Prediction of cutting force and surface roughness using Taguchi technique for aluminum alloy AA6061

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1 Austrlin Journl of Mechnicl Engineering ISSN: (Print) (Online) Journl homepge: Prediction of cutting force nd surfce roughness using Tguchi technique for luminum lloy AA6061 Elsswi Yhy, Guofu Ding & Shengfeng Qin To cite this rticle: Elsswi Yhy, Guofu Ding & Shengfeng Qin (2016): Prediction of cutting force nd surfce roughness using Tguchi technique for luminum lloy AA6061, Austrlin Journl of Mechnicl Engineering, DOI: / To link to this rticle: Published online: 24 Jul Submit your rticle to this journl View relted rticles View Crossmrk dt Full Terms & Conditions of ccess nd use cn be found t Downlod by: [Southwest Jiotong University], [MOHAMED ESHAG] Dte: 29 July 2016, At: 06:01

2 Austrlin Journl of Mechnicl Engineering, TECHNICAL PAPER Prediction of cutting force nd surfce roughness using Tguchi technique for luminum lloy AA6061 Elsswi Yhy,b, Guofu Ding nd Shengfeng Qin c School of Mechnicl Engineering, Advnced Design nd Mnufcturing Institute, Southwest Jiotong University, Chengdu , Chin; b School of Mechnicl Engineering, Sudn University of Science nd Technology, Khrtoum, Sudn; c School of Design nd Engineering, Northumbri University, London, UK ABSTRACT Surfce roughness nd cutting force re strongly ffected by mchining prmeters. In the pst few decdes, mny reserchers hve estblished the reltionship between the surfce roughness nd mchining prmeters, but less ttention hs been pid to tool shpe nd geometry, nd the number of tool flutes which ffects vibrtions nd mchining system. Therefore, this study includes the tool flutes in ddition to cutting speed, depth of cut nd feed rte s independent vribles. A set of mchining experimentl work ws crried out on verticl milling mchine (end milling) nd AA6061 ws used s the work piece mteril to provide originl dt. Response surfce method is dopted to estblish the reltionship between mchining prmeters to the surfce roughness nd cutting force using the Tguchi technique. The findings bsed on nlysis of vrince nd Minitb 16, concluded tht surfce roughness hs only two significnt prmeters (tool flutes nd depth of cut) which ffected the surfce mchining, while cutting force ws significntly ffected by ll mchining prmeters used in this study. Liner nd non-liner models, for surfce roughness nd cutting force re incorported. Verifictions nd optimiztions of the results crried out indicted suitbility of the technique used in this study. 1. Introduction Mchining opertions hve been the core of the mnufcturing industry since industril revolution strted. Mchining is process of mteril removl using cutting tools nd mchine tools to obtin the required product dimensions with good surfce finish. The mnufcturing industries strive to chieve either minimum cost of production or mximum production rte, or even optimum combintion of both, long with better product qulity in mchining. Mchining process is influenced by number of input nd output vribles, input vribles re the process-independent vribles while output re response vribles. In this study, surfce roughness nd cutting force re response vribles in mchining process which re ffected by the mchining prmeters known s independent or input vribles. Mchining prmeters, cutting speed, depth of cut, feed rte nd tool flutes re ffected surfce roughness nd cutting force in this study. Milling mchining process is selected to perform experimentl work, in which the metl is removed by rotting multiple tooth cutter, while the cutter rottes, ech tooth removes smll mount of mteril from dvncing work for ech spindle revolution. ARTICLE HISTORY Received 3 Mrch 2014 Accepted 21 July 2014 KEYWORDS Cutting force; mchining prmeters; significnt nlysis; surfce roughness; Tguchi method Surfce roughness is criticl qulity indictor for mchined surfce nd it ffects mechnicl properties such s: wer resistnce, coefficient of friction, ftigue strength, lubrictions, wer rte nd the corrosion resistnce of the mchined prts (Feng nd Wng 2002). In ddition, depending on specific cutting resistnce of the mteril, cutting force is the most importnt prmeter which determines the consumed power nd energy costs. There re lrge number of prmeters influencing the cutting force nd surfce roughness such s: cutting condition, cutting prmeters, tool geometry, work