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<ul><li><p>[9] </p><p>CHAPTER 2 </p><p>LITERATURE REVIEW </p><p>2.1 LITERATURE REVIEW ON SINGLE OBJECTIVE OPTIMIZATION </p><p>THROUGH TAGUCHIS METHOD </p><p>Taguchis parameter design offers a systematic approach for optimization of various </p><p>parameters with regard to performance, quality and cost (Phadke, 1989). The quality </p><p>of design can be improved by improving the quality and productivity in various </p><p>company-wide activities. Those activities concerned with quality include in quality of </p><p>product planning, product design and process design (Park 1996, Ranjit 2001). </p><p>Taguchis parameter design approach can reduce number of experiments to optimize </p><p>design for performance, quality and cost. Signal to Noise(S/N) ratio and orthogonal </p><p>array (OA) are two major tools used in robust design. S/N ratio measures quality with </p><p>emphasis on variation, and OA accommodates many design factors simultaneously </p><p>(Park 1996, Phadke 1998). </p><p>Taguchi method offers the quality of product is measured by quality characteristics </p><p>such as: nominal is the best, smaller is better and larger is better (Phadke, 1998 &amp; </p><p>Ranjit, 2001). </p><p>Das et al. (1997a and 1997b), Choudhury and Apparao (1999) and Choudhury et al. </p><p>(1999) have developed different models for optimization of process parameters using </p><p>different responses such as surface roughness, tool wear, vibrations etc. </p><p>Antony J (2001) presents a step by step approach to the optimization of production </p><p>process of retaining a metal ring in a plastic body by a hot forming method through </p><p>the utilisation of Taguchi methods of experimental design and achieves the good </p><p>results. </p></li><li><p>[10] </p><p>Park and Yum (2003) developed a procedure with the help of Taguchis method for a </p><p>dynamic parameter design problem and explained it with the help of an illustrated </p><p>example. </p><p>Gopalsamy B.M. et al. ( 2009 ), applied Taguchi method to find out the optimum </p><p>machining parameters while hard machining of hard steel and uses L18 orthogonal </p><p>array, S/N ratio and ANOVA to study the performance characteristics of machining </p><p>parameters which are cutting speed, feed, depth of cut and width of cut while </p><p>considering surface finish and tool life as response. Results of the study obtained by </p><p>Taguchi method match closely with ANOVA and cutting speed is the most </p><p>influencing parameter. </p><p>Rajendrakumar (2011) focuses on a design of experiment based approach to obtain an </p><p>optimal setting of turning process parameters (cutting speed, feed rate and depth of </p><p>cut) that may yield optimal tool flank wear and subsequent optimal settings of the </p><p>parameters and it have been accomplished with using Taguchis parameter design </p><p>approach. </p><p>Ficici F. et al. (2011) uses the Taguchi method to study the wear behaviour of </p><p>boronized AISI 1040 steel. They use orthogonal array, S/N ratio and ANOVA to </p><p>investigate the optimum setting parameters. The control factors used here are </p><p>boronizing time, applied load, sliding distance and sliding speed with weight loss as </p><p>response variables. The study show that the boronizing time had the greatest effect on </p><p>the wear followed by sliding distance. </p><p>Feng and Wang (2002) develops an empirical model for the prediction of surface </p><p>roughness in finish turning while considering working parameters like material, feed </p><p>rate, cutting tool point angle, depth of cut, spindle speed and cutting time. </p><p>Gusri et al. (2008) applied Taguchi optimization methodology to optimize cutting </p><p>parameters in turning Ti-6Al-4v ELI with coated and uncoated cemented carbide </p><p>tools. They show that the cutting speed and type of tool have a very significant effect </p><p>on the tool life, and the feed rate and type of tool have also a very significant effect on </p><p>the surface roughness. </p><p>Fnides et al. (2008) conducted tests on X38CrMoV5-1 steel treated at 50 HRC, </p><p>machined by a mixed ceramic tool to study the influence of the following parameters: </p></li><li><p>[11] </p><p>feed rate, cutting speed, depth of cut and flank wear on cutting forces and on surface </p><p>roughness. </p><p>Shinde et al. (2011) focuses on the effect of different machining parameters on </p><p>surface finish during turning operation. They consider cutting speed, feed rate and </p><p>depth of cut as machining parameters. </p><p>Kaladhar et al. (2012) applied Taguchi method to determine the optimum process </p><p>parameters for turning of AISI 304 austenitic steel on CNC lathe. They conducted </p><p>tests at four levels of cutting speed, feed and depth of cut. The influence of these </p><p>parameters are investigated on the surface roughness and material removal rate </p><p>(MRR). The results revealed that cutting speed significantly affects the surface </p><p>roughness followed by noise radius while depth of cut affects the MRR most followed </p><p>by cutting speed. </p><p>Rodrigues et al. (2012) proposes a study for the effect of cutting speed, feed rate and </p><p>depth of cut on surface roughness and cutting force while turning mild steel using </p><p>high speed steel (HSS) cutting tool. Experiments were conducted on a precision </p><p>centre lathe and the influence of