Top PDF A novel approach to predict surface roughness in machining operations using fuzzy set theory

A novel approach to predict surface roughness in machining operations using fuzzy set theory

A novel approach to predict surface roughness in machining operations using fuzzy set theory

adjusted and the original or a new workpiece is inserted into the machining center for machining [3] . Depending on the materials, the effect of major variables on the surface rough- ness changes signi ficantly. For instance, highly ductile materi- als tend to induce a built-up edge on the tool nose, hence creating degraded surface in terms of roughness. Very brittle materials such as cast iron also create challenging machining conditions due to fractures, which worsens the surface rough- ness. In the case of aluminum alloys, as the hardness of material increases, the surface roughness tends to improve. However, very hard materials induce vibration during the machining that generates the rough surface finish. Therefore, some widely machined common materials such as medium carbon alloy steel contain the elements that help improve the machinability. Moderately ductile yet hard materials fixed on a rigid machine tool tend to manifest the desirable machining condition, if the cutting tool materials, coolants, chip breakers and major variables are correctly applied. The major variables can be independently controlled to attain the desired surface roughness. For instance, the feed rate is usually set at a slow level to improve the surface roughness. The cutting speed is set at a rather higher level to prevent the built-up edge from occurring on the tool nose. Cutting depth is usually set to be small in order to reduce the machining vibration as well as the resistance or opposing cutting force from the materials. Over- all, a combination of slow feed rate, higher cutting speed, and small depth of cut is employed to generate the smooth surface. In this context, the main idea behind this research is that one should develop techniques to predict the surface roughness of a product before milling to evaluate the robustness of machining parameters for keeping a desired surface roughness and increasing product quality for a given set of cutting conditions, work material, tool insert type and tool geometry. It is also important that the prediction technique be accurate and reliable [4] . This can be achieved with the help of Fuzzy logic and fuzzy inference systems which are proven to be effective techniques for the identi fication, prediction and control of complex, nonlinear, and vague systems. Fuzzy logic is particularly attractive due to its ability to solve problems in the absence of accurate mathematical models [5] . The overall objective of this research was to develop an algorithm for milling operation that predicts surface roughness with designed set of conditions. Two approaches DOE and Fuzzy Logic were used where the design of experiments (DOE) is an effective approach to optimize the throughput in various manufacturing- related processes. The fractional factorial DOE and statistical
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Fuzzy Logic Approach with Weighted Compensatory Operator to Predict Surface Roughness of Hardfaced Components

Fuzzy Logic Approach with Weighted Compensatory Operator to Predict Surface Roughness of Hardfaced Components

Hard-faced parts contain irregular surface after welding process, hence it needs subsequent machining process which should be operated in controlled manner for optimum surface finishing. So from the above facts it is obvious that for utilizing the benefits of hard-facing such as reduction in cost, machine downtime and spare parts inventories, extension in equipment service life etc. a subsequent suitable machining process have to be operated in a controlled manner using soft computing techniques because for a skilled operator too it is not possible to set correct cutting parameters each time.
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GUI Based Mamdani Fuzzy Inference System Modeling To Predict Surface Roughness in Laser Machining

GUI Based Mamdani Fuzzy Inference System Modeling To Predict Surface Roughness in Laser Machining

High nu mber of simulat ion tools are being e mployed for this reasons as the method is seen to be more reliable as compared to the traditional trial and error methods. There are several Fu zzy based model that has been developed to determine machin ing para meters and responses. Wong and Hamouda [1] developed Fuzzy Expe rt System fo r machinability data sets on the web, where the results produce by the system was compared with the data fro m mach ining handbook. The error was about 0.25 to 2.41 percent. Su ley man et al. co mpared e xperimental results with the consistent fuzzy rule based model estimated values for cutting forces in turning operation [2]. In th is e xperiment, three inputs; cutting speed, feed rate and depth of cut were used to investigate the response. The model with 27 rules has obtained the prediction accuracy up to 99.6 percent. Tansel in year 2006 e mployed fuzzy logic controller for auto detection of chatter in turning operation using S-transformat ion technique [3]. Fu zzy logic approach was also used for optimizing the mach ining para meters of an inject ion mo lding to produce thin shells which we re then applied as mobile phone casings [4]. Fuzzy model was successfully used to select the best silicon crystal slic ing techniques by Doraid and Omar [ 5]. The application of Fuzzy Logic and modeling techniques are not only limited for mach ining processes , parameter selection and control, but the advancements have taken place beyond e xpectation such as in the field of environ ment manage ment where a method to capture the view of mu ltip le stakeholders is developed using fuzzy set theory and Fuzzy Logic [6]. Th is method was successfully applied for flood manage ment of Red River Basin, Minatoba, Canada. Fu zzy model was also successfully used in selecting the best logs using porosity and permeab ility data sets at Korea offshore [7]. Fuzzy logic hand writing recognizing system has been developed and used wide ly [8]. Diesel spray penetration using Fuzzy Logic modeling has been carried out by [ 9], where the model has gain the accuracy reaching a lmost 98 percent with the experimental data sets.
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Fuzzy set theory approach to model hard facing process using weighted compensatory operator

