Review On Ptimization Techniques Used For Etermining Machining .

1y ago
9 Views
2 Downloads
2.35 MB
30 Pages
Last View : 26d ago
Last Download : 2m ago
Upload by : Olive Grimm
Transcription

DAAAM INTERNATIONAL SCIENTIFIC BOOK 2017pp. 235-264Chapter 19REVIEW ON OPTIMIZATION TECHNIQUES USED FOR DETERMININGMACHINING CONDITIONS TO GET EFFECTIVETOOL LIFE AND SURFACE FINISHKUMAR, S.Abstract: Manufacturing industries are growing widely with development of varioustools and techniques to manufacture products with desired quality and high accuracy.It has become extremely important for the manufacturing units to engineer andmanufacture products with high quality and also eliminate the waste in production andprocesses. Reduction of scrap and rework help in achieving the desired target as extramaterial will be required. One of the most popularly used manufacturing processes ismachining. It is the process of removing unwanted material to obtain the product ofrequired shape, size and surface finish. Improper setting of cutting condition can causedamage to the workpiece and tool, hence it is important to investigate the performanceof the machining parameters and study the effects of these parameters on the requiredresponse. Optimization of the machining parameters is an effective way to producerequired quality product with negligible scrap generation. The present paper reviewsthe application of optimization techniques like Taguchi method, regression analysis,analysis of variance (ANOVA), response surface methodology, artificial neuralnetwork (ANN), fuzzy logic, and adaptive neuro fuzzy inference system (ANFIS) inpredicting the influence of controlling factors on the required output and finding theoptimal combination of machining parameters to obtain required response.Key words: Manufacturing, Optimization, ANOVA, ANN, ANFISAuthors data: PhD. Kumar, S[atish]*; Symbiosis Institute of Technology, Baner411007, Pune India satishkumar.vc@gmail.comThis Publication has to be referred as: Kumar, S[atish] (2017). Review onOptimization Techniques used for Determining Machining Conditions to get EffectiveTool Life and Surface Finish, Chapter 19 in DAAAM International Scientific Book2017, pp.235-264, B. Katalinic (Ed.), Published by DAAAM International, ISBN 9783-902734-12-9, ISSN 1726-9687, Vienna, AustriaDOI: 10.2507/daaam.scibook.2017.19235

Kumar, S.: Review on Optimization Techniques used for Determining Machining C.1. IntroductionOptimization is the method to achieve best possible result using the availableresources effectively. In manufacturing industry, it is necessary to obtain optimumparameters for production of high quality products without generating scrap whichcauses economical loss to the firm. Various machining and geometrical parametersaffect the quality of product manufactured. Some of the parameters that are varied tocheck for the most efficient combinations include feed rate, tool material, work piecematerial, cutting speed, tool nose radius, depth of cut etc.The responses that are studied by varying these parameters are as follows:1. Estimation surface finish.2. Tool wear prediction3. Tool life prediction4. Cutting forces generated during manufacturing5. Effect of vibration on tool1. Surface finish refers to the surface texture that is attained after machining theworkpiece. Selection of improper parameters may cause roughness and waviness onthe surface of the product which leads to degradation of quality and rejection of thecomponent. Various methods are employed to examine the effects of machiningparameters on surface roughness, so the finished product has the required finish andshine without causing much of rework and scrap generation as surface roughness is oneof the significant factor that has an effect on friction. Thus, it is necessary to optimizethe process to ensure proper generation of surface finish.2. Tool wear is a phenomenon where there is a loss of tool material whileundergoing the manufacturing process causing failure of the tool. There are varioustypes of tool wear like flank wear, crater wear, edge wear etc. caused due to abrasion,adhesion, diffusion, chemical decomposition and oxidation. Increase in cutting speedand temperature improper setting of machining parameters like feed rate, depth of cutand spindle speed are some of the causes of tool wear. Tool wear prediction helps inunderstanding the pattern and the parameters that have significant effect in wearing oftool. These parameters can be optimized to decrease the wear rate of the tool.3. Tool life is the machining period which the tool undergoes before its failure.Tool life is greatly affected by machining parameters like feed rate, cutting speed anddepth of cut. Tool wear too has a substantial effect on the tool life. Tool life predictiongives an idea about how the parameters contribute to the life of the tool. Drasticchanges in some of the factors affect the tool life. Optimization of these parameters byapplying various statistical techniques assists in estimating the tool life and establishingthe optimum conditions.4. Cutting force is the force that is generated when the tool machines theworkpiece. It is affected by the cutting parameters like feed, depth of cut and cuttingspeed. Another important parameter that has a vital influence on cutting force is toolgeometry. Cutting force and tool life are inversely proportional to each other. Properlubrication is required to maintain the temperature change caused due to cutting forces.236

