Application of Artificial Intelligence Approach in Modeling Surface Quality of Aerospace Alloys in WEDM Process

Devarasiddappa, D.; George, Jees; Chandrasekaran, M.; Teyi, Nabam

doi:10.1016/j.protcy.2016.08.239

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…Among the results, these authors found that ANFIS models perform more efficiently than convectional polynomial models [34]. Devarasiddappa et al [35] employed an artificial neural network (ANN) to predict surface roughness in the wire-cut electrical discharge machining (WEDM) of Inconel 825. These authors found that this methodology is effective for modeling surface roughness in this Inconel alloy [35].…”

Section: State Of the Artmentioning

confidence: 99%

“…Devarasiddappa et al [35] employed an artificial neural network (ANN) to predict surface roughness in the wire-cut electrical discharge machining (WEDM) of Inconel 825. These authors found that this methodology is effective for modeling surface roughness in this Inconel alloy [35]. Maher et al [36] employed an adaptive neuro-fuzzy inference system (ANFIS) to predict cutting speed, surface roughness, and heat-affected zone in WEDM.…”

Section: State Of the Artmentioning

confidence: 99%

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Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Pérez

2020

Mathematics

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Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Pérez

2020

Mathematics

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“…Shandilya et al [18] employed ANN and statistic approach for the estimation of the average cutting speed in the machining of SiCp/Al 6061 composites with WEDM and they found that the prediction accuracy of ANN model was about three times better than statistical model. Devarasiddappa et al [19] applied ANN model to predict the surface roughness in WEDM of aerospace alloy with accuracy as much 93.62%. They also mentioned that this ANN technique could further be develop for artificial intelligence based on-line learning system.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

Zakaria

Izamshah

Kasim

et al. 2019

emitter

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Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process.

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“…In a study of Ti6Al4V alloy cut with WEDM; when the values of Ton, Toff, I, Va, fw and tw increase, the cutting width is increased and also the increment of Ton, Va, water pressure and fw increase the MRR is declared [11]. In a study in which statistical analysis and experimental studies were evaluated together, it was emphasized that the most effective parameters on MRR and Ra are discharge current and T on [12][13][14][15][16]. The thermophysical properties of the workpiece, plays a important role in affecting the EDM process.…”

Section: Introductionmentioning

confidence: 99%

Optimization of machining performance parameters in wire EDM of metals with different thermophysical properties

Özerkan¹

2019

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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CNC Wire electric discharge machining (WEDM) is different mode of electro discharge machining (EDM) in which conductive, stretched small diameter thin wire is used as tool. It has been widely used to shape machine elements by electrical sparks. In cutting with WEDM, suitable input parameters for the work piece material should be selected due to the its specifications. However the desired machining performace outputs like material removal rate (MRR) and surface roughness (Ra) parameters can be controlled in this way. In this study the role of thermophysical properties of work piece materials are researched in WEDM. And also, in this presented study an attempt has been made to Grey relational analysis to obtain desirable performance characteristics through the selection of appropriate process parameters. For this purpose, aluminum, copper and high speed steel (HSS) are machined with various parameters. As a result, the greatest MRR was obtained in the cutting of copper and in HSS and aluminum processes, respectively. And also if the detected surface roughness values are evaluated from lowest to highest copper, HSS and aluminum metals, respectively.

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Application of Artificial Intelligence Approach in Modeling Surface Quality of Aerospace Alloys in WEDM Process

Cited by 19 publications

References 8 publications

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

Optimization of machining performance parameters in wire EDM of metals with different thermophysical properties

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