piece mterils nd properties, cutting tool properties, vibrtions nd cutting phenomen. Therefore, it is very difficult to develop n ccurte nlyticl model for surfce roughness nd cutting force which includes ll input prmeters (Chen nd Lou 1999; Toms 1999). Trditionl experimentl design methods re complicted nd difficult to use. Also these methods require lrge number of experiments. In order to minimize the number of tests required thus reducing the experimentl cost, the Tguchi experimentl design method could be powerful tool for designing high-qulity system (Bgci nd Ozcelik 2006; Ros et l. 2009). This method uses CONTACT Elsswi Yhy 2016 Engineers Austrli esswiyhy@yhoo.com

3 2 E. Yhy et l. specil design of orthogonl rrys (OAs) to study the entire prmeter spce with smll number of experiments. There re mny methods used in optimiztion nd prediction of cutting force nd surfce roughness, such s fuzzy system, rtificil neurl networks (ANN), multiple regression model, response surfce method, sttisticl nd finite-element nlysis. Phdke (1989) suggested Tguchi-bsed optimiztion technique hs produced unique nd powerful optimiztion discipline tht differs from trditionl prctices. Kurt, Bgci, nd Kynk (2009) pplied Tguchi methods in the optimiztion of cutting prmeters for surfce finish nd whole-dimeter ccurcy in dry drilling processes. OA, signl-to-noise (S/N) rtio, nlysis of vrince (ANOVA) nd regression nlyses were used to determine the optiml levels nd the effects of the drilling prmeters on surfce roughness nd whole dimeter. Muthukrishnn nd Pulo Dvim (2009) optimized the mchining prmeters of luminium nd silicon with ANOVA nd ANN techniques in mchining sttes. ANOVA results show tht the feed rte hs the highest significnce of 51% bsed on surfce roughness, while 30% bsed on depth of cut nd 12% bsed on cutting speed. Also neurl networks model results suggest there is very close correltion between the model output nd the physicl surfce roughness mesured. This method seems to possess predictive potentils for non-experimentl pttern dditionlly (simultion), ANN methodology consumes less time nd provides higher ccurcy. Therefore, optimiztion using ANN is the more effective method thn ANOVA nlysis. The Tguchi method is very populr technique, it proposed by Dr Tguchi in 1960s. It does not require complex mthemticl clcultions nd it cn esily determine the optimum levels of process prmeters. The Tguchi OAs re experimentl designs tht usully require only frction of the full-fctoril combintions. Mny OAs re vilble in other forms, such s frctionl fctoril nd Plckett Burmn designs. The rrys re designed to hndle s mny fctors s possible in certin number of runs. Creting Tguchi designs involve ssigning some or ll of the rry columns to the fctors in the experiment. The columns of the rrys re blnced nd orthogonl. This mens tht in ech pir of columns ll fctor combintions occur t the sme number of times. Orthogonl designs llow estimting the effect of ech fctor on the response independently of ll other fctors. Therefore, it ws used in the mnufcturing industry to reduce production time in both design nd production sttes, which led to reduction in the totl costs of the products nd n increse in the compny s profit. The Tguchi method cters for controlling the vritions cused by the uncontrollble fctors which re not tken into considertion t the conventionl design of the experiment (DOE; Zhng, Chen, nd Kirby 2007; Gologlu nd Skry 2008). The Tguchi method converts the objective function vlues into S/N rtio in order to mesure the performnce chrcteristics of the cutting prmeters ffecting surfce roughness nd cutting force. The S/N rtio is defined s the desired signl rtio for the undesired rndom noise vlue nd shows the qulity chrcteristics of the experimentl dt (Kurt, Bgci, nd Kynk 2009; Guny, Kcl, nd Turgut 2011). There re three different functions of S/N rtion in the nlysis of objective function used, which re known s the objective function nd lso defined s S/N rtio: the-lrger-thebetter, the-smller-the-better nd the-nominl-thebest. These nlysis were used in combintion with ANOVA to determine the sttisticl significnce of the cutting prmeters. Aluminium is n importnt mteril in mnufcturing process due to its specil properties nd performnce. In this study, AA6061 luminium lloy is selected s the work piece mteril. This type of luminium is het-tretble, it offers rnge of