DAAAM INTERNATIONAL SCIENTIFIC BOOK 2017pp. 235-264Chapter 19Optimization of the parameters helps estimating the ideal conditions to limit the cuttingforce.5. Tool vibration is another phenomenon that can degrade the quality of products,produce noise while machining and shortens tool life. Improper machining conditionslike high cutting speed or high feed rate are responsible for tool vibration. Analysis ofthe machining condition helps in understanding their role and contribution to thevibration while optimization helps in obtaining the best possible combination of themachining parameters to eliminate tool vibration.2. Introduction to the methods used for optimization2.1 The Taguchi MethodTaguchi method is a statistical approach to build robustness in the process during theexperimental design stage. It was developed by Dr. Genichi Taguchi and employed in1980 at AT&T Bell laboratories. Dr. Taguchi introduced three of the importantconcepts to the world of statistics which are specific loss function, philosophy of offline control and innovation in design of experiments. Dr. Taguchi emphasized onreduction of unwanted parameters that would not add any value to the required output.Specific loss function helps in investigating the deviation of performance fromits target. For this purpose quality characteristics were specified such as smaller thebetter, nominal the better and larger the better depending upon nature of the outputparameter to develop different quality function. The loss function is given as follows饾憴 饾憳饾惛 (饾懄 饾憽)2Wherel quality loss functionk quality loss coefficientE Expected valuet Target value of yThus, any difference from the required value and the measured value wouldindicate deviation from the target value which can also be considered as an indicationto change the combination of input parameters to obtain required results. The objectivefor optimum design is to minimize the loss function. To eliminate variation, off linequality control is applied which is mostly used in manufacturing industry. It is dividedinto three stages.The first stage is system design which a conceptual stage where the concept ofproduct is created followed by the second stage known as the parameter design stagewhere the parameters are set and the detail design is developed. The last stage is thetolerance design stage where effect of process parameters on the output are studied andchanges are made accordingly to reduce variations so that the output is in requiredlimits. The main objective of tolerance design is to set the design parameters inacceptable level of variation.237

