scholarly journals A study on die sinking EDM of Nimonic C-263 super alloy : an intelligent approach to predict the process parameters using ANN

2017 ◽  
Vol 7 (1.1) ◽  
pp. 651
Author(s):  
Rama Bhadri Raju Chekuri ◽  
Ramakotaiah Kalluri ◽  
Rajesh Siriyala ◽  
Jamaleswara Kumar Palakollu
Keyword(s):  
Process Parameters ◽  
Electrode Wear ◽  
Material Erosion ◽  
Contrary Effect ◽  
Hidden Layer ◽  
Intelligent Approach ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Machining Characteristics

In current study, machining characteristics of Nimonic C-263 are analysed by TAGUCHI and modelled using Artificial Neural Networks (ANN). The response parameters under consideration are Material Erosion Rate (MER), Electrode Wear Rate (EWR), Surface Roughness (SR) and Dimensional Overcut (DOC). A regression mathematical model is also developed to verify the capabilities of ANN. The modelling of ANN includes identifying appropriate combination of hidden layers and number of neurons in each hidden layer. Study on machining characteristics revealed, peak current as the most influential process parameters affecting all the responses; followed by Pulse on-time. A contrary effect is observed for Pulse off-time. A rare process parameter named flushing pressure showed negligible influence on responses. Among various ANN architectures, 6-6 architecture is noted to possess phenomenal prediction accuracy of 99.71% compared to 93.55% of regression analysis.  

Machining Characteristics Estimation in WEDM Process While Machining Titanium Grade-2 Material Using ANN

2020 ◽  
Author(s):  
Prathik Jain Sudhir ◽  
Ravindra Holalu Venkatadas ◽  
Ugrasen Gonchikar
Keyword(s):  
Process Parameters ◽  
Machining Process ◽  
Electrode Wear ◽  
Training Set ◽  
Hard Materials ◽  
Titanium Grade ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Machining Characteristics

Abstract Wire Electrical Discharge Machining (WEDM) provides an effective solution for machining hard materials with intricate shapes. WEDM is a specialized thermal machining process is capable to accurately machining parts of hard materials with complex shapes. However, selection of process parameters for obtaining higher machining efficiency or accuracy in wire EDM is still not fully solved, even with the most up-to-date CNC WED machine. The study presents the machining of Titanium grade 2 material using L’16 Orthogonal Array (OA). The process parameters considered for the present work are pulse on time, pulse off time, current, bed speed, voltage and flush rate. Among these process parameters voltage and flush rate were kept constant and the other four parameters were varied for the machining. Molybdenum wire of 0.18mm is used as the electrode material. Titanium is used in engine applications such as rotors, compressor blades, hydraulic system components and nacelles. Its application can also be found in critical jet engine rotating and airframes components in aircraft industries. Firstly optimization of the process parameters was done to know the effect of most influencing parameters on machining characteristics viz., Surface Roughness (SR) and Electrode Wear (EW). Then the simpler functional relationship plots were established between the parameters to know the possible information about the SR and EW. This simpler method of analysis does not provide the information on the status of the material and electrode. Hence more sophisticated method of analysis was used viz., Artificial Neural Network (ANN) for the estimation of the experimental values. SR and EW parameters prediction was carried out successfully for 50%, 60% and 70% of the training set for titanium material using ANN. Among the selected percentage data, at 70% training set showed remarkable similarities with the measured value then at 50% and 60%.

The Effect of EDM Die-Sinking Parameters on Machining Characteristics in Aluminum Alloy Machining

2015 ◽  
Vol 761 ◽  
pp. 303-307 ◽  
Author(s):  
Laily Suraya ◽  
M.A. Ali ◽  
N.I.S. Hussein ◽  
Mohd Razali Muhamad ◽  
Manshoor Bukhari ◽  
...  
Keyword(s):  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Design Matrix ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Electrode Tool ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Machining Characteristics

The effect of machining parameters on machining characteristics for aluminium alloy LM6 (Al-Sil2) in Electrical Discharge Machining (EDM) die-sinking is studied. The objective of this project is to determine the relationship between the machining parameters including pulse-on time, pulse-off time, peak current and voltage with the machining characterictics such as Material Removal Rate (MRR), Electrode Wear Rate (EWR) and Surface Roughness (Ra). Copper materials having diameter 15mm was chosen as the electrode tool. Design of experimenent using Taguchi technique was employed to design experimental matrix that was used to optimize the MRR, EWR and Ra. The analysis was done by using the Minitab software version 16. It is found that current and pulse off time significantly affect MRR, EWR and Ra while pulse on time and voltage are less significant in their effect on machining responses. Results show that using Taguchi as a design matrix, the best setting of optimum value for machining parameters to find the required machining responses can be obtained.