good mechnicl properties nd good corrosion resistnce. It cn be fbricted by most of the commonly used techniques, welded by ll methods nd cn be furnce brzed. Good surfce finish of this type of luminium would lso improve both ppernce nd corrosion resistnce. Therefore, pplictions of this grde re used for wide vriety of products nd pplictions from truck bodies nd frmes to screw mchine prts nd structurl components. AA6061 is used where ppernce nd corrosion resistnce with good strength re required. Experimentl work is crried out on milling mchine. Cutting prmeters used in this study re cutting speed, depth of cut, feed rte nd tool flutes. Tool flutes is new fctor dded in this study. The Tguchi method is selected s the technique to express the cutting prmeters significnce in surfce roughness nd cutting force mchining. 2. Methodology The im of this ppliction is the evlution of the significnce of mchining prmeters to surfce roughness nd cutting force mchining process using the Tguchi method. And to express predictive models for responses, nd surfce roughness optimiztions. Equtions (1) nd (2) show the surfce roughness (R ) nd cutting force (F c ) functions, both functions re bsed on cutting speed (v), depth of cut (d), feed rte (f) nd tool flute (z). The Tguchi technique L 27 OA is used s ppliction method. R (x i )=f (x i1, x i2, x i3,, x in ) F c (x i )=f (x i1, x i2, x i3,, x in ) (1) (2)

4 Austrlin Journl of Mechnicl Engineering 3 Tble 1. Cutting prmeters nd their levels. Low level Medium level High level Seril No. Cutting prmeter ( 1) (0.0) (+1) 1 Cutting speed (v). rev/min Feed rte (f). mm/rev Depth of cut (d). mm Tool flutes number (z) Where R : surfce roughness, F c : cutting force nd x i1, x i2, x i3,, x in re cutting prmeters. Four mchining prmeters with three levels re the design prmeters. Bsed on the Tguchi technique L 27 OA, there re 27 experiments were crried out. Minitb 16 is the softwre used to evlute the significnce of the input prmeter bsed on ANOVA nd S/N rtio. The S/N rtio is n effective representtion tht enbles to find the significnce of the degree of prmeters which intervene in process through evluting their vrince. Usully, there re three ctegories of qulity chrcteristics in the nlysis of the S/N rtio, i.e. Smller is Best Eqution (3), Higher is Best Eqution (4) nd Nominl is Best s in Eqution (5). [ ( n )] 1 S/N = 10 log y 2 i (3) n S/N = 10 log where n is the number of mesurements, y i is the mesured chrcteristic vlue, ȳ: the men, nd σy: stndrd devition. Non-liner model cn be estimted using full qudrtic model eqution (6) to express the ccurte mthemticl model for responses nd cutting prmeters. n 1 n n n ŷ = b 0 + b i x i + b ii x 2 i + b ij x i x j + ε(6) i=1 where ŷ is the predicted vlue (response); b 0, b i, b ii nd b ij re regression coefficients; x i, x j re independent vribles nd ε is the error. 3. Experimentl set-up [ 1 n In this work, the Tguchi technique is used to express the significnce of the mchining prmeters bsed on surfce roughness nd cutting force. The first step of the prmeter selection is to choose the pproprite OA tht ffects the experiment pln nd ccurcy of the sttisticl nlysis. As per Tguchi s method, the totl degree of freedom (DF) of the selected OA must be greter thn n i=1 i=1 S/N = 10 log ( ȳ σy ) i=1 ( ) ] 1 y 2 i i=1 j=i+1 (4) (5) or equl to the totl DF required for the experiment (Aggrwl et l. 2008). In this study, OA L 27 is selected, therefore DF is 26. Bsed on the bove criteri, four mchining prmeters influence surfce roughness nd cutting force in this work ppliction. The prmeters selected re cutting speed, depth of cut, feed rte nd tool flute s shows in Tble 1, cutting prmeters re chosen in three levels. Therefore, design of performnce chrcteristics L27 (3^4) OA ws chosen since it hs the bility to control the interctions mong the fctors (Dvim 2003; Tso 2007; Tso nd Hocheng 2008). Verticl milling mchine is selected to crry out the physicl experimentl work. CNC milling mchine DMC 635 V with spindle speed up to 14,000 rpm, motor drive power of 40 kw nd ir coolnt, locted in the institute of Advnced Design nd Mnufcturing Lbortory Southwest Jio Tong University, is used for mchining the experiments. High- Speed Steel cutting tool with dimeter 16 mm, 25 mm opertion length nd 2, 3 nd 4 flutes is used in mchining processes. Aluminium lloy AA6061 with cubic shpe with mm work piece