Kumar, S.: Review on Optimization Techniques used for Determining Machining C.The experimental design stage has been greatly affected by Taguchi鈥檚 method oforthogonal array. It helps in analyzing the combinations of input parameters byarranging them in orthogonal arrays depending upon the runs or the number of tests tobe conducted, process parameters controlling the output parameter and the level atwhich they should be varied to obtain optimum results.These fractional factorial experimental matrices are used to define the maineffects using only few experimental runs. In Taguchi experiments the main effect andonly two factor interactions are considered.Another important concept in Taguchi method is signal to noise ratio. It originatesfrom the communication system. It is expected that the input signal will produce outputin exactly the same manner without any disturbances or variation. The disturbances areconsidered as noise in Taguchi method which is uncontrollable factors that occurduring the experiments. One of the ideal ways for optimization is to maximize thesignal to noise ratio.The analysis of the Taguchi experimental data is done by analysis of variance(ANOVA) where the influence of considered process parameters are studied andsignificant parameter is estimated by the percentage contribution. Another dataanalysis method is the main effects chart and interaction chart, the main effect chartconsists of a plot of average response at different level factor versus the factor level.On an interaction chart all factor level combinations for two factor interactions aredisplayed. The combinations of factors that produce optimal response are consideredas optimum factors in Taguchi method.The following literature review on application of Taguchi method in variousmanufacturing processes wherein optimum results in minimum number of experimentsare obtained.Surinder Kumar et al. (2013) adopted utility concept and Taguchi method to studythe performance of machining parameters on turning of unidirectional glass fiberreinforced plastics with carbide k10 tool. The varying parameter selected were toolrake angle, feed rate, tool nose radius, depth of cut and cutting speed while theresponses selected for were material removal rate and surface roughness. Theexperiments were conducted in varying cutting atmosphere like wet, cooled and dry.The study states that optimization of one parameter lead to conflicting changes inother parameters; hence it was necessary to optimize all the parameters to obtainoptimum results. Taguchi L18 orthogonal array, utility function and S/N ratio wereused to accomplish the multiresponse optimization. Utility concept is based onconsidering all the parameters for optimization. Utility function development involvescreating a preference scale based on the optimal values of responses obtained fromTaguchi method which were assigned weight based on preferences like smaller thebetter, nominal the better and higher the better, the entire data is analyzed accordingly.It was determined using analysis of variance that depth of cut had dominant influenceon utility function followed by cutting speed and feed rate. The study concluded thatintegration of Taguchi method and utility function can be successfully applied to obtainoptimum setting of the process parameters.D. Philip Selvaraj et al.(2014) inspected the machining parameters on nitrogenalloyed duplex stainless steel for turning operation using TiCN and TiC coated carbide238

DAAAM INTERNATIONAL SCIENTIFIC BOOK 2017pp. 235-264Chapter 19cutting tool inserts. Taguchi method was employed for optimization using L9orthogonal array and signal to noise ratio. Analysis of variance was applied to examinethe effect of feed rate, depth of cut and cutting speed on surface roughness, tool wearand cutting force. Depth of cut was maintained constant while feed rate and cuttingspeed were varied to collect the experimental data. The study reported that feed rategreatly affected the surface roughness and cutting force while cutting speed had aprevailing effect on tool wear. It was concluded that high tool wear was observed onrake surface than the edge of the cutting tool, also it was validated that high cuttingspeed led to critical flank wear, rake wear and notch wear.Turgay K谋vak (2014) applied Taguchi method, analysis of variance (ANOVA)and regression analysis to predict flank wear and surface roughness of austeniticmanganese steel also known as Hadfield steel using chemical vapor deposition (CVD)TiCN/Al2O3-coated carbide inserts under dry milling conditions physical vapordeposition (PVD) TiAlN. The machining parameters selected were cutting speed,cutting tool and feed rate. Taguchi method was employed to design the experimentwhile S/N ratio were used for optimization of machining conditions. Regressionmodels were developed to estimate the surface roughness and tool wear. The ANOVAresults showed that the feed rate is the most predominant factor affecting surfaceroughness while flank wear was significantly affected by cutting speed. The study alsoproposed that CVD coated that tools produced less flank wear compared to PVD coatedtools.Meenu Gupta, Surinder Kumar (2015) developed a model to predict optimummaterial removal rate and surface roughness for turning process of unidirectional glassfiber reinforced plastics composite with polycrystalline diamond tool by using Taguchidesign of experiments, analysis of variance (ANOVA) and particle component analysis(PCA). The experimental data was obtained by means of Taguchi's L18 orthogonalarray as a result of varying parameters like tool rake angle, cutting speed, feed rate,tool nose radius, depth of cut and cutting environment (dry, wet, cooled). The PCAmethod was employed to obtain a single objective function for multivariableoptimization while ANOVA was employed to study the effect of process parameterson surface roughness and material removal rate. The study reported that materialremoval rate increases as all process parameters increase while the ANOVA resultsconfirmed that the feed rate has the utmost influence on surface roughness.Nilrudra Mandal et al. (2015) made an attempt to optimize the surface roughnessof AISI 4340 steel using turning inserts of Zirconia Toughened Alumina (ZTA) bydeveloping regression model and applying Taguchi method and ANOVA. Theexperiments were planned using L9 orthogonal array by varying parameters like depthof cut ,feed rate and cutting speed and the optimum machining conditions wereachieved using S/N ratio. Regression models were used to estimate the response whileANOVA was adapted to investigate the influence of parameters on surface roughness.The study reported that cutting speed had the most substantial effect on surfaceroughness tailed by depth of cut.Oussama Zerti et al. (2016) used Taguchi method to study the impact of feed rate,depth of cut, cutting edge angle, cutting insert nose radius and cutting speed on surfaceroughness, tangential force and cutting force under dry cutting conditions on AISI D3239

