Impact of copper-titanium carbide tooltip on machined surface integrity during electrical discharge machining of EN31 steel

Walia, Arminder Singh; Srivastava, Vineet; Jain, Vivek

doi:10.1088/2053-1591/ab3ac4

Cited by 5 publications

(5 citation statements)

References 37 publications

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“…The VOEDM identifies the major contributing factor of pulse off time for EWR (50.33%) and MRR (34.78% (Meshram et al 2020). The effect of these parameters has been investigated by using Response Surface Methodology (RSM) (Walia, Srivastava, and Jain 2019). Speed, feed rate and depth of cut is compared with the uncoated Carbide tool at the same parametric values in the CNC turning machine (Shirpurkar, Waghmare, and Date 2019).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Performances of Novel Titanium carbide tool Insert and Uncoated Tool in CNC Turning of HCHC D2 Steel for Minimizing Surface Roughness and Maximizing Material Removal Rate

Teja¹,

Lokesh²

2021

revistageintec

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Aim:-The Present study is focused on the comparison of performances of Titanium carbide(TIC) and Uncoated carbide tool inserts with CNC turning of HCHC D2 Steel for minimizing surface roughness and maximizing material removal rate. Materials And Methods:-Machining of HCHC D2 steel material was compared with Titanium carbide(TIC) and Uncoated carbide tool inserts. It was divided into two groups of 27 samples each. The samples were machined with two different tool inserts one was control group and other was experimental group. Results:-Surface roughness was decreased and material removal rate was increased when machining was done with TIC carbide compared to HSS tool insert (P=0.028).Conclusions: It is concluded that the surface roughness was decreased and material removal rate was increased when TIC carbide tool insert was used for machining.

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Section: Methodsmentioning

confidence: 99%

Comparison of Performances of Novel Titanium carbide tool Insert and Uncoated Tool in CNC Turning of HCHC D2 Steel for Minimizing Surface Roughness and Maximizing Material Removal Rate

Teja¹,

Lokesh²

2021

revistageintec

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“…The correlation coefficient lies between +1 and −1, where values closer to +1 indicate a stronger relationship, and values closer to −1 indicate a weaker relationship, between the actual and predicted responses. It is defined using Equation (1).…”

Section: Performance Evaluation Of Machine Learning Techniquesmentioning

confidence: 99%

“…In EDM, stock from the specimen is removed by means of continuous discharges between two electrodes, i.e., the tool and the work material. It causes the creation of a plasma channel and the temperature reaches the range of 7500-19,000 • C, thereby removing the material from the electrodes via melting and evaporation [1][2][3][4][5]. As the supply of pulsating current is withdrawn, the plasma envelope collapses and the dielectric removes the melted material from the machining site in the form of debris.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques

Walia

Srivastava

Rana

et al. 2021

Metals

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In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

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“…It has been observed from the given figures that the increment in surface inconsistencies takes place with the increment in pulse-on time. This might be due to the plasma channel expansion with the increase in pulse-on time [39][40][41], which has augmented the machined zone of workpiece and therefore decreased both the impulsive force and energy density. Due to this, the melting part of debris is not expelled totally and frames an obvious globule-like recast layer to debilitate the surface.…”

Section: Influence Of Pulse On Time Variationmentioning

confidence: 99%

“…This results in the resolidification of the debris. However, with the larger pulse-off time, there is a substantial temperature drop on the tool and work surface [39][40][41][42]. Therefore, the energy requirement to restore plasma channel increases.…”

Section: Effect Of Variation In Pulse Intervalmentioning

confidence: 99%

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu–TiC Sintered Electrode

et al. 2021

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In electrical discharge machining (EDM), the machined surface quality can be affected by the excessive temperature generation during the machining process. To achieve a longer life of the finished part, the machined surface quality plays a key role in maintaining its overall integrity. Surface roughness is an important quality evaluation of a material’s surface that has considerable influence on mechanical performance of the material. Herein, a sintered cermet tooltip with 75% copper and 25% titanium carbide was used as tool electrode for processing H13 steel. The experiments have been performed to investigate the effects of EDM parameters on the machined surface roughness. The findings show that, as the pulse current, pulse length, and pulse interval are increased, the surface roughness tends to rise. The most significant determinant for surface roughness was found to be pulse current. A semi-empirical surface roughness model was created using the characteristics of the EDM technique. Buckingham’s theorem was used to develop a semi-empirical surface roughness prediction model. The semi-empirical model’s predictions were in good agreement with the experimental studies, and the built empirical model based on physical features of the cermet tooltip was tested using dimensional analysis.

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Impact of copper-titanium carbide tooltip on machined surface integrity during electrical discharge machining of EN31 steel

Cited by 5 publications

References 37 publications

Comparison of Performances of Novel Titanium carbide tool Insert and Uncoated Tool in CNC Turning of HCHC D2 Steel for Minimizing Surface Roughness and Maximizing Material Removal Rate

Comparison of Performances of Novel Titanium carbide tool Insert and Uncoated Tool in CNC Turning of HCHC D2 Steel for Minimizing Surface Roughness and Maximizing Material Removal Rate

Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu–TiC Sintered Electrode

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