Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model

Sarkar, Soumya; Mitra, S.; Bhattacharyya, B.

doi:10.1007/s00170-004-2203-7

Cited by 113 publications

(47 citation statements)

References 18 publications

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“…Shah et al (2010) have shown that the material thickness has little effect on the material removal rate and kerf but is a significant factor in terms of surface roughness in wire electrical discharge machining (WEDM) of tungsten carbide samples. Now-a-days, artificial neural network is used as a tool in modeling of EDM process (Spedding and Wang 1997;Tsai and Wang 2001;Sarkar et al 2006;Mandal et al 2007;Assarzadeh and Ghoreishi 2008).…”

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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Section: Review Of Roughness Study In Machiningmentioning

confidence: 99%

Fundamental Consideration

Sahoo¹,

Barman²,

Davim³

2011

Fractal Analysis in Machining

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“…Test result reveals that in machining Al 2 O 3p /6061Al composites a very low wire tension, a high flushing rate and a high wire speed are required to prevent wire breakage: an appropriate servo voltage, a short pulse-on time, and a short pulse-off time, which are normally associated with a high current speed, have a little effect on the surface roughness. Sarkar et al (2006) studied the WEDM of γ titanium aluminide. They also attempted to develop an appropriate machining strategy for a maximum process yield criteria.…”

Section: Past Research Work On Wedm Of Mmcsmentioning

confidence: 99%

Wire Electric Discharge Machining of Metal Matrix Composite Materials

Shandilya¹,

Jain²,

Jain³

2011

DAAAM International Scientific Book 2011

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“…They coupled a genetic algorithm (GA) with an ANN to forecast the surface roughness. S. Sarkar et al 29 produced a multilayer feed-forward-ANN model to predict the process parameters of the machining of g titanium aluminide with a wire-electrical-discharge machine. A. K. Singh et al 30 used a multilayer feed-forward ANN to predict the flank wear of high-speed steel drill bits for drilling holes into a copper workpiece.…”

Section: Introductionmentioning

confidence: 99%

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Rajesh¹,

Hynes²,

Kumar³

et al. 2017

Mater. Tehnol.

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Thermal drilling is a novel sheet-metal-hole-making technique that utilizes the heat produced at the interface of the rotating conical tool and workpiece in order to soften the workpiece and pierce a hole into it. In this work, experiments with thermal drilling of galvanized steel were conducted based on the Taguchi L27 orthogonal array. Significant process parameters such as rotational speed, tool angle and workpiece thickness were varied during the experimentation. In thermal drilling, the thermal-drill tool pushes aside a large amount of workpiece material to form a sleeve, which is often referred to as the bushing length. A predictive model for the bushing length was developed using a feed-forward artificial neural network based on experimental data. As the bushing length is closely associated with the tapping process, the influences of the input process parameters play a vital role in fastening galvanized steel with threaded fasteners in diverse engineering applications. The optimization problem was solved by implementing a genetic algorithm under constraint limits to maximize the bushing length. Further, a confirmation test was conducted with the intention to compare the optimum value and its corresponding bushing length predicted by the genetic algorithm. Good agreement was observed between the predicted and the experimental values. Keywords: thermal drilling, artificial neural network, genetic algorithm, galvanized steel, bushing length Termi~no vrtanje je nova, za vrtanje lukenj v plo~evino, uporabljena tehnika, ki izkori{~a toploto, proizvedeno na povr{ini vrte~e se konice orodja na obdelovancu z namenom, da ga zmeh~a in vanj naredi luknjo. V delu so bili izvedeni preizkusi na osnovi metode Taguchi L27 z ortogonalno matriko s termi~nim vrtanjem galvaniziranega jekla. Pomembni parametri postopka, kot so: hitrost vrtenja, kot orodja in debelina obdelovanca, so se med eksperimentiranjem spreminjali. Pri toplotnem vrtanju, vrtanje orodja potisne stran ve~materiala obdelovanca tako, da se tvori navarek (rokav) okoli luknje, ki se pogosto omenja kot dol`ina {ablone. Napovedni model za dol`ino {ablone, je razvit z uporabo umetne nevronske mre`e, ki temelji na znanstvenih podatkih. Ker je dol`ina {ablone precej povezana s procesom izdelave navoja, vplivi teh vhodnih procesnih parametrov igrajo klju~no vlogo pri pritrditvi galvaniziranega jekla z navojem pritrdilnih elementov v razli~nih in`enirskih aplikacijah. Problem optimizacije je bil re{en z implementacijo genetskega algoritma na podlagi omejitev za pove~anje dol`ine {ablone. Ugotovljeno je bilo dobro ujemanje med napovedano in eksperimentalno vrednostjo. Klju~ne besede: termi~no vrtanje, umetna nevronska mre`a, genetski algoritem, galvanizirano jeklo, dol`ina {ablone

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Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model

Cited by 113 publications

References 18 publications

Fundamental Consideration

Fundamental Consideration

Wire Electric Discharge Machining of Metal Matrix Composite Materials

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

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