Process Optimization and Estimation of Machining Performances Using Artificial Neural Network in Wire EDM

Ugrasen, G.; Ravindra, H.V.; Prakash, G. Vijaya; Keshavamurthy, R.

doi:10.1016/j.mspro.2014.07.205

Cited by 54 publications

(17 citation statements)

References 10 publications

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“…The optimal conditions are as machine feed rate (4mm/min), wire speed 8mm/min, wire tension .4Kg, voltage 60v were identified. [6]optimized and estimated the machining performances using artificial neural network in wire EDM. Experimentation was performed as per Taguchi's L'16 orthogonal array.…”

Section: B B Rajesh Kumar Lodhi Sanjay Agarwarmentioning

confidence: 99%

Review on Effect of Machining Parameters on Performance Characteristics of Wire EDM Process

Ajay¹

2018

IJRASET

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The Wire EDM process has several machining parameters such as pulse on time, pulse off time, supply voltage, peak current, wire speed, wire feed, machining time, and rate of flushing, material and size of the wire, dielectric type, viscosity and other flow characteristics. Several researchers have made an attempt to improve the performance characteristics namely surface roughness, cutting speed, dimensional accuracy, dimensional deviation and material removal rate. Selection of optimal machining parameters plays an important role. Improperly selected parameters may result in serious problems like shot circuit and wire breakage. Hence there is a huge demand for research studies to find out the optimal machining parameters of WEDM. In this paper; a detailed review on effect of machining parameters on performance characteristics of Wire EDM process is presented and effect of optimization of process parameters is discussed.

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Section: B B Rajesh Kumar Lodhi Sanjay Agarwarmentioning

confidence: 99%

Review on Effect of Machining Parameters on Performance Characteristics of Wire EDM Process

Ajay¹

2018

IJRASET

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“…They recommend that a short pulse term combined close to a high apex regard gives better surface obnoxiousness. Ugrasen et al [16] used Taguchi method close to the extortion neural framework to refresh the precision, surface seriousness and MRR. They found that for the more than three responses, current is the most goliath factor Sudhakara and Prasanthi [17] investigated machining parameters for tool steel in WEDM.…”

Section: Literature Surveymentioning

confidence: 99%

Modeling and experimental investigation of process parameters in WEDM for surface roughness using regression model

Muniappan

Senthilkumar

Jayakumar

et al. 2018

MATEC Web of Conferences

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Abstract. The present study focused on the multiple regression modeling and predicting the surface roughness of the Aluminum hybrid composite during the WEDM process. The hybrid MMC was manufactured by process named as stir casting utilizing particulates of Silicon carbide and graphite each in Al6061 combination. The analyses were outlined with Taguchi L27 design matrix. Mathematical relationships between the surface roughness and WEDM cutting parameters (Pulse on time, Pulse off time, current, gap voltage, wire speed and wire tension) have been investigated. The results show that the multiple regression analysis is a successful method for developing a mathematical model to predict the surface roughness. The optimum value of process parameters for the predicted optimum value of surface roughness (1.285) is pulse on time 106 units (Level 1), pulse off time 60 units (Level 3), peak current 90 units (Level 2), gap set voltage 50 units (Level 3), wire speed3 units (Level 1) and wire tension 12 units (Level 3).The optimum results are adopted in validation study and the results based on WEDM process responses can be effectively improved.

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“…However, the selection of the proper machining parameters to achieve the desired machining performance has always been a challenging task for industry [5]. Ugrasen et al [24] BPNN with 1-hidden layer Levenberg-Marquardt -T ON , pulse-off-time, I P , bed speed VMRR, accuracy, R a ANN model with 70% training data gave best prediction as that of model with 50% or 60% training data.…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

Lalwani

Sharma

Pruncu

et al. 2020

JMMP

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This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.

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Process Optimization and Estimation of Machining Performances Using Artificial Neural Network in Wire EDM

Cited by 54 publications

References 10 publications

Review on Effect of Machining Parameters on Performance Characteristics of Wire EDM Process

Review on Effect of Machining Parameters on Performance Characteristics of Wire EDM Process

Modeling and experimental investigation of process parameters in WEDM for surface roughness using regression model

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

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