Fuzzy set theory approach to model hard facing process using weighted compensatory operator

Weld hard facing techniques are mainly used to improve the service life of engineering parts by rebuilding in such a way that it produces a metallic/alloy wall section to resist wear, faced parts contain irregular surface after welding process, hence it needs subsequent machining process which should be operated in controlled manner for optimum surface finishing. So from the above facts it is obvious that for facing such as reduction in cost, machine downtime and spare parts inventories, extension in equipment service life etc. a subsequent suitable machining process have to be operated in a controlled manner using soft computing techniques because for a skilled operator too it is not possible to set correct cutting parameters each time.Typically, selected cutting operations have limited of attaining the required surface roughness. However, it is necessary to determine optimal cutting parameters in order to achieve minimal expenses or minimal cost/production time. Different methods for prediction of optimal cutting parameters have been done by many linearity of the cutting parameters, tool work combination and rigidity of machine tool, soft computing (Neural network, Fuzzy logic, Genetic algorithm and their hybridizations) is used for prediction of important (Grzesik and Brol, 2003; Ho et Mainsah and Ndumu, 1998). In this paper fuzzy logic technique using compensatory operator has been used to find
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Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach

Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach

2 %. The cross-sectioned surface shows deposition of a recast layer on the machined surface. This EDM charac- teristic shows the superimposition of craters due to metal evaporation during machining particles of differ- ent size melted and re-solidified on the surface which affects the surface properties of the component. The recast layer was measured for a varying pulse on time. It shows that the recast layer melting and deposition rate is increasing with an increase pulse on time duration. From Fig. 4a – c, the layer deposition is of the average of 7.2, 12.15, and 16.16 μ m, respectively. Indeed, the damage of surface consists of recast layer zone and heat-affected zone which was found to be limited in its thickness and could not recognize easily. Hence, the depth of damaged zone mea- sured from the extreme surface to the end of the recast zone. The recast layer zone increased with increase in pulse on time. This is due to the high discharge energy melting more material which is not flushed away from the surface caused by the less thermal conductivity of D2 steel at lower heat
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Neural Network Modeling and Prediction  of Surface Roughness in Machining Aluminum Alloys

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

The MLP and RBF network models must be trained first using a set of training data. Then, the prediction accu- racy of the two models can be validated using a set of test data. In the present study, all training and test data were generated from real-world machining experiments, rather than from virtual computer simulation experi- ments. A total of 45 sets of training and test data were generated, with each set including 13 data points, i.e., the eight in-puts and five outputs described in Section 2.1. Among these 45 sets, 38 sets (84%) were randomly se- lected as training data, and the remaining 7 sets (16%) were used as test data. The following two sub-sections describe the experimental set-up and measurements that were involved in data generation.
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Neutrosophic Set is a Generalization of Intuitionistic Fuzzy Set,   Inconsistent Intuitionistic Fuzzy Set (Picture Fuzzy Set, Ternary Fuzzy Set),  Pythagorean Fuzzy Set, q-Rung Orthopair Fuzzy Set, Spherical Fuzzy Set,   and n-HyperSpherical Fuzzy Set,  w

Neutrosophic Set is a Generalization of Intuitionistic Fuzzy Set, Inconsistent Intuitionistic Fuzzy Set (Picture Fuzzy Set, Ternary Fuzzy Set), Pythagorean Fuzzy Set, q-Rung Orthopair Fuzzy Set, Spherical Fuzzy Set, and n-HyperSpherical Fuzzy Set, while Neutrosophication is a Generalization of Regret Theory, Grey System Theory, and Three-Ways Decision (revisited)