dimensions ws prepred. Cutting speed clcultions bsed on work piece mterils, cutting tool type nd dimeter re vried between 150 nd 250 m/min, these results corresponding to rev/min. Feed rtes nd depth of cut re obtined fter severl ttempts in physicl mchining; these mchining selection vlues were expected to yield good results. After the completion of the mchining processes, surfce roughness (R ) ws mesured three times using surfce roughness device (TR200 portble) nd then the men of the reding is registered. TR200 portble device with disply rnge R : μm nd mximum disply resolution μm is used for mesuring R roughness vlues of mchined experiments. The cutting force is mesured using dynmometer fixed on the mchine tble nd specimen with screen monitor to sve the cutting force dt. The min concept of the experimentl work is to set up independent prmeters tht ffected surfce roughness. Figure 1 shows the process flow digrm. The experimentl work conducted on CNC milling mchine, nd end milling mchining ws performed. Mchining prmeters selected in this study re cutting speed, feed rte nd depth of cut beside cutter flutes number. Mchined surfce cn be controlled by mchining prmeters, which ffect surfce mchining

5 4 E. Yhy et l. Figure 1. Process flow digrm. independently. In this study, n ttempt is mde to control the surfce roughness through the input mchining prmeters. Firstly, one should know the significnt prmeter in the mchining process, nd then try to control it ccording to the desirble mchined surfce roughness. DOE is ply n importnt role in this study, therefore the Tguchi technique is pplied in this study nd experimentl results crried out for nlysis nd investigtion. 4. Results nd discussions Tble 2 shows the experimentl results bsed on the Tguchi design nd OA (OA L 27 ). The surfce roughness is the men of mesured vlues; ech reding step ws repeted three times. Cutting force in this tble is equl to the squre root of sum squres of the forces in x, y nd z directions. The totl number of experimentl dt is 27 tests. Tble 3 shows the ANOVA result for surfce roughness. Bsed on P-vlue, only two fctors re significnt t 95% confidence intervl, which re tool flutes nd depth of cut. New regression is dopted; Eqution (7) shows liner regression for surfce roughness prediction. Finlly, Tble 4 shows the liner model coefficients for surfce roughness. Non-liner prediction model is dopted for surfce roughness. Tble 5 shows the non-liner model coefficients. Bsed on these coefficients, Eqution (8) is crried out to express non-liner model. R = d 0.132z (7) R = d 0.132z z 2 (8) The second prt of the nlysis is for the cutting force. Tble 6 shows ANOVA results for the cutting force. From the results obtined, ll the cutting prmeters re highly significnt t 95% confidence intervl. From F-test vlues, depth of cut hs higher significnt vlue, while tool flute hs lower. The regression R 2 for this nlysis is 89.42%, djusted is 87.5% nd predicted is 85.44%. To dopt mthemticl prediction liner model for the cutting force, Tble 7 shows liner regression coefficients, nd finlly Eqution (9) is crried out for cutting force prediction bsed on significnt prmeters. Non-liner models hve regression R 2 for nlysis is 94.12%, while djusted is 91.51%. Non-liner prediction model for cutting fore is dopted; Tble 8 shows the model coefficients. Bsed on these coefficients, Eqution (10) is crried out s non-liner prediction model. F c = v 18.28d 41.64f 1.31z (9) F c = v 3.66d 2.08f 1.31z f z 2 (10) From the results obtined, the responses considered in the present experiment work re surfce roughness nd cutting force which re both to be minimized. Figures 2 nd 3 show S/N rtions for surfce roughness nd cutting force, respectively. In the surfce roughness plot in Figure 2, there re only two significnt prmeters (tool flute nd depth of cut); tool flute hs higher significnce thn depth of cut. In the cutting force plot in Figure 3, ll the prmeters used re significnt, but the significnce of cutting speed nd depth of cut were higher while feed rte nd tool flute were lower. In contour plots, Figure 4 shows the surfce roughness plots of the significnt prmeters only, it suggest tht the tool flute hs higher significnce in surfce roughness; tool with high number of flutes with minimum depth of cut cn led to minimum surfce roughness. Incresing in depth of cut could led to n increse in the surfce roughness vlue. Figure 5 shows the cutting force contour plot, the sme ide pplied in surfce roughness (selection of the first two high significnt prmeters). The two highly significnt fctors in cutting force re cutting speed nd depth of cut; incresing both the cutting speed nd depth of cut leds to minimize in the cutting force vlue. From Figures 2 nd 4, the minimum optiml level of surfce roughness cn be obtined when using the tool hs three flutes, nd depth of cut 0.4 mm, cutting speed 5000 rpm nd 0.05 mm/rev feed rte. The sme optimiztion for cutting force Figures 3 nd 5, minimum cutting force cn be chieve with the setting prmeters,