Kumar, S.: Review on Optimization Techniques used for Determining Machining C.steel using mixed ceramic inserts. Analysis of variance was applied on S/N ratios toget the dominant parameter while regression models were built to predict the responsesby controlling the various parameters. Optimum machining parameters were obtainedusing Taguchi optimatisation approach. The study determined that feed rate has themost substantial effect on surface roughness and depth of cut is the most dominatingparameter affecting tangential force and cutting force.2.2 Application of Taguchi method in manufacturing processesBased on the literature referred, Table 1 enlists the application of Taguchi method todesign experiments using orthogonal array and to find the combination of processparameters to obtain optimal response by analyzing the signal to noise ratio for variousin manufacturing processes.No.12D. PhilipSelvaraj etal.(2014)Machiningparametertool rake angle,feed rate, toolnose radius,depth of cut andcutting speedfeed rate, depthof cut andcutting 2015)5NilrudraMandal etal. (2015)OussamaZerti et al.(2016)6AuthorSurinderKumar etal. val rateand surfaceroughnessturningsurfaceroughness, toolwear andcutting forceCutting speed,cutting tool andfeed rate.Millingflank wear andsurfaceroughnesstool rake angle,cutting speed,feed rate, toolnose radius,depth of cut andcuttingenvironment(dry, wet,cooled)depth of cut,feed rate andcutting speedfeed rate, depthof cut, cuttingedge angle,cutting insertnose radius andcutting speedturningmaterialremoval rateand aceroughness,tangentialforce andcutting forceTab. 1. Application of Taguchi Method240RemarksIntegration of Taguchi method and utilityfunction approach was successful inpredicting the response accurately andoptimal conditions were determined.Cutting speed had a great influence on toolwear and feed rate had dominating effecton surface roughness and cutting force.High tool wear was detected on rakesurface than on the cutting edgeOptimization using S/N ratio and provideeffective results. It was observed CVDcoated tool perform better than PVDcoated toolOptimized machining conditions werefound using Taguchi method and particlecomponent analysis. ANOVA resultsdepicted feed rate has the utmost influenceon surface roughnessIt was observed that cutting speed hadconsiderable effect on surface roughnessfollowed by depth of cutAnalysis of S/N ratio showed that feed ratehas the most substantial effect on surfaceroughness and depth of cut is the mostdominating parameter affecting tangentialforce and cutting force. Taguchi methodwas effective in obtaining optimalparameters.

DAAAM INTERNATIONAL SCIENTIFIC BOOK 2017pp. 235-264Chapter 192.3 Response Surface methodologyResponse Surface Methodology is a collection of mathematical and statistical tools tofind the optimum response influenced by several independent variables. RSM helps inbuilding an empirical model by taking into consideration the input parameters and therequired response. A function is developed based on the experimental data andregression analysis which is represented by the following equation,y f(x1, x2, ,xm) 系wherey responsem number of independent variables or factors influencing responsef is the response surface function that which gives the relation between variousindependent variables like x1, x2 etc. and response系 errorThe response can be represented as contour plots for better understanding of theeffects of input parameters where contour lines represent the response pertaining to itsrespective input. The optimization depends on the nature of the response; like in somecases it is better if the response is larger. For example in case of tool life predictioncombination of such input parameters would be selected that would give maximumtool life while in some cases it is preferred if response is smaller like in finding theoptimum parameters for surface roughness and wear rate, the best combination ofparameters would be those that would yield minimum surface roughness and wear raterespectively. For some cases it is better if it is achieved what is exactly required, inother words, nominal the best.First order and second order models are employed depending upon the location ofthe current value of the response; if the value obtained is not close to the optimum thenthe first order model is applied where steepest ascent or descent method is useddepending on the nature of response to move the value close to the optimum value.Once it is close to the optimum point second order model is used to find the accurateoptimal response. The first order model when the function is linear is given as饾憣 饾浗0 饾浗1 饾憢1 饾浗2 饾憢2 饾浗饾憲 饾憢饾憲Where,尾 regression coefficientX main effect parametersY response to be predictedWhile the second order equation is used when interaction is considered between thevariables and the function is quadratic and is given by,241