Set (IFS) no matter if the sum of single-valued neutrosophic components is < 1, or > 1, or = 1. For the case when the sum of components is 1 (as in IFS), after applying the neutrosophic aggregation operators one gets a different result from that of applying the intuitionistic fuzzy operators, since the intuitionistic fuzzy operators ignore the indeterminacy, while the neutrosophic aggregation operators take into consideration the indeterminacy at the same level as truth-membership and falsehood-nonmembership are taken. NS is also more flexible and effective because it handles, besides independent components, also partially independent and partially dependent components, while IFS cannot deal with these. Since there are many types of indeterminacies in our world, we can construct different approaches to various neutrosophic concepts.
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Fuzzy Measure: A Fuzzy Set Theoretic Approach

Fuzzy Measure: A Fuzzy Set Theoretic Approach

Classical measure theory [3] is studied based on additive property. But, in many real time applications like fuzzy logic [2], artificial intelligence, decision making, data mining and re-identification problem etc. require the non-additive measure. In this situation, fuzzy measure was introduced by Sugeno [4] which is a non additive measure. Fuzzy measure was discussed by several authors [4, 6, 7, 8, 9,10,12] in different manner.

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Assessment of Surface Roughness and Material Removal Rate on Machining of TIB2 Reinforced Aluminum 6063 Composites: A Taguchi’s Approach

Assessment of Surface Roughness and Material Removal Rate on Machining of TIB2 Reinforced Aluminum 6063 Composites: A Taguchi’s Approach

Experimental design is a statistical technique that enables an investigator to conduct realistic experiments, analyze data efficiently, and draw meaningful conclusions from the analysis. The aim of scientific research is usually to show the statistical significance of an effect that a particular factor (input parameter) exerts on the dependent variable (output/response) of interest. Specifically, the goal of DOE is to identify the optimum settings for the different factors that affect the production process. The primary reason for using statistically designed experiments is to obtain maximum information from minimum amount of resources being employed. An experiment (also called run) may be defined as a test in which purposeful changes are made to the input variables of a process so that the possible reasons for the changes in the output/response could be identified. The experimental strategy frequently practiced by the industries is one factor at-a time approach in which the experiments are carried out by varying one input factor and keeping the other input factors constant. This approach fails to analyze the combined effect, when all the input factors vary together which simultaneously govern the experimental response. A well designed experiment is important because the results and conclusions that can be drawn from the experimental response depend to a large extent on the manner in which data were collected.
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Skidding Machines Allocation (SMA) Using Fuzzy Set Theory

Skidding Machines Allocation (SMA) Using Fuzzy Set Theory

U~inkovito planiranje rada jedno je od glavnih zadataka u gospodarenju {umama, posebice kada postoje}a sredstva za rad nisu jednako dostupna u svim radnim jedinicama. Smanjenje ukupnih tro{kova rada svakako je bitna sastavnica gospodarenja {umama posebice u radno nedostupnim ili te`e dostupnim podru~jima. S obzirom na visoke tro{kove rada specijaliziranih {umskih vozila, u ovom slu~aju skidera, potrebno je smanjiti jedini~ne tro{kove pridobivanja drva odgovaraju}im planiranjem rada skidera te njihovim smje{tanjem u prostoru (Skidding Machines Allocation – SMA). Neizrazita teorija (fuzzy set theory) i sustav za potporu odlu~ivanja primijenjeni su za obradu nestalnih varijabli te raspona podataka kao {to su obujam obloga drva i terenski uvjeti radili{ta, tj. udaljenost privla~enja drva. Svrha je ovoga istra`ivanja prikazati sustav za potporu pri odlu~ivanju kojim bi se utvrdilo ekonomski najisplativije podru~je rada pojedinih vrsta skidera, {to }e u kona~nici smanjiti ukupne tro{kove te pove}ati dobit u pridobivanju drva. Da bi se postigao taj cilj, istra`ivano je podru~je podijeljeno u radne jedinice od 75 m {irine i duljine od 200 m do 900 m. Unutar svake radne jedinice matemati~ki su izra~unati modeli proizvodnosti te je procijenjeno vrijeme radnoga ciklusa i tro{ak strojnoga rada vozila koji su zajedno sa srednjom udaljenosti privla~enja drva (SD), obujmom tovara (VLC) te brojem komada obloga drva u tovaru (NLC) smatrani kao ulazne varijable modela SMA. Razvijena su tri odvojena neizrazita sustava za predvi|anje jedini~nih tro{kova privla~enja drva po radnim jedinicama koji su omogu}ili ekonomski najisplativije smje{tanje vozila u prostoru. U neizrazitom sustavu ulazni su podaci pretvoreni u jezi~ne varijable i tada se odre|uje stupanj pripadnosti varijable prema postavljenim pravilima. Primijenjena je Madamijeva metoda najmanjih i najve}ih vrijednosti koja za svako pravilo odre|uje stupanj pripadnosti varijable pojedinomu zaklju~ku, {to u kona~nici dovodi do stvaranja neizrazitoga skupa podataka odre|enoga stupnjem pripadnosti pojedine varijable. Modeli su primijenjeni na tri naj~e{}e kori{tena skidera u iranskom {umarstvu: Timberjack 450C, HSM 904 i Zetor, u planinskim {umama provincije Mazandaran.
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Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