cutting parameters on surface roughness and cutting </p><p>force was studied with the help of analysis of variance (ANOVA) based on adjusted </p><p>approach and also used linear regression analysis. </p><p>Petropoulos et al. (2005) develops a predictive model for cutting force components in </p><p>longitudinal turning of constructed steel with a coated carbide tool. Taguchi method is </p><p>used for the plan of experiments and the analysis is performed using response surface </p><p>methodology. Lastly a comparison was attempted to the result obtained with the help </p><p>of help of a well established semi-empirical and cutting resistance based Kienzle-</p><p>Victor model. </p><p>Singh and Kumar (2005 &amp; 2006) obtain an optimal setting of turning process </p><p>parameters (cutting speed, feed rate and depth of cut) resulting in an optimal value of </p><p>the cutting force and feed force when machining EN24 steel with Tic-coated Tungsten </p><p>carbide inserts using Taguchis parameter design approach. They uses Taguchis L27 </p><p>orthogonal array, signal to noise ratios (S/N) and analysis of variance (ANOVA) for </p><p>the study. </p></li><li><p>[12] </p><p>Kosaraju et al. (2012) investigate the effect of process parameters (cutting speed, feed </p><p>rate and depth of cut) on machinability performance characteristics and there by </p><p>optimization of turning of Titanium Grade 5 based on Taguchis L9 orthogonal array, </p><p>signal to noise ratios (S/N) and analysis of variance (ANOVA). The cutting speed was </p><p>identified as the most influential machining parameter on cutting force and </p><p>temperature. </p><p>Chorng et al., (2009) report that only the cutting speed affects significantly the </p><p>roundness of a cylindrical bar while the others are not. Rico et al., (2010) also </p><p>concludes the same result and also report that cutting speed-feed rate interaction and </p><p>cutting speed-depth of cut interaction significantly affects the roundness of cylindrical </p><p>bar. </p><p>Cicek et al., (2012) performed experimental trials using Taguchi orthogonal arrays to </p><p>obtain optimum surface roughness and roundness error values in the drilling of AISI </p><p>316 austentic stainless steel with untreated and treated drills and it was found that the </p><p>cutting speed had a significant effect on the surface roughness and that the cutting </p><p>speed and feed rate had a significant effects on the roundness error. </p><p>Sahoo and Sahoo (2011) develop a mathematical model for surface roughness while </p><p>turning tool steel using response surface methodology coupled with Taguchi design of </p><p>experiment. Taguchis S/N ratio and response surface methodology shows that the </p><p>feed rate is the most influencing parameter for surface roughness followed by depth of </p><p>cut and cutting speed has the less effect on the surface roughness. </p><p>Reddy and Valli (2011) shows the effect of process parameters on the machining of </p><p>EN-31 tool steel with copper as a tool in the rotary electrical discharge machining </p><p>process (EDM) with help of linear regression analysis and Taguchis method. </p><p>Material removal rate, tool wear ratio and surface roughness was used as response </p><p>variables. Results showed that MRR, TWR and SR was greatly influenced by peak </p><p>current. Experimental results also confirmed that simultaneous optimization of MRR, </p><p>TWR an SR was not possible for a given set of control factors. </p><p>Venkataramaiah (2011) used the Taguchis method with Grey relational analysis for </p><p>finding the optimum levels for the parameters which influence the production yield in </p></li><li><p>[13] </p><p>a foundry unit which manufactures cover plates. It is evident from the results that </p><p>there is consistency in the product quality with considerable yield. </p><p>Upadhye and Keswani (2012) used Taguchis method in sand casting process. The </p><p>parameters which were considered are moisture percentage, green compression </p><p>strength, mould hardness number and permeability. The expected improvement in </p><p>reduction in casting defects was found to be 40.82 percent. </p><p>Das et al. (2013) conducted a experimental study to investigate the effect of cutting </p><p>parameters on tool wear, surface roughness and material removal rate during the dry </p><p>turning of EN-31 steel. They also uses multiple regression analysis to develop a </p><p>relationship between cutting parameters and response variables which can be used to </p><p>estimate the values of response variables for any level of control parameters. </p><p>Modgil et al. (2012) presents a robust parameter design through Taguchis method </p><p>which has shown a breakthrough improvement in purity percentage of chemical X. </p><p>The means signal to noise ratio and standard deviation are predicted for optimal </p><p>setting and validated by producing 15 batches of inorganic chemical X with optimal </p><p>setting. </p><p>Kumar et al. (2013) uses Taguchis method and analysis of variance to study the </p><p>performance characteristics in turning operations. By using cutting speed of 150 </p><p>m/min, depth of cut 15 mm and feed rate 0.15 mm/revolution, the optimum tool wear </p><p>was found as 0.142 which is close to the experimental value of 0.156. </p><p>Ultrasound based sonication process was used for deriving the nano-crystals of </p><p>sirolimus in a narrow range. Seven critical