Optimisation Technique for Face Milling Stainless Steel with Multiple Performance Characteristics

Lin, Tsann-Rong

doi:10.1007/s001700200021

Cited by 89 publications

(41 citation statements)

References 10 publications

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“…The classical experimental design methods are overly complex and not easy to apply. Additionally, when the number of machining parameters increases a large number of experiments are required [15]. Therefore, the factors instigating variations should be determined and checked under laboratory environments.…”

Section: Experimental Designmentioning

confidence: 99%

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Aherwar¹,

Unune²,

Pathri³

et al. 2014

IJMECH

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Section: Experimental Designmentioning

confidence: 99%

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Aherwar¹,

Unune²,

Pathri³

et al. 2014

IJMECH

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“…With the highest speed, lowest feed rate and highest radial rake angle of the cutting conditions scale, the GA technique recommends the best minimum surface roughness value. For end milling also, to modeling surface roughness different tools are used like RSM (Alauddin et al 1996;Mansour and Abdalla 2002;Wang and Chang 2004;Oktem et al 2005;Reddy and Rao 2005;Reddy and Rao 2006b;Routara et al 2009), Taguchi analysis (Yang and Chen 2001;Lin 2002;Ghani et al 2004;Bagci and Aykut 2006), ANN (Tsai et al 1999;Balic and Korosec 2002;Benardos and Vosniakos 2002;El-Sonbaty et al 2008;Öktem 2009;Zain et al 2010b). From the literature survey, it is seen that most of literatures deal with conventional roughness parameters to describe surface roughness and also in the study, three machining parameters viz.…”

Section: Review Of Roughness Study In Machiningmentioning

confidence: 98%

“…presented a method for the simulation of surface roughness of the machined surface in high-speed end milling. Lin (2002) has optimized cutting speed, feed rate and depth of cut with consideration of multiple performance characteristics including removed volume, surface roughness and burr height in face milling of stainless steel and shown that the most influence of the cutting parameters is the feed rate. Mansour and Abdalla (2002) have concluded that with increase in feed rate or in axial depth of cut, surface roughness increases whilst with increase in cutting speed, surface roughness decreases in end milling operations of EN32 materials.…”

Section: Review Of Roughness Study In Machiningmentioning

confidence: 99%

Fundamental Consideration

Sahoo¹,

Barman²,

Davim³

2011

Fractal Analysis in Machining

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The importance and usefulness of fractal dimension in describing surface roughness over the conventional roughness parameters are presented in this chapter. The fundamental of fractal dimension and the methodology for evaluation of fractal dimension are also discussed. Literature survey is carried out for four different types of machining processes and shows that there is scarcity of literatures which deal with fractal description of surface roughness. Fundamentals of design of experiments and response surface methodology are also discussed. IntroductionSurfaces are irregular though they may look like very smooth. When the surfaces are magnified, the irregularities become prominent. This is true for the machining surfaces as well. In a material removal process such as machining, unwanted material is removed and altered surface topography is obtained. The surface generated consists of inherent irregularities left by the cutting tool, which are commonly defined as surface roughness. Such a surface is composed of a large number of length scales of superimposed roughness that are generally characterized by the standard deviation of surface peaks. Three statistical characteristics are generally used to describe the structure of machined surface topography: texture, waviness and roughness. The texture determines the anisotropic property of the surface. The waviness reflects the reference profile (or surface). The surface roughness is formed by the micro deformation during the machining process.Surface roughness plays an important role. It has large impact on the mechanical properties like fatigue behavior, corrosion resistance, creep life, etc.It also affects other functional attributes of machine components like friction, wear, light reflection, heat transmission, lubrication, electrical conductivity, etc. Surface roughness may depend on various factors like machining parameters, work-piece materials, cutting tool properties, cutting phenomenon, etc. In a review P. Sahoo et al.,

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“…Step No.1: Normalizing or data preprocessing would be the initial step conducted to normalize (transforming measured units into dimensionless parameters) the test results on the scale of zero and one due to the series of the degree of the process factors are different (Cavaliere et al 2008;Lin 2002). In this grey relational generating step, initial series are changed into a collection of comparable series.…”

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

Taguchi-Grey relation analysis for assessing the optimal set of control factors of thermal barrier coatings for high-temperature applications

Yunus

Alsoufi

Munshi

2016

Mech Adv Mater Mod Process

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Background: In an aerospace industry, the efficient use of thermally sprayed coatings for high-temperature applications is achieved by improving the thermal characteristics (TC) such as thermal drop/barrier (TD) and thermal fatigue cycles (TFC). The characterization of ceramic coatings demands a better understanding of TC and their performance. Methods: In this paper, an attempt has been made to use hybrid Taguchi design method based grey relation analysis (GRA) for optimizing the control factors such as the thickness of coating, type of coating, bond coating and exposed temperature. The necessary experiments were carried out using Taguchi L16 factorial design of experiments for analysis based on the larger the better signal-to-noise (S/N) ratio. The multi-response/output optimization and grading of control factors were successfully carried out by GRA.

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Optimisation Technique for Face Milling Stainless Steel with Multiple Performance Characteristics

Cited by 89 publications

References 10 publications

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Fundamental Consideration

Taguchi-Grey relation analysis for assessing the optimal set of control factors of thermal barrier coatings for high-temperature applications

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