6 Austrlin Journl of Mechnicl Engineering 5 Tble 2. Tguchi L27 (3^4) OA design. Seril number Cutting speed (rev/min) Depth of cut (mm) Feed rte (mm/ rev) Tool flutes Surfce roughness men (μm) Cutting force (N) Tble 3. ANOVA results for surfce roughness. Source DF Seq. SS Adj. SS Adj. MS F-test P-vlue Regression Cutting speed Depth of cut Feed rte Tool flute Error Lck-of-fit Pure error Totl Significnt t 95% confidence intervls. Tble 4. Liner surfce roughness coefficients. Predictor Coefficient SE. coefficient T-test P-vlue Constnt Depth of cut (d) Tool flute (z) Significnt t 95% confidence intervls. Tble 5. Non-liner surfce roughness coefficients. Predictor Coefficient SE. coefficient T-test P-vlue Constnt Depth of cut (d) Tool flute (z) Tool flute Tool flute (z 2 ) Significnt t 95% confidence intervls rpm cutting speed, 0.8 mm depth of cut, 0.15 mm/ rev feed rte nd tool hs four flutes. Figure 6 shows the residul plots for the surfce roughness. In norml probbility plot, the fitting is much closer to the stright line, which indicted it to be good regression model. In histogrm plot, the fitting of frequency nd residuls showing norml distribution, with zero men nd two tils ending with nd From observtion, the residul nd predicted vlues (fitted) give sctter plots which indicted it to the good model. The residul is limited between +0.2 nd 0.2, Figure 7 shows the probbility plot with 95%

7 6 E. Yhy et l. Tble 6. ANOVA results for cutting force. Source DF Seq. SS Adj. SS Adj. MS F-test P-vlue Regression Cutting speed Depth of cut Feed rte Tool flute Error Lck-of-fit Pure error Totl Significnt t 95% confidence intervls. Tble 7. Liner cutting force coefficients. Predictor Coefficient SE. coefficient T-test P-vlue Constnt Cutting speed (v) Depth of cut (d) Feed rte (f) Tool flute (z) Significnt t 95% confidence intervls. Tble 8. Non-liner cutting force coefficients. Predictor Coefficient SE. coefficient T-test P-vlue Constnt Cutting speed (v) Depth of cut (d) Feed rte (f) Tool flute (z) Feed rte Feed rte (f 2 ) Tool flute Tool flute (z 2 ) Notes: R 2 = 94.12%, R 2 (dj) = 91.51%. Significnt t 95% confidence intervls. Figure 2. S/N rtion of surfce roughness. confidence intervls, ll the points re locted between lower nd upper control limits. Figure 8 shows the residul plots for the cutting force. In norml probbility plot, the fitting is not complete stright line, but the R 2 for prediction in the regression ws high (0.85%), which indicted the high correltion of the nlysis. In the histogrm plot, the fitting of frequency with the residuls gives norml distribution shpe, with zero men nd round 10 redings showing zero residuls. The residul nd predicted vlues (fitted) show sctter plots which indicte good results. From observtions, the residul is limited between +3 nd 3N; only one reding is out of the rnge with vlue 4N; suggesting tht the technique used in this study is robust.

8 Austrlin Journl of Mechnicl Engineering 7 Figure 3. S/N rtio of cutting force. Figure 4. Contour plot of the surfce roughness. Figure 5. Contour plot of the cutting force. 5. Verifictions Surfce roughness verifictions re crried out for liner nd non-liner models. Bsed on the verifiction results of mchining nd significnt prmeters in Tble 9, liner models re vlid for ll levels of the tool flute number, while non-liner models re only vlid when the flute number is equl to two with 95% confidence intervls. In cutting force verifiction, Tble 10 shows liner models re lso vlid for ll smples selected in this process, while non-liner models hve the sme route with surfce roughness. From the observtions, the results in non-liner models showed tht surfce roughness hs strong reltionship with cutting force showing the sme rnge of ccepting vlues.