process were selected with three levels and </p><p>optimized with Taguchis L18 orthogonal array design. Detailed statistical analysis </p><p>like t-test, regression analysis and descriptive statistics of the results have been carried </p><p>out (Gabdhi et al., 2012). </p><p>The Taguchi method was reported to alter the surface properties of commercial </p><p>Degussa P25 TiO2, which could used as visible light driven photocatalyst and was </p><p>investigated to determine the material characteristics with the use of Taguchis L9 </p><p>orthogonal array (Su et al., 2011). </p></li><li><p>[14] </p><p>Gustavo C-A(1998) used Taguchi method to optimize DNA amplification finger </p><p>printing (DAF) . Quadratic loss function penalize deviation from predicted values and </p><p>L9 and L18 orthogonal array revealed the effects and interactions of amplification </p><p>reaction components and thermal cycling parameters. Here, Taguchis method holds </p><p>potential for as an optimization tool in molecular biology. </p><p>Demirci et al. (2011), conduct a study to achieve the fatigue life parameters of </p><p>GFR/epoxy filament wound composites pipe according to ASTM D 2992. Taguchis </p><p>L9 orthogonal array and S/N ratios was used and a stress level was determined to be </p><p>the most important parameters on fatigue life among the filament angle, surface </p><p>crack-depth ratio and stress levels. </p><p>Aghakhani (2011) explains proper selection of input welding parameters is necessary </p><p>to obtain a good quality weld and subsequently increase the productivity of process. </p><p>In this method, with the help of Taguchis method and regression analysis a </p><p>mathematical model was developed using parameters such as wire feed rate, welding </p><p>voltage, nozzle to plate distance welding speed and gas flow rate on weld dilution. </p><p>Taguchis method was also applied in optimization of abrasive wear behaviour of </p><p>FeCrC coating composite (Yildiz T. and Gur A.K., 2011). The effect of parameters </p><p>levels on mean lowest wear value were analysed by ANOVA and the optimum wear </p><p>resistance value was obtained with help of Taguchis method. </p><p>Taguchis method with L9 orthogonal array was used to optimize the fabrication of </p><p>bovine serum albumin (BSA) nanoparticle. Agitation speed , initial BSA </p><p>concentration, pH and temperature were considered as control parameters and </p><p>according to the Taguchi analysis temperature and agitation speed were the most </p><p>influencing parameters on the particle size (Jahanshahi et al., 2008). </p><p>Mehravar et al. (2011) optimized the fabrication of Lactablumin nanoparticle by </p><p>applying the Taguchis method with pH, temperature and agitation speed as process </p><p>variables. The nanoparticle size at the determined condition was less than 220 nm at </p><p>the optimal condition of pH 2.5, temperature 500 </p><p>C and agitation speed 750 rpm. </p><p>Esme (2009) shows the application of Taguchis method and ANOVA in the </p><p>optimization of resistance spot welding process with electrode force, welding current, </p><p>electrode diameter and welding times as process parameters. The level of importance </p></li><li><p>[15] </p><p>of welding parameters on tensile shear strength was determined by using analysis of </p><p>variance (ANOVA). </p><p>Kim and Lee (2009) presents a systematic approach to determine the optimal process </p><p>parameters associated with hybrid welding (combination of laser beam and gas metal </p><p>arc welding) of aluminium alloy (AA5052-H32) using Taguchis method. Welding </p><p>direction, laser power, laser focus, voltage, wire feed rate, root opening balance and </p><p>travelling speed were considered as process parameters. </p><p>Dobrzanski et al. (2007) find the optimum parameters to produce Twintex (glass and </p><p>polypropylene) tubes by filament winding with fibres temperature, winding speed, </p><p>number of layers and roving as control parameters. Results show that the fibres </p><p>temperature is very significant parameter both in tensile strength and shear test. </p><p>Thakur et al. (2010) presents an experimental investigation for optimization of tensile </p><p>shear strength of resistance spot welding for galvanized steel using Taguchis method. </p><p>They used L27 orthogonal array, ANOVA and F test for determining the most </p><p>significant affecting the spot welding performance with welding current, welding </p><p>time, electrode diameter and electrode force were used as process parameters. </p><p>Taguchis method was also used to find out the optimal process parameters for an </p><p>injection moulding machine that was used to produce a consumer product (plastic </p><p>tray) from polypropylene plastic material. Orthogonal array, signal to noise ratio and </p><p>analysis of variance were employed to study the bending characteristics of tray under </p><p>constant load (Kamaruddin et al., 2004). </p><p>Rama Rao and Padmanabhan (2012) presents an experimental investigation of </p><p>electrochemical machining process of Al/5%SiC composites with voltage, feed rate </p><p>and electrolyte concentration as control factors and material removal rate as response </p><p>variable. Taguchis orthogonal array, signal to noise ratio (S/N ratio), ANOVA and </p><p>regression analysis were used to find out the optimum parameter l...</p></li></ul>


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