Optimization and surface modification in electrical discharge machining of AA 6061/SiCp composite using Cu–W electrode

2015 ◽  
Vol 231 (3) ◽  
pp. 332-348 ◽  
Author(s):  
Balbir Singh ◽  
Jatinder Kumar ◽  
Sudhir Kumar
Keyword(s):  
Process Parameters ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Surface Characteristics ◽  
Electrode Wear ◽  
Material Transfer ◽  
Machined Surface ◽  
Recast Layer Thickness ◽  
Pulse On Time ◽  
Pulse Off Time

This paper presents the experimental investigation on the electro-discharge machining of aluminum alloy 6061 reinforced with SiC particles using sintered Cu–W electrode. Experiments have been designed as per central composite rotatable design, using response surface methodology. Machining characteristics such as material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR) have been investigated under the influence of four electrical process parameters; namely peak current, pulse on time, pulse off time, and gap voltage. The process parameters have been optimized to obtain optimal combination of MRR, EWR, and SR. Further, the influence of sintered Cu–W electrode on surface characteristics has been analyzed with scanning electron microscopy, energy dispersive spectroscopy, and Vicker microhardness tests. The results revealed that all the process parameters significantly affect MRR, EWR, and SR. The machined surface properties are modified as a result of material transfer from the electrode. The recast layer thickness is increased at higher setting of electrical parameters. The hardness across the machined surface is also increased by the use of sintered Cu–W electrode.

scholarly journals Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

2018 ◽  
Vol 63 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Partha Protim Das ◽  
Sunny Diyaley ◽  
Shankar Chakraborty ◽  
Ranjan Kumar Ghadai
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Machining Process ◽  
Wire Tension ◽  
Electro Discharge Machining ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Fuzzy Logic Technique ◽  
Off Time

Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG.

Surface Roughness Optimization of Wire Electrical Discharge Machining Using ABC Algorithm

2015 ◽  
Vol 766-767 ◽  
pp. 902-907
Author(s):  
Bibin K. Tharian ◽  
B. Kuriachen ◽  
Josephkunju Paul ◽  
Paul V. Elson
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Wire Electrical Discharge Machining ◽  
Wire Tension ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Wire Feed

Wire electrical discharge machining is one of the important non-traditional machining processes for machining difficult to machine materials. It involves the removal of material by the discrete electric discharges produced between the inter electrode gap of continuously moving wire electrode and the work piece. The ability to produce intricate profiles on materials irrespective of the mechanical properties made this process to be widely used in industries. The present study investigates the relationship of various process parameters in WEDM of AISI 202 stainless steel with brass electrode.The experiments were planned according to Taguchi’s L18 orthogonal array and experimental models were developed. The important process parameters identified for the present study were pulse on time, peak current, pulse off time, wire feed, wire tension, dielectric flushing pressure, servo feed and gap voltage. The surface roughness of the machined surface was measured as the process performance measure. Analysis of variance test has also been carried out to check the adequacy of the developed models and to identify the level of significance of each process parameters. In addition to the developed models, ABC optimization has been performed to identify the optimum parameter combination for minimum surface roughness and the obtained optimal process parameters are peak current 11 A, pulse on time 100 μs, pulse off time 49 μs, wire feed 4 m/min, wire tension 10 N, flushing pressure 12 kg/cm2, servo feed 2100 mm/min and set gap voltage 30 V. Finally the results were verified with the experimental results and found that they are in good agreement.