Kumar, S.: Review on Optimization Techniques used for Determining Machining C.饾憳饾憳Y 尾0 饾浗饾憴 饾憢饾憴 饾浗饾憴饾憴 饾憢饾憴2 饾浗饾憴饾憵 饾憢饾憴 饾憢饾憵 系饾憴 1饾憴 1饾憴饾憵Where,尾 regression coefficientX main effect parameterY response to be predictedX (l ) X m interaction between the parameters系 errorAnother important method that has been applied in many research works is thedesirability function approach. It is used in optimization of multi response surfaces. Adesirability function is used to indicate the desirable values by assigning the value suchas 0 and 1 for the most undesirable value and the most desirable value respectively.Individual desirability function are developed depending upon the optimization criteriawhich are similar to the nature of response mentioned earlier. Higher desirabilityindicates higher contentment in obtained values.The following literature review would provide a broader view on the applicationof RSM in finding the optimum response value in various manufacturing processes.Ashvin J. Makadia et al. (2013) applied design of experiments to investigate theeffects of cutting parameters on AISI 40 steel with ceramic inserts for turningoperations. The cutting parameters selected to examine the effect on surface roughnesswere feed rate, tool nose radius, cutting speed and depth of cut using Response SurfaceMethodology. A 3 level factorial design was planned and ANOVA was applied to studythe effects of cutting parameters. The study reported that feed rate had the mostinfluencing effect on surface roughness followed by tool nose radius and cutting speed.The interaction between tool nose radius and feed rate too had significant effect.Optimum machining conditions were obtained using response surface optimization.S. Gopalakannan and T. Senthilvelan(2014) investigated the effect of gap voltage,pulse on and off time and pulse current on electrode wear ratio (EWR), surfaceroughness (SR) and material removal rate (MRR) for operations on ElectricalDischarge Machine. The work piece was prepared by reinforcing SiC particles toAluminum to obtain Aluminum 7075 (Al-Zn-Mg-Cu) alloy. RSM and Analysis ofvariance were applied to examine the effect of parameters on metal removal rate,surface roughness and electrode wear rate. The results reported through RSM andANOVA indicate that pulse current was the most influential parameter which hadeffect on all the responses while the optimal machining combinations were obtainedusing desirability approach. Figure 1 (a) and (b) shows the SEM micrograph of Siliconcarbide particle distribution and the machined surface taken during the research workby the authors.242

DAAAM INTERNATIONAL SCIENTIFIC BOOK 2017a)pp. 235-264Chapter 19b)Fig. 1. SEM micrograph of (a) SiC particle distribution and (b) crack of electricaldischarged machined surface with dislodged SiC particlesBhuvnesh Bhardwaj et al. (2014) developed two empirical model to predictsurface roughness of EN 353 steel using carbide inserts for end milling operation.ANOVA was used for significance testing of both quadratic models while only onemodel was developed applying Box Cox transformation. The parameters selected werecutting speed, depth of cut, feed rate and tool nose radius. The results indicated thatcutting speed was the most dominant factor followed by nose radius. The studyconcluded that model obtained using a Box Cox transformation has a better predictioncapability than the other model.M. Kamruzzaman et al. (2016) investigated the effect of feed rate, cutting speed,tool material, machining environment and work piece material on chip tool interfacetemperature by applying Artificial Neural Network techniques, Response surfacemethodology and ANOVA to determine the significant parameters. The workpiecematerial used were C-60, 17CrNiMo4, and 42CrMo4 steel alloys and standard carbideinserts like SNMG and SNMM were tools used while machining. Dry machiningcondition and high pressure coolant machining conditions were employed forinvestigation for interface temperature. The design of experiment was conductedthrough full factorial design while the response (tool chip interface temperature waspredicted through RSM and ANN). According to ANOVA results cutting speed had adominant effect on interface temperature followed by environment conditions. Thestudy concluded that HPC assisted machining provides improved tool life. Figure 2 (a),(b) and (c) represent the flank wear at dry, wet and high pressure coolant conditionrespectively.a)b)c)Fig. 2 Images of the worn out edges of the carbide tool at 48 min in (a) dry (b) wet and(c) HPC condition243

