Effect of Different Tool Electrodes (Wire) of WEDM Process of Inconel 718

Chaudhari, Rakesh; Patel, Het; Sheth, Manav; Prajapati, Nisarg; Fuse, Kishan; Abhishek, Kumar; Vora, Jay

doi:10.1007/978-981-16-7660-4_28

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Recurring spark formation leads to the melting and vaporization of work material thereby, erosion rate during machining [ 70 ]. Increased T on subsequently enriches thermal energy which in turn enhances the sparking frequency [ 71 , 72 ]. This is the additional factor for obtaining higher MRR.…”

Section: Resultsmentioning

confidence: 99%

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

et al. 2022

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Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (Ton), and pulse-off-time (Toff) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and Ton were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm3/s at Ton of 95 µs, Toff of 9 µs, current of 6 A. Least SR was obtained at Ton of 35 µs, Toff of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

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Section: Resultsmentioning

confidence: 99%

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

et al. 2022

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“…Table 1 displays the conditions for input factors. Taguchi's approach has multiple advantages, reducing the experimental trial's relationship between input and output factors [52][53][54]. Thus, it has been adopted in the present work.…”

Section: Experimental Set-up and Conditionsmentioning

confidence: 99%

Multi-Response Optimization and Influence of Expanded Graphite on Performance of WEDM Process of Ti6Al4V

Vora

Shah

Khanna

et al. 2023

JMMP

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Wire electrical discharge machining (WEDM) is widely preferred for machining difficult-to-cut materials like Ti6Al4V. In the present study, current, pulse-off-duration (Toff), and pulse-on-duration (Toff) were identified as vital input factors for the WEDM process of Ti6Al4V. Material removal rate (MRR) and surface roughness (SR) were selected as output measures for the study. The experiments were carried out by employing Taguchi’s L9 design at three levels. Empirical models were generated, which give the relationship between the input and output factors of the process. To check the acceptability of the model terms, analysis of variance (ANOVA) was used. The regression mode was observed to be significant for the output measures. For MRR, Toff was recorded as the highly significant factor affecting the response values with 74.95% impact, followed by Ton with 16.39%, and current with 6.56%. In the case of SR, Ton was found to be a highly significant factor with a 50.24% impact, followed by current with 43.99%, and Toff with 1.47%. Further, multi-objective optimization by using the HTS technique was performed. The effect of expanded graphite (EG) nano-powder has been studied on the output factors of MRR and SR. The use of EG nano-powder was found to improve WEDM operations as MRR was increased by 45.35%, and simultaneously, SR was reduced by 36.16%. Lastly, the surface morphology of the machined surface was investigated by employing SEM to understand the effect of EG nano-powder. The results have shown a reduction in surface defects by