In addition, the figures depict that the fuzzy-rule based and fuzzy local enhancement produced the overenhanced regions mostly at the center of the images. The fuzzy local enhancement has increased the contrast in local neighbourhoods in the images and revealed the fine details in the image. Furthermore, the fuzzy local enhancement able to avoid intensity saturation as compared to the fuzzy rule- based technique. The fuzzy rule-based technique caused the intensities saturations near Man 1’s face and Woman’s hair in Figures 1 (e) and 2(e) respectively. Thus, enhancement of those images may contain additional noises due to the unnecessary saturation.
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Senstivity Analysis of Project Scheduling using Fuzzy Set Theory

Senstivity Analysis of Project Scheduling using Fuzzy Set Theory

----------------------------------------------------------------------***------------------------------------------------------------------------- ABSTRACT: - This paper describes an application of fuzzy logic in analysis of delays in construction projects using Fuzzy set linguistic values as well as membership values. There are wide range of factor which effect construction project. In this paper we use the fuzzy logic to estimate delay by using weather good(g), medium(m), bad(b) and labor experience high(h), medium(m), low(l). we know that project scheduling use either probabilistic method or deterministic method for measuring schedule duration time. These membership values help us in determining the total effect on the duration of project due to different reason in construction project. This method helps us to understand the total delay through critical path method and program evaluation and review technique. One of the main advantages of the proposed technique is that it can be easily implemented in existing computer programs for project scheduling. We know that some of the activity are delay due to some external factor, which are considered by the software earlier using critical path method using fuzzy logic.
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SCHEDULE COMPRESSION USING FUZZY SET THEORY AND CONTRACTORS JUDGMENT

SCHEDULE COMPRESSION USING FUZZY SET THEORY AND CONTRACTORS JUDGMENT

An alternative approach to deterministic and probabilistic methods is fuzzy set theory (FST). Fuzzy set theory, pioneered by Zadeh (1978), is useful for representing and modeling uncertainties, particularly in absence of historical data. Compared to simulation, modeling uncertainty using fuzzy set theory is computationally simpler, not very sensitive to moderate changes in the shapes of input distributions, and does not require the analyst to assume particular correlations among inputs (Shaheen et al., 2007). Fuzzy set theory has been used in the development of many applications in construction engineering including; pricing construction risk (Paek, 1993); project network schedule (Lorterapong and Moselhi, 1996); reliability assessment (Booker and Singpurwalla, 2002); and range cost estimating (Shaheen et al., 2007). As to schedule compression models, fuzzy set theory is used along with simulation and/or genetic algorithm (e.g., Zheng and Thomas Ng, 2005; Eshtehardian, 2008a; Eshtehardian, 2008 b). The later methods consider only cost in the schedule compression process. This paper presents a multi-objective method to circumvent the above stated limitations in schedule compression of construction projects. The developed method accounts for cost, contractors’ judgment and for the uncertainties associated with the direct cost of crashing activity durations. The use of FST, as presented in this paper, is particularly suited for the problem at hand due to two main reasons. Firstly, crashing durations of project activities is frequently carried out during construction subjected to the unique conditions of each project environment. As such, there is no historical data for each and every activity being considered for crashing. This is contrary to cost planning of the original projects’ scope of work prior to construction; where historical data may exist to support the use of probabilistic methods. Secondly, FST facilitates the direct utilization of expert knowledge that applies to the unique conditions of each project at hand through the use of membership functions that best suit these unique conditions.
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Optimization of Milling Parameters for Minimizing Surface Roughness Using Taguchi‘s Approach