9 8 E. Yhy et l. Figure 6. Residul plot of the surfce roughness. Figure 7. Probbility plot of the surfce roughness. Figure 8. Residul plot of the cutting force. Optimiztion of the mchining prmeters is difficult to chieve in mnufcturing environments, therefore, selection of the mchining prmeters levels could be chieved by pplying desirbility function rule, which is depend on the desired response. Minitb softwre displys design prmeters for the response (surfce roughness) by checking trget, lower nd upper vlues for response, then optimized vlues of cutting vribles is obtined using inner lgorithm. Figure 9 shows optimized output results.

10 Austrlin Journl of Mechnicl Engineering 9 Tble 9. Surfce roughness verifictions. S. No. Cutting speed Depth of cut Feed rte No. of flutes The vlue is out of confidence intervl rnge with 95%. Liner prediction reding Non-liner prediction reding 95% confidence intervls Tble 10. Cutting force verifictions. S. No. Cutting speed Depth of cut Feed rte No. of flutes The vlue is out of confidence intervl rnge with 95%. In order to verify the optimiztion results, experimentl work crried out is bsed on optimized mchining prmeters vlues (5000, 0.4, 0.05 nd 3) for cutting speed, depth of cut, feed rte nd tool flutes number. In ech cutting test, surfce roughness re mesured three times nd the men of the redings is registered. Tble 11 shows the verifiction of the optimized results, indicting the error vlues re limited, which vlidte the technique used in this work. 6. Conclusion From the results obtined, surfce roughness hs only two significnt fctors, tool flute nd depth of cut ffecting surfce roughness mchining, while cutting force is Liner prediction reding Non-liner prediction reding 95% confidence intervls Figure 9. Optimiztion output results. Tble 11. Verifiction of optimiztion results. Test No. Cutting speed (rpm) Depth of cut (mm) Feed rte (mm) Tool flutes Optimiztion results (μm) Experimentl results (μm) Error (μm) significntly ffected by ll cutting vribles used in this study. Mchining prmeters used in this study re cutting speed, depth of cut, feed rte nd tool flute. Liner nd non-liner models for surfce roughness nd cutting force prediction were crried out delivering good results. Non-liner models verifiction showed surfce roughness nd cutting force hve the sme route, which suggests strong reltionship between surfce roughness to the cutting force. Minimum surfce roughness optimiztion obtined in this study hve the 5000 rpm cutting speed, 0.4 mm depth of cut, 0.05 feed rte nd 3 4 tool flutes number. Verifiction results for optimized vlues were crried out delivering good results, which suggest the technique used in this study is suitble (Tguchi method).