Kumar, S.: Review on Optimization Techniques used for Determining Machining C.K. Venkata Rao et al. (2016) applied, Artificial Neural network, Response SurfaceMethodology (RSM) and Support Vector Regression Method and ANOVA to studythe effect of feed rate, spindle speed and tool nose radius on tool vibration and surfaceroughness to obtain optimum surface roughness and tool vibration by employing multiresponse optimization. The experiments conducted were used to find the relationshipbetween input and output parameters using RSM, ANN and SVR for AISI 316 is astainless steel using carbide coated tool inserts for boring operation. A feed forwardalgorithm was employed for ANN. The results for tool vibration showed that thecutting speed were significant parameter while the interaction between nose radius andfeed rate substantially affected the surface roughness. The study concluded that ANNand SVR had higher predictability than Response Surface Methodology.Priyabrata Sahoo et al. (2017) developed an empirical model using weightedprincipal component analysis (WPCA) and response surface methodology (RSM) toinvestigate the effect of feed rate, depth of cut and spindle speed on tool vibration andsurface roughness on AluminumAlloy using coated carbide tool inserts in dry environment for turning process.Box-Behnken methodology was employed to RSM model was used to optimize theresponse that is influenced by various process parameters and WPCA was employedto achieve the better combination of process parameters to obtain optimum machiningconditions. The results of Analysis of variance (ANOVA) showed that feed rate hadgreat influence on surface roughness while the second order term of spindle speed wasthe dominant parameter in tool vibration. The study concluded that the interactioneffect of feed rate and spindle speed played a vital role in tool vibration.2.4 Application of RSM in Manufacturing ProcessesIn the referred research papers, response surface methodology was used to obtainoptimum response by finding developing models to obtain optimum response. Table 2lists the application of response surface methodology to depict optimum machiningconditions for manufacturing processes.No.123244AuthorAshvinJ.Makadia etal. (2013)S.Gopalakannan and T.Senthilvelan(2014)BhuvneshBhardwaj etal. (2014)MachiningParametersfeed rate, toolnoseradius,cutting speedand depth of cutgapvoltage,pulse on and offtime and pulsecurrentcutting speed,depth of cut,feed rate andtool nose dMillingMachiningperformanceSurfaceRoughnessmetal removalrate,surfaceroughness andelectrode wearratesurfaceroughnessRemarksRSM precisely depicted the optimummachining conditions. ANOVA resultssuggested that feed rate had the mostinfluencing effect on surface roughnessfollowed by tool nose radius and cuttingspeed.Optimum machining conditions wereattained using desirability approach andpulse rate was the dominating parameteraffecting all the responsesBox Cox transformation was used to developone of the two models used in predicting theresponse. The predictability of the box coxtransformation was higher than the othermodel.