Optimization of Milling Parameters for Minimizing Surface Roughness Using Taguchi‘s Approach

The experiments were conducted on CNC vertical milling machine, Agni, BMV45 TC24, with FANUC controller while machining AISI 1040 MS material. For designing the experiments, four factors viz. spindle speed, feed rate, depth of cut and coolant flow, and three levels of each factor were considered as shown in table 1. L9 orthogonal array was used for milling the work piece using carbide inserts at different combinations in random order. The coolant used is Blastocut 500 which includes water and coolant in 80:20 ratio be volume. The surface roughness (Ra) was measured on Surface roughness tester SV514, Mitutoyo. The optimal results were verified with the help of analysis of variance (ANOVA).
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Efficient Data Mining With The Help Of Fuzzy Set Operations

Efficient Data Mining With The Help Of Fuzzy Set Operations

In this paper [1], author has Frequent weighted itemsets speak to connections much of the time holding in data in which things might weight in an unexpected way. In any case, in a few settings, e.g., when the need is to minimize a specific cost capacity, finding uncommon data relationships is more intriguing than mining continuous ones. This paper handles the issue of finding infrequent and weighted itemsets, i.e., the rare weighted itemset (IWI) mining issue. Two novel quality measures are proposed to drive the IWI mining process. Moreover, two algorithm that perform IWI and Minimal IWI mining efficiently, determined by the proposed measures, are displayed. Trial results show efficiency and viability of the proposed approach.
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Surface Morphology Analysis Using Fractal Theory in Micro Electrical Discharge Machining

Surface Morphology Analysis Using Fractal Theory in Micro Electrical Discharge Machining

Figure 1 shows that different surface morphologies are ob- tained by different machining methods, and the surface mor- phology obtained by traditional grinding has a clear texture, but that machined by micro EDM does not possess this prop- erty due to the randomness of the discharge. Figure 2 shows the Ra and RMS of the high-speed steel machined using mi- cro EDM, and it fails to establish the relationship between the discharging energy and the surface morphology. Hence, it is hard to evaluate the surface morphology resulting from micro EDM processes using traditional evaluation methods such as the Ra and RMS. As the surface morphology machined by micro EDM has fractal characters, the newly proposed meth- od in this study can serve as a new tool showing good accura- cy and concision in evaluating these types of surfaces. There- fore, the traditional evaluation method seems unsuitable when the surface morphology includes craters, peaks, blow- holes, and cracks, and they are the result of the high energy
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Predicting Rainfall Using the Principles of Fuzzy Set Theory and Reliability Analysis

Predicting Rainfall Using the Principles of Fuzzy Set Theory and Reliability Analysis

pared non-linear regression modeling and fuzzy knowl- edge-based modeling, and explained that fuzzy models were most appropriate when subjective and qualitative data were utilized and the numbers of empirical observa- tions were small. Brown-Brandl et al. [5] used four mod- eling techniques to predict respiration rate as an indicator of stress in livestock. Four modeling techniques con- sisted of two multiple regression and two fuzzy inference systems. Fuzzy inference models offered better results than the two multiple regression models (Brown-Brandl et al. [5]). Fuzzy inference models yielded a lower per- centage of error when compared to the linear multiple regression model (Hasan et al., [2]). Similar research by Wong et al. [6] compared the results of fuzzy rule based rainfall prediction with an established method which used radial basis function networks and orographic effect. They concluded that fuzzy rule based methods could pro- vide similar results from the established method. How- ever, the method has an advantage of allowing the ana- lyst to understand and interact with the model using fuz- zy rules. Lee et al. [7] considered two smaller areas where they assumed precipitation was proportional to ele- vation. Predictions of those two areas were made using a simple linear regression based on elevation information only. Comparison with the observed data revealed that the radial basis function (RBF) network produced better results than the linear regression models. Hence, consid- ering the advantage of using the concept of fuzzy logic for predicting rainfall as stated by other researchers was justifiable. The advantage of fuzzy inference modeling can reflect expert knowledge and yield results with pre- cision and accuracy. In fuzzy rule basics, knowledge acquisition is the main concern for building an expert system. Knowledge in the form of IF-THEN rules can be provided by experts or can be extracted from data. Each rule has an antecedent part and a consequent part. The antecedent part is the collection of conditions connected by AND, OR, NOT logic operators and the consequent part represents its action (Pant and Ashwagosh [8]). In a fuzzy inference engine, the truth-value for the premise of each rule is computed and applied to the conclusion part of each rule. This result is one fuzzy subset being as- signed to each output variable for each rule. For compos- ite rules, usually, min-max inference technique is used.
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New operations for interval-valued Pythagorean fuzzy set

