFIS to perform the followng fuzzy nference steps. () Input fuzzfcaton, () Fuzzy set database constructon, () Fuzzy rule base constructon, (v) Decson makng, and (v) Output defuzzfcaton. For nstance assume that the FIS has two nputs x and x 2 and one output y. For the frst order Sugeno fuzzy model, a typcal rule set wth two fuzzy f-then rules can be expressed as: Rule : IF (x s A ) AND (x 2 s B ) THEN f = p x +q x 2 +r () Rule 2: IF ((x s A 2 ) AND (x 2 s B 2 ) THEN f 2 = p 2 x +q 2 x 2 +r 2 (2) Where A, A 2 and B, B 2 are the member shp functons for the nput x and x 2, respectvely, p, q, r and p 2, q 2, r 2 are the parameters of the output functon. The functonng of the ANFIS s descrbed as: Layer : Calculate Membershp Value for Premse Parameter Every node n ths layer produces membershp grades of an nput parameter. The node output O l, = µ A (x ) for =,2, or (3) O l, = µ B-2 (x 2 ) for =3, 4 (4) Where x (or x 2 ) s the nput to the node ; A (or B -2 ) s a lngustc fuzzy set assocated wth ths node. O, s the membershp functons (MFs) grade of a fuzzy set and t specfes the degree to whch the gven nput x (or x 2 ) satsfes the quantfer. MFs can be any functons that are Gaussan, generalzed bell shaped, trangular and trapezodal shaped functons. A generalzed bell shaped functon can be selected wthn ths MFs and t s descrbed as: µ A (x) (5) 2b x - c a Where a, b, c s the parameter set whch changes the shapes of the membershp functon degree wth maxmum value equal to and mnmum value equal to 0. Layer 2: Frng Strength of Rule Every node n ths layer, labeled Π, whose output s the product of all ncomng sgnals: O 2, = w = µ A (x ) µ B (x 2 ) for =, 2 (6) Layer 3: Normalze Frng Strength The th node of ths layer, labeled N, calculates the normalzed frng strength as, O 3, = w = w w w 2 =,2 (7)

3 2009 Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved - Volume 3, Number 4 (ISSN ) 254 Fgure. ANFIS archtecture. Table. Chemcal composton of Alumnum Alloy 6082 Composton weght (%) Coper 0. (max) Magnesum Slcon Iron 0.6 Manganese Chromum up to 0.25 Others 0.3 Alumnum balance Layer 4: Consequent Parameters Every node n ths layer s an adaptve node wth a node functon, O 4, = w f = w (px +q x 2 +r ) (8) Where w th s the normalzed weghtng factor of the rule, f s the output of the th rule and p, q r s consequent parameter set of ths node. Layer 5: Overall Output The sngle node n ths layer s a fxed node labeled Σ, whch computes the oveall output as the summaton of all ncomng sgnals: Overall output = O 5, w f w f ANFIS requres a tranng data set of desred nput/output par (x, x 2 x m, y) depctng the target system to be modeled. ANFIS adaptvely maps the nputs (x, x 2 x m ) to the outputs (y) through MFs, the rule base and the related parameters emulatng the gven tranng data set. It starts wth ntal MFs, n terms of type and number, and the rule base that can be desgned ntutvely. ANFIS apples a hybrd learnng method for updatng the FIS parameters. It utlzes the gradent descent approach to w (9) Table 2. Physcal propertes of Alumnum alloy Property fne-tune the premse parameters that defne MFs. It apples the least-squares method to dentfy the consequent parameters that defne the coeffcents of each output equaton n the Sugeno-type fuzzy rule base. The tranng process contnues tll the desred number of tranng steps (epochs) or the desred root mean square error (RMSE) between the desred and the generated output s acheved. In addton to the tranng data, the valdaton data are also optonally used for checkng the generalzaton capablty of FIS. 4. Expermental Detals Value Densty 2.70 g/ cm 3 Meltng pont 555 C Modulus of Elastcty 70 G Pa Electrcal Resstvty 0.038x0-6 Ω.m Thermal Conductvty 80 W/m K Thermal Expanson 24x0-6 /K The experments were conducted accordng to full factoral desgn. The cuttng parameters selected for the present nvestgaton s cuttng speed (V), feed (f) and depth (d) of cut. Snce the consdered varables are multlevel varables and ther outcome effects are not lnearly related. It has been decded to use three level tests for each factor. The machnng parameters used and ther levels are gven n Table 3. The machnng parameters, actual settng values and average surface roughness values are presented n Table 4. All the experments were conducted on CNC Turnng Lathe wth the followng specfcatons: Swng Over the Bed: 50mm, Swng Over Cross Slde: 50mm, Dstance Between Centers: 300mm, Spndle Power

