Modeling of surface finish in electro-discharge machining based upon statistical multi-parameter analysis

Petropoulos, G.; Vaxevanidis, Nikolaos M.; Pandazaras, C.

doi:10.1016/j.jmatprotec.2004.04.189

Cited by 47 publications

(21 citation statements)

References 14 publications

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“…Dimensional analysis shows the good surface finish, MRR and tool wear [29]and result shows the less than 10% surface finish model and less than 20% MRR [30]. Single and multiple statistical regression models are found for texture parameters [31].Heat transfer model was analyzed for EDM parameters such as pulse duration,pulse energy,MRR and crater shape [32].Thermal model was illustrated by data dependent system [33].Online wire rupture monitoring system was carried out by sparking frequency monitor [34].variable mass cylindrical plasma models was introduced [35].EDM process modeling was done by artificial neural network [36].ANN provides more accurate results of the parameters such as pulse on time, pulse off time and discharge current [37]. Tangent sigmoid multi-layered perceptron(TANMLP),radial basis function network(RBFN) are used to predict the surface finish [38].More accurate result was found by this model during machining process to predict MRR [39].Surface methodology was observed for pulse on time and pulse of time which shows the optimum speed cutting of 3mm/min [40].…”

Section: VImentioning

confidence: 88%

A Review on Progress in Electrical Discharge Machining and Theoretical Models

Prasanna¹

2017

IJRASET

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Electrical release machining (EDM) is one of the most non-conventional machining forms. EDM process depends on the thermoelectric vitality between the work piece and a cathode. A pulse discharge to happen in a little hole between the work piece and the cathode and expels the undesirable material from the parent metal through liquefying and vaporizing. The electrode and the work piece must have electrical conductivity to generate the spark. There are different sorts of items which can be delivered by EDM such as dies and moulds. The Auto motive industry, Aerospace parts and surgical components are manufactured by EDM process. This paper focuses on dry EDM machining, EDM with powder additives, EDM in water and advanced technique to predict the EDM performance.

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Section: VImentioning

confidence: 88%

A Review on Progress in Electrical Discharge Machining and Theoretical Models

Prasanna¹

2017

IJRASET

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“…in EDM of a cemented carbide and observed that in the case of R a parameter the most influential factors are intensity, followed by the pulse time factor. Petropoulos et al (2004) have emphasized the interrelationship between surface texture parameters and process parameters in EDM of Ck60 steel plates. They have considered amplitude, spacing, hybrid, as well as random process and fractal parameters.…”

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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“…Data-driven approaches use learning algorithms and experimental data to capture underlying influence of control parameters on outputs and build prediction models so that an in-depth understanding of underlying physical processes 2 Complexity is not a prerequisite [9]. Multivariable regression analysis [10,11], response surface methodology [12,13], artificial neural networks (ANN) [14][15][16], and support vector machine (SVM) [17][18][19] are the most widely data-driven approaches applied for modeling machined surface roughness. Other techniques like ensembles also are used for surface roughness prediction.…”

Section: Introductionmentioning

confidence: 99%

An Effective ABC‐SVM Approach for Surface Roughness Prediction in Manufacturing Processes

Lü

Liao

et al. 2019

Complexity

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It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and -insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC-SVM are better than DE-SVM, GA-SVM, and PSO-SVM except three indicator values of DE-SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.

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Modeling of surface finish in electro-discharge machining based upon statistical multi-parameter analysis

Cited by 47 publications

References 14 publications

A Review on Progress in Electrical Discharge Machining and Theoretical Models

A Review on Progress in Electrical Discharge Machining and Theoretical Models

Fundamental Consideration

An Effective ABC‐SVM Approach for Surface Roughness Prediction in Manufacturing Processes

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