New operations for interval-valued Pythagorean fuzzy set

in detail. Meanwhile, they studied two interval-valued Pythagorean fuzzy aggregation operators for integrat- ing the interval-valued Pythagorean fuzzy information, such as IVPFWA and IVPFWG operators, and pre- sented an interval-valued Pythagorean fuzzy elimina- tion and choice translating reality method (ELEC- TRE) to solve Multi-Criteria Group Decision Making (MCGDM) problem with uncertainty. Liang et al. [17] conceived the maximizing deviation method based on interval-valued Pythagorean fuzzy weighted aggregat- ing operator for MCGDM problem. Garg [36] intro- duced a novel interval-valued Pythagorean fuzzy accu- racy function for solving MCDM problem. Rahman et al. [37] discussed interval-valued Pythagorean fuzzy ge- ometric aggregation operators and their application to MCGDM problem. Garg [38] proposed a new improved score function of an interval-valued Pythagorean fuzzy set-based TOPSIS method. Chen [39] pioneered the IVPF outranking algorithm with a closeness-based assignment model for MCDM.
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Modeling and prediction of mrr and surface roughness in turning operations using factorial method and regression method

Modeling and prediction of mrr and surface roughness in turning operations using factorial method and regression method

Surface roughness, which is used to determine and to evaluate the quality of a product, a measure of the technological quality of a product and a factor that greatly influences manufacturing cost. It describes the geometry of the machined surfaces and combined with the surface texture. Barry and Byrne (2001) investigated the mechanism of AL2O3/TiC cutting tool wear in the finish turning of hardened steels with particular cognizance of the work material inclusion content. The rate of tool wear appears to be determined by the hard inclusion contend or alloy carbide content of the work material. a new mechanism is proposed to account for the superior wear resistance of CBN/TiC composites in comparison to high- content CBN tools in the finish machining of 4340 (AISI) steel of 52 HRC. Hassan, K. et al. (Hassan, 2012) has done the experimental investigation of material removal rate (MRR) in cnc turning of C34000 using taguchi method using L‟27 array. When the MRR is optimized alone the MRR comes out to be 8.91. The optimum levels of process parameters for simultaneous optimization of MRR have been identified. Optimal results were verified through confirmation experiments. It was concluded that MRR is mainly affected by cutting speed and feed rate. Jenn-Tsong et al. (Jenn-Tsong, 2008) developed RSM model using CCD in the hard turning using uncoated Al2O3/TiC mixed ceramics tool for flank wear and surface roughness. Flank wear was influenced principally by the cutting speed and the interaction effect of feed rate with nose radius of tool. The cutting speed and the tool corner
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Influence of cutting parameters on tool 
		wear and surface roughness in machining SS304 using carbide and ceramic 
		inserts

Influence of cutting parameters on tool wear and surface roughness in machining SS304 using carbide and ceramic inserts

Figure-4 shows the deviation of surface finish with cutting speed. It is evident from Figure-4 that for carbide tool surface roughness decreases with greater cutting speeds for depth of cut 0.5mm and feed rate 0.015mm/rev as compared to ceramic tool where there is an abrupt increase in roughness. A peak of the burr appears at a speed of around 150 m/min, which corresponds to huge lateral movement of cut metal. In event of ceramic cut hard burrs in combination with noticeable lateral movement of the uncut metal turn as abrasive at the insert border.
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