DAAAM INTERNATIONAL SCIENTIFIC BOOK 20174M.Kamruzzaman et al.(2016)5K. VenkataRao et al.(2016)6PriyabrataSahoo et al.(2017)feedrate,cutting speed,tool material,machiningenvironmentand work piecematerialfeedrate,spindle speedand tool noseradiusfeed rate, depthof cut andspindle speedTab. 2. Application of RSMpp. 235-264Chapter 19TurningchiptoolinterfacetemperatureANOVA results suggested cutting speed hada dominant effect on interface temperaturefollowed by environment conditions. Toollife can be improved using high nessThe predictability of ANN and supportvector regression model was found to behigher than response surface sRSM and WPCA were included foroptimization of process parameters. It wasobserved that feed rate had great influence onsurface roughness while the second orderterm of spindle speed was the dominantparameter in tool vibration2.5 Regression AnalysisRegression analysis is a statistical method to establish a relationship between variables.The most important parameter of regression model building is collection of datathrough any medium such as data obtained through designed experiment, throughobservation or through surveys. It is clearly evident that any regression analysis is onlyas good as the data that it is based on. Regression helps in converting a large amountof data collected into an equation, describing the relationship between input and outputparameters. It is used in prediction and estimation of response variable and control ofinfluencing variables to obtain the required output value.There are two types of regression models that are widely used namely simplelinear regression model and multi regression model. Simple regression model is usedwhen only one control variable is affecting the response while multi regression modelsare used when more than one control variables affect the response.Regression models are popularly used to estimate the mean response for aparticular value of control variable x. Method of least squares is used in both themethods for estimating the regression coefficient using t

3. Tool life is the machining period which the tool undergoes before its failure. Tool life is greatly affected by machining parameters like feed rate, cutting speed and depth of cut. Tool wear too has a substantial effect on the tool life. Tool life prediction gives an idea about how the parameters contribute to the life of the tool. Drastic

Related Documents:

1 EOC Review Unit EOC Review Unit Table of Contents LEFT RIGHT Table of Contents 1 REVIEW Intro 2 REVIEW Intro 3 REVIEW Success Starters 4 REVIEW Success Starters 5 REVIEW Success Starters 6 REVIEW Outline 7 REVIEW Outline 8 REVIEW Outline 9 Step 3: Vocab 10 Step 4: Branch Breakdown 11 Step 6 Choice 12 Step 5: Checks and Balances 13 Step 8: Vocab 14 Step 7: Constitution 15

Image restoration techniques are used to make the corrupted image as similar as that of the original image. Figure.3 shows classification of restoration techniques. Basically, restoration techniques are classified into blind restoration techniques and non-blind restoration techniques [15]. Non-blind restoration techniques

JFET, an analysis of the influence of various types on electrical of penetrating radiation characteristics is carried out. ptimization calculationsO gave the modes of processing procedure, which reduce the effect of the neutron flux with energy 1.5 MeV on the electrical characteristics of n-JFET device structure by 1.45 times.

ao Firozjaii AM Moradi S(218) Sensitivity Analysis and ptimization of the Effective parameters on ASP Flooding Compared to Polymer Flooding sing CM-STARS. Pet Environ iotechnol : 361. doi: 1.41722157-7463.1 361 Page 2 of 5 oe e 3 Pe o oeo a oe ae oa 2543 polymer are injected to the reservoir. Alkaline firstly reacts with an acid

ERP customization concerns modification of out -of - the -box functionality of ERP systems using various tools provided. It is performed in the customization and implementation phases and is aimed at reducing gaps between the required and provided functionality. Aslam et al. (2012) summarize several typologies of customizations i n ERP systems.

the public鈥損rivate partnership law review the real estate law review the real estate m&a and private equity review the renewable energy law review the restructuring review the securities litigation review the shareholder rights and activism review the shipping law review the sports law review the tax disputes and litigation review

2.1 A Review of Credit Scoring Techniques . Credit Scoring techniques are divided into two parts . i. Statistical Based Techniques ii. Artificial Intelligence Based Techniques . 2.1.1 Statistical Based Techniques . Various credit scoring statistical based techniques have been researched on and implemented. These statistical based tech-

Astrophysics combines our knowledge of light, chemical reactions, atoms, energy, and physical motion all into one. The things we鈥檙e going to study in this unit borders on sci-fi weird, but I assure you it鈥檚 all the same stuff real scientists are studying. This unit is broken into three completely separate sections that you can to in any order. Some are easy-and-fun and others are mind .