Surface Study in a Non-conventional (Electrical Discharge Machining) Process for Grade 6 Titanium Material

2014 ◽  
Vol 68 (1) ◽  
Author(s):  
Md. Ashikur Rahman Khan ◽  
M. M. Rahman
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Machining Process ◽  
Machined Surface ◽  
Titanium Material ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

Electrical discharge machining (EDM) produces complex shapes and permits high-precision machining of any hard or difficult-to-cut materials. The performance characteristics such as surface roughness and microstructure of the machined face are influenced by numerous parameters. The selection of parameters becomes complicated. Thus, the surface roughness (Ra) and microstructure of the machined surface in EDM on Grade 6 titanium alloy are studied is this study. The experimental work is performed using copper as electrode material. The polarity of the electrode is maintained as negative. The process parameters taken into account in this study are peak current (Ip), pulse-on time (Ton), pulse-off time (Toff), and servo-voltage (Sv). A smooth surface finish is found at low pulse current, small on-time and high off-time. The servo-voltage affects the roughness diversely however, a finish surface is found at 80 V Sv. Craters, cracks and globules of debris are appeared in the microstructure of the machined part. The size and degree of craters as well as cracks increase with increasing in energy level. Low discharge energy yields an even surface. This approach helps in selecting proper process parameters resulting in economic EDM machining. 

Optimization of WEDM process parameters in machining Nimonic 75 alloy using brass wire

2020 ◽  
Vol 16 (5) ◽  
pp. 1189-1202 ◽  
Author(s):  
Harvinder Singh ◽  
Vinod Kumar ◽  
Jathinder Kapoor
Keyword(s):  
Removal Efficiency ◽  
Process Parameters ◽  
Material Removal ◽  
Kerf Width ◽  
Content Type ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Nimonic 75 ◽  
Off Time

PurposeAn experimental study has been conducted to model and optimize wire electric discharge machining (WEDM) process parameters such as pulse-on time, pulse-off time, servo voltage and peak current for response characteristics during machining of Nimonic 75 alloy.Design/methodology/approachThe response surface methodology (RSM)-based Box–Behnken's design has been employed for experimental investigation. RSM is used for developing quadratic regression models for selected response variables i.e. material removal efficiency and kerf width. To validate the model, confirmation experiments have been performed. The multi-response optimization has been done using desirability function approach.FindingsThrough analysis of variation, the percent contribution of process parameters on the response characteristics has been found. Pulse-off time is the most significant parameter affecting the kerf width and material removal efficiency followed by pulse-on time. The quadratic regression models have been developed for prediction of selected response variables. An attempt has been made to optimize the WEDM parameters for material removal efficiency and kerf width. The recommended process parameter setting for maximum material removal efficiency and minimum kerf width have been found to be pulse-on time = 0.6 µs, pulse-off time = 14 µs, servo voltage = 25 V and peak current = 200 A.Originality/valueThe “kerf width” is an important response variable for maintaining dimensional accuracy of the machined component, but has not been given due attention by the researchers. In the present work, the developed regression model for “kerf width” can be used in estimating wire offset setting and thereby getting a dimensionally accurate product. The optimum process parameters obtained in WEDM of Nimonic 75 alloy will contribute in database of machining. The outcome of this study would be added to scare database of the machining of Nimonic 75 alloy and also would be extremely useful for making the technology charts for WEDM.

Estimation and Comparison of Electrode Wear and Ae Parameters of Titanium Material in Wire Electric Discharge Machining Using ANN

2019 ◽  
Vol 895 ◽  
pp. 144-151 ◽  
Author(s):  
Jain S. Prathik ◽  
H.V. Ravindra ◽  
G.V. Naveen Prakash ◽  
G. Ugrasen
Keyword(s):  
Process Parameters ◽  
Machining Process ◽  
Vibration Monitoring ◽  
Electrode Wear ◽  
Training Set ◽  
Titanium Material ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Ae Signals ◽  
Testing Set

Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. Selection of process parameters for obtaining higher cutting efficiency or accuracy in WEDM is still not fully solved, even with most up-to-date CNC wire EDM machine. It is widely recognised that Acoustic Emission (AE) is gaining ground as a monitoring method for health diagnosis on rotating machinery. The advantage of AE monitoring over vibration monitoring is that the AE monitoring can detect the growth of subsurface cracks whereas the vibration monitoring can detect defects only when they appear on the surface. This study outlines the optimization of titanium material using L16 design of experiment. Each experiment has been performed varying the process parameters like pulse-on time, pulse-off time, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Simple functional relationships between the parameters were plotted to arrive at possible information on Electrode Wear (EW) and AE signals. But these simpler methods of analysis did not provide any information about the status of the electrode. Thus, there is a requirement for more sophisticated methods that are capable of integrating information from the multiple sensors. Hence, method like Artificial Neural Network (ANN) has been applied for the estimation of EW, AE signal strength, AE count and AE RMS. The ANN algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into three sets: the training set, validation set and testing set. The training set is used to make the ANN learn the process and the testing set will check the performance of ANN. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 60% and 70%. The best model is selected from the said percentages of data. Estimation of the EW and AE signals parameters by ANN at 70% of data training set showed the best correlation with the measured value.

Performance Evaluation of Electrical Discharge Machining Die Sinking on Stainless Steel 316L Using Copper Impregnated Graphite

2014 ◽  
Vol 660 ◽  
pp. 48-54 ◽  
Author(s):  
Wahaizad Safiei ◽  
Safian Sharif ◽  
Ahmad Fairuz Mansor ◽  
Mohd Halimudin Mohd Isa
Keyword(s):  
Stainless Steel ◽  
Removal Rate ◽  
Dimensional Accuracy ◽  
Time Range ◽  
Electrode Wear ◽  
Stainless Steel 316L ◽  
Peak Current ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

This study presents the results of experimental studies carried out to conduct a comprehensive investigation on the influence of Electrical Discharge Machining (EDM) input parameters on characteristics of EDM process. The machining parameters include peak current, servo voltage, pulse ON time and pulse OFF time. The study was conducted using 2 levels of Full Factorial Method in Design of Experiments. The design expert software employed to perform all the data analysis for Full Factorial and Central Composite Design (CCD) experiments. This study evaluates the machining performance of the Stainless Steel 316L using Sodick EDM linear motor series AM3L which employed Copper impregnated graphite diameter 7.0 mm as the tool electrode. The response variables are material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR) and dimensional accuracy. The result shows that the peak current was the most significant factors to all variable responses. The servo voltage does not have significant effects to the machining responses in RSM. Higher current produced higher MRR, EWR, SR and Dimensional Accuracy. Maximum MRR was obtained at peak current range from 27amp to 38amp, pulse on time range from 120μs to 145μs and 60μs of pulse off time. Maximum EWR was obtained at peak current range from 27amp to 37amp, pulse on time range from 140μs to 160μs and 60μs of pulse off time. High probably, the minimum EWR only can be obtained if peak current parameter sets greater than 45amp. Lower dimensional accuracy and SR obtain at 5amp of pulse on time. Higher pulse off time produced lower MRR and EWR.Keywords: EDM Die sinking, Stainless Steel 316L, Copper Impregnated Graphite Electrode, Response Surface Methodology, Surface Roughness, Material Removal Rate, Electrode Wear Rate, Dimensional Accuracy

The Effect of EDM Die-Sinking Parameters on Material Characteristic for Aluminum Composite

2014 ◽  
Vol 699 ◽  
pp. 26-31 ◽  
Author(s):  
Mohd Amran Ali ◽  
Laily Suraya ◽  
Nor Atiqah Jaffar Sidek ◽  
Nur Izan Syahriah Hussein ◽  
Mohd Razali Muhamad ◽  
...  
Keyword(s):  
Taguchi Method ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Process ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Aluminum Composite ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

The machining ability of Electrical Discharge Machining (EDM) die-sinking on material characteristics of LM6 (Al-Sil2) is studied. This is due to the machining process on sharp edge, pocket, deep slot and micro hole cannot be performed by milling and turning machine. The objective of this paper is to determine the relationship between the machining parameters such as pulse on time, pulse off time, peak current and voltage on material removal rate (MRR) that are electrode wear rate (EWR) and surface roughness (Ra). Graphite tool of diameter 15mm was chosen as an electrode. Taguchi method is used as analysis technique to develop experimental matrix that is used to optimize the MRR, EWR and Ra. The analysis was done by using the Minitab software version 16. It is found that the current and pulse off time are significantly effected the MRR, EWR and Ra while pulse on time and voltage are less significant factors that affected the responses. From the Taguchi method, the best setting of optimum value was obtained. Thus, it shows that Taguchi method is the best quality tools that can be applied for production.

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