11 10 E. Yhy et l. Acknowledgement The uthor would like to express deep thnks to the director of dvnce design nd mnufcturing institute in south-west Jio Tong University for llowing the use of the workshop equipments nd mchines. Also mny thnks to Dr Jing Lie nd Engineer Zhng for their gret ssistnce in experimentl mchining nd mesuring for this work. Disclosure sttement No potentil conflict of interest ws reported by the uthors. Notes on contributors Elsswi Yhy, PhD in Mnufcturing nd Automtion Engineering, Southwest Jiotong University, Chin. He ws wrded MSc in Mnufcturing Engineering 2005, BSc in Generl Mechnics 2001, nd HND He is lecturer in Mechnicl Engineering Production, Sudn University of Science nd Technology, Sudn. He is leding mechnicl engineering section from 2006 to 2010, his experiences in mchining shop opertions, welding, forging, csting nd fitting processes. He ws leding metrology lb stff from 2003 to 2005, lso leding mchining shop from 1995 to He hs been n exmintion deprtment member for more thn 10 yers. He teches mny courses such s, Mechnics of Mterils, Mechnics of Mchines, nd Mnufcturing Technology. Dr. Yhy hs published more thn seven ppers. Guofu Ding, Eng. PhD, is full-time professor working in Institute of Advnced Design nd Mnufcturing, Southwest Jiotong University, Chin since He is leding lrge cdemic reserch group focusing on digitl design nd mnufcturing including virtul prototyping, virtul mnufcturing, NC Mchining nd mnufcturing utomtion. Shengfeng Qin is Reder in Digitl Design nd Mnufcturing. Prior to joining Northumbri School of Design 2014, he ws lecturer nd then senior lecturer in Product Design in Deprtment of Design t Brunel University. There, he led the Computer Aided Design nd Engineering reserch group. Dr Qin ws post-doctorl reserch fellow t the University of Loughborough s Mnufcturing System Integrtion Institute (MSI) between 2000 nd 2001, nd reserch ssistnt in the School of Product Design t the Crdiff Metropolitn University He ws n cdemic visiting scholr t the University of Birminghm ; nd n ssocite professor in Est Chin Jiotong University. He hs published more thn 150 ppers in book chpters, journls nd conferences. He is member of IEEE, member of the Design Society, nd the editor of the Journl of Systems Science nd Control Engineering (Tylor nd Frncis); he is visiting professor in south-west Jiotong University Chin. References Aggrwl, A., H. Singh, P. Kumr, nd M. Singh Optimizing Power Consumption for CNC Turned Prts Using Response Surfce Methodology nd Tguchi's Technique A Comprtive Anlysis. Journl of Mterils Processing Technology 200: Bgci, E., nd B. Ozcelik Anlysis of Temperture Chnges on the Twist Drill under Different Drilling Conditions Bsed on Tguchi Method during Dry Drilling of Al7075-T651. The Interntionl Journl of Advnced Mnufcturing Technology 29: Chen, J. C., nd M. S. Lou Fuzzy-nets Bsed Approch Using n Accelerometer for n In-process Surfce Roughness Prediction System in Milling Opertions. Interntionl Journl of Computer Integrted Mnufcturing 13: Dvim, J. P Study of Drilling Metl Mtrix Composites Bsed on the Tguchi Techniques. Journl of Mterils Processing Technology 132: Feng, C. X. J., nd X. Wng Development of Empiricl Models for Surfce Roughness Prediction in Finished Turning. Interntionl Journl of Advnced Mnufcturing Technology 20: Gologlu, C., nd N. Skry The Effects of Cutter Pth Strtegies on Surfce Roughness of Pocket Milling of Steel Bsed on Tguchi Method. Journl of Mterils Processing Technology 206: Guny, M., A. Kcl, nd Y. Turgut Optimiztion of Mchining Prmeters in Milling of Ti 6Al 4V Alloy Using Tguchi Method. e-journl of New World Sciences Acdemy Engineering Science 6 (1): Kurt, M., E. Bgci, nd Y. Kynk Appliction of Tguchi Methods in the Optimiztion of Cutting Prmeters for Surfce Finish nd Hole Dimeter Accurcy in Dry Drilling Processes. The Interntionl Journl of Advnced Mnufcturing Technology 40: Muthukrishnn, N., nd J. Pulo Dvim Optimiztion of Mchining Prmeters of Al/SiC-MMC with ANOVA nd ANN Anlysis. Journl of Mterils Processing Technology 209: Phdke, M. S Qulity Engineering Using Robust Design. Englewood Cliffs, NJ: Prentice Hll. Ros, J. L., A. Robin, M. B. Silv, C. A. Bldn, nd M. P. Peres Electrodeposition of Copper on Titnium Wires: Tguchi Experimentl Design Approch. Journl of Mterils Processing Technology 209: Toms, S Cutting Force Modeling Using Artificil Neurl Networks. Journl of Mterils Processing Technology 92: Tso, C. C Tguchi Anlysis of Drilling Qulity Associted with Core Drill in Drilling of Composite Mteril. The Interntionl Journl of Advnced Mnufcturing Technology 32: Tso, C. C., nd H. Hocheng Evlution of Thrust Force nd Surfce Roughness in Drilling Composite Mteril Using Tguchi Anlysis nd Neurl Network. Journl of Mterils Processing Technology 203: Zhng, J. Z., J. C. Chen, nd E. D. Kirby Surfce Roughness Optimiztion in n End-milling Opertion Using the Tguchi Design Method. Journl of Mterils Processing Technology 184: