Optimization of WEDM process parameters using deep cryo-treated Inconel 718 as work material

Turning process is a primary process in engineering industries and optimization of process parameters enhance the machining performance. Inconel 718 is a nickel-based superalloy, widely found applications in the manufacturing of blades, sheets and discs in aircraft engines and rocket engines. It provides toughness at low temperature, with stand high mechanical stresses at elevated temperature and creep resistance. In this work, turning process is carried out on Inconel 718 with micro whole textured cutting inserts filled with solid lubricants. Three different solid lubricants are used namely molybdenum-di-sulfide (MoS2), tungsten-di-sulfide (WS2) and calcium-di-fluoride (CaF2). Experiments are performed as per L9 orthogonal array. Statistical approaches such as orthogonal array, Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) are used to find the importance and effects of machining parameters. In this study, input parameters included are feed, cutting speed and depth of cut and output parameter includes surface roughness. Optimization of process parameters is carried out and the significance is estimated. The result suggested that WS2 followed by MoS2 and CaF2 given good surface finish value. Also, solid lubricant in machining enhances the sustainability in manufacturing.

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Multi Quality Characteristics Of WEDM Process Parameters with RSM

Procedia Engineering ◽

10.1016/j.proeng.2013.09.146 ◽

2013 ◽

Vol 64 ◽

pp. 710-719 ◽

Cited By ~ 35

Author(s):

Neeraj Sharma ◽

Rajesh Khanna ◽

Rahuldev Gupta

Keyword(s):

Process Parameters ◽

Quality Characteristics ◽

Wedm Process

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Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽

10.5755/j01.mech.26.6.20484 ◽

2020 ◽

Vol 26 (6) ◽

pp. 540-544

Author(s):

Jayaraj JEEVAMALAR ◽

Sundaresan RAMABALAN ◽

Chinnamuthu SENTHILKUMAR

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Parameters ◽

Inconel 718 ◽

Machining Process ◽

Artificial Neural ◽

Pulse On Time ◽

Pulse Off Time ◽

Matlab Programming ◽

The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

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Influence of Wire Electrical Discharge Machining (WEDM) process parameters on surface roughness

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/290/1/012019 ◽

2018 ◽

Vol 290 ◽

pp. 012019 ◽

Cited By ~ 1

Author(s):

Mohammad Yeakub Ali ◽

Asfana Banu ◽

Mazilah Abu Bakar

Keyword(s):

Surface Roughness ◽

Process Parameters ◽

Electrical Discharge ◽

Electrical Discharge Machining ◽

Wire Electrical Discharge Machining ◽

Wedm Process

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

Volume 2A: Advanced Manufacturing ◽

10.1115/imece2020-23347 ◽

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%.

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