Modeling and analysis of surface roughness of microchannels produced by μ-WEDM using an ANN and Taguchi method

Jafari, Rahim; Kahya, Müge; Oliaei, Samad Nadimi Bavil; Ünver, Hakkı Özgür; Ozyurt, Tuba Okutucu

doi:10.1007/s12206-017-1039-7

Cited by 17 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surface roughness of the microchannels has a significant effect on the heat transfer characteristics, pressure drop and velocity fluctuations. An increased surface roughness will result in an increased pressure drop and velocity fluctuations [31]. Surface roughness characteristics including arithmetical mean height (Sa), root mean square roughness (Sq) and maximum height (Sz) are studied.…”

Section: Parametric Influence On Surface Roughness (Sa Sq and Sz)mentioning

confidence: 99%

Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

et al. 2020

View full text Add to dashboard Cite

This paper presents the results of an investigation on the capacity of wire electrical discharge machining (WEDM) to produce microchannels in the Nickel-based alloy, Monel 400. The main objective of the current study is to produce microchannels with desired/target geometry and acceptable surface quality. Square cross-sectional microchannels with dimensions of 500 × 500 µm were investigated. Experiments were conducted based on the one-factor-at-a-time approach for the key input WEDM process parameters, namely pulse-on time (TON), pulse-off time (TOFF), average gap voltage (VGAP), wire feed (WF), and dielectric flow rate (FR). Dimensional accuracy, machining speed, surface roughness, surface morphology, microhardness, and microstructure were analyzed to evaluate the microchannels. The minimum errors of 6% and 3% were observed in the width and depth of the microchannels, respectively. Furthermore, microchannels with enhanced surface integrity could be produced exhibiting smooth surface morphology and shallow recast layer (~0–2.55 µm).

show abstract

Section: Parametric Influence On Surface Roughness (Sa Sq and Sz)mentioning

confidence: 99%

Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Jafari et al [27] ANN with 1-hidden layer Bayesian regularization Copper T ON , T OFF , I P , spark gap voltage, wire speed R a I P had most significant effect of Ra followed by T ON. Singh and Mishra [28] BPNN with 1-hidden layer Levenberg-Marquardt Nimonic 263 T ON , T OFF , I P , spark gap voltage R a , recast layer thickness Mukhopadhyay et al [29] feedforward neural network Levenberg-Marquardt EN 31 tool steel Discharge current, SV, T ON , T OFF fractal dimension From Table 1, it can be seen that most of the work carried out in the past did not consider the superalloys, especially, Inconel 718, as the work piece.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…They concluded that surface roughness increases as feed rate increases. Jafari et al [13] have studied surface roughness in micro-wire electric discharge machining using Taguchi and ANN method. They concluded that micro-weld parameters have great influence on surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis on the Turning of Aluminum Alloy 7075 Based on Taguchi Method and Artificial Neural Network

Joshy¹,

D'souza²,

Muthirulan³

et al. 2019

JESA

View full text Add to dashboard Cite

This paper mainly aims to disclose the effects of cutting conditions on the turning of aluminum alloy 7075 (AA7075). First, the artificial neural network (ANN) was programmed to investigate how cutting parameters, namely cutting speed, feed rate and depth of cut, affect the surface roughness of AA7075. Then, the taguchi method was introduced to design an L 27 orthogonal array, in which each cutting parameter is considered on three levels. The results of orthogonal analysis were used to train the ANN called backpropagation neural network (BPNN) on MATLAB. The trained network was applied to predict the surface roughness of AA7075 through MATLAB simulation. Meanwhile, an experiment was conducted under the same conditions. The experimental results were found consistent with the simulation data, indicating that the BPNN is suitable for simulation the turning of AA7075. It is also learned that the cutting speed has the greatest impact on surface roughness; the surface roughness is negeatively correlated with feed rate; the negative correlation is positively mediated by the cutting speed.

show abstract

Modeling and analysis of surface roughness of microchannels produced by μ-WEDM using an ANN and Taguchi method

Cited by 17 publications

References 26 publications

Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

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

Experimental Analysis on the Turning of Aluminum Alloy 7075 Based on Taguchi Method and Artificial Neural Network

Contact Info

Product

Resources

About