4 Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved - Volume 3, Number 4 (ISSN ) Table 3. Machnng Parameters and ther Levels. HP, Spndle Speed (step less):0-3000rpm, Spndle Bore: 2mm, Spndle Taper: MT3, Talstock Taper: MT2, the Tool Holder used for Turnng operaton was a WIDAX tool holder SDJCR 22 F3 and the tool materal used for the study was Carbde Cuttng Tool. The average surface roughness (R a ) whch s mostly used n ndustral envronments s taken up for the present study. The roughness was measured number of tmes and averaged. The average surface roughness s the ntegral absolute value of the heght of the roughness profle over the evaluaton length and was represented by the followng equaton. a L L R (0) 0 Y(x)dx Where L s the length taken for observaton and Y s the ordnate of the profle curve. The surface roughness was measured by usng Surtronc 3 + stylus type nstrument manufactured by Taylor Hobson wth the followng specfcatons. Traverse Speed: mm/sec, Cut-off values 0.25mm, 0.80mm and 2.50mm, Dsplay LCD matrx, Battery Alcalne 600 measurements of 4 mm measurement length. The surfaces are cleaned and postoned usng a V- block before each measurement. The actual settng values for the desgn matrx [6] and expermental results are shown n Table Results and Dscusson The ANFIS model has been developed as a functon of machnng parameters usng twenty seven tran data presented n Table 4. The fuzzy logc toolbox of MATLAB 7.0 was used to tran the ANFIS and obtan the results. Dfferent ANFIS parameters were tested as tranng parameters n order to acheve the perfect tranng and the maxmum predcton accuracy. Fg 2 shows the fuzzy nference system (FIS) of ANFIS. The three nputs and one output and ther fnal fuzzy membershp functons are shown n Fg 2. A total of 78 network nodes and 27 fuzzy rules were used to buld the fuzzy nference system. A trangular membershp functons were used to tran ANFIS because t acheved the lowest tranng error of (0.666) at 0 epochs, as shown n the tranng curve of Fg 3. A perfect tranng s clear from Fg 3. Three trangular membershp functons were used for nputs (V, f and d). Fg 4 shows the comparson between the expermental and predcted values by the ANFIS and RSM model for tranng data. The predcted values by ANFIS and RSM model for tranng data are presented n Table 4. The average percentage devaton for tranng data set n the predcton of Surface roughness usng ANFIS and RSM model s found to be 9.75%, 5.57% respectvely. 5.. Valdaton Runs The models developed by ANFIS and RSM are valdated usng the valdaton data presented n Table 5. The predcted results were presented n Table 5. The predcted surface roughness values wth the actual expermental values of surface roughness were plotted and shown n Fg 5. The average percentage devaton n the predcton of Surface roughness usng ANFIS and RSM s found to be 3.29% and 5.86% respectvely. 6. Conclusons An adaptve neuro-fuzzy system and RSM s appled to predct the surface roughness durng the turnng process. The machnng parameters were used as nputs to the ANFIS and RSM to predct surface roughness. The followng conclusons can be drawn from ths study: The ANFIS model could predct the surface roughness for tranng data wth an average percentage devaton of 9.75% when a trangular member shp functon s appled or 90.25% accuracy, whle RSM model could predct the surface roughness for tranng data wth an average percentage devaton of 5.57% or 84.43% accuracy from tranng data set. The ANFIS model could predct the surface roughness for testng or valdaton data set wth an average percentage devaton of 3.29% when a trangular member shp functon s appled or 96.7% accuracy, whle RSM model could predct the surface roughness for tranng data wth an average percentage devaton of 5.86% or 84.4% from valdaton data set. The accuracy of the developed model can be mproved by ncludng more number of parameters.

5 2009 Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved - Volume 3, Number 4 (ISSN ) 256 Table 4. Expermental Condtons, results (Expermental and Predcted). Fgure 2. Fuzzy nference system for surface roughness predcton

6 Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved - Volume 3, Number 4 (ISSN ) Fgure 3. ANFIS Tranng Curve. Surface Roughness (Ra, μm) 7 6 Expermental Ra RSM Predcted Ra ANFIS Predcted Ra Experment No. Fgure 4. comparson between expermental and predcted values for tranng data. Surface Roughness(Ra, μm ) Expermental Ra RSM Predcted Ra ANFIS Predcted Ra Experment No. Fgure 5. ANFIS Valdaton dagram.

7 2009 Jordan Journal of Mechancal and Industral Engneerng. All rghts reserved - Volume 3, Number 4 (ISSN ) 258 Acknowledgement The Authors are thankful to the Hon ble Charman Dr. M.Santh Ramudu, Managng Drector Mr. M. Svaram, Prncpal Dr. T. Jaya Chandra Prasad and H.O.D of M.E, Rajeev Gandh memoral College of Engg& Technology, Nandyal for provdng the facltes to carry out the research work. References [] M. S. Lou, J.C. Chen, C. M. L, Surface roughness predcton for CNC end mllng. Journal of Industral Technology, Vol.5, No., Nov 998 Jan 999, 2-6. [2] P. V. S. Suresh, P. V. Rao, S. G. Desmukh, A genetc algorthm approach for optmzaton of the surface roughness predcton model. Internatonal Journal of Mach Tools & Manufacture, Vol. 42, 2002, [3] C. A. Van Luttervelt, T. H. C. Chlds, I. S. Jawahr, F. Klocke, P. K. Venuvnod, Present stuaton and future trends n modelng of machnng operatons. Process Report of the CIRP workng group on Modelng of Machnng Operatons, Annals of the CIRP, Vol. 47, No.2, 998, [4] K. Taraman, B. Lambert, A surface roughness model for a turnng operaton. Internatonal Journal of producton Research, Vol. 2, No.6, 974, [5] M. Hasegawa, A. Sereg, R.A. Lndberg, Surface roughness model for turnng. Trbology Internatonal December, 976, [6] R.M. Sundaram, B.K. Lambert, Mathematcal models to predct surface fnsh n fne turnng of steel. Internatonal Journal of Producton Research, Vol. 9, Part-I, 98, [7] R.M.D.E. Dmla, P.M. Lster, N.J Leghton, Neural network solutons to the tool condton montorng problem n metal cuttng-a revew crtcal revew of methods. Internatonal Journal of Machne Tools and Manufacture, Vol. 39, 997, [8] A Mtal, M. Mehta, Surface roughness predcton models for fne turnng. Internatonal Journal of Producton Research, Vol. 26, 988, [9] S. G. Ahmed, Development of a predcton model for surface roughness n fnsh turnng of alumnum. Journal of Sudan Engneerng socety, Vol. 52, No.45, 2006, -5. [0] D. Sngh, P. Venkateswara Rao, A surface roughness predcton model for hard turnng process. Internatonal Journal of Advanced Manufacturng, Vol. 32, No.-2, 2007, [] L. Zhanje, Y. Bng, T. Mel, Predcton of surface roughness of dffcult-to-cut materal by HSM based on RBF neural network. 6 th Internatonal conference on Instrumentaton, measurement, crcuts and systems, Hangzhou, Chna, [2] C. Lu, J. Costes, Surface profle predcton and analyss appled to turnng process. Internatonal Journal of Machnng and Machnablty of Materals, Vol. 4, No. 2-3, 2008, [3] S. M. Samhour, B.W. surgenor, surface roughness n grndng: on-lne predcton wth adaptve neuro-fuzzy nference system. Transactons of NAMRI/SME, Vol. 33, 2005, [4] J.S.R. Jang, ANFIS: adaptve-network- based fuzzy nference system. IEEE Transactons on Systems, Man and Cybernetcs, Vol. 23, 993, [5] J.S.R. Jang, C.T. Sun, Neuro-fuzzy modelng and control. Proceedngs of the IEEE, Vol. 83, No.3, 995, [6] Montgomery D. C. Desgn and analyss of Experments. 2 nd ed. New York: John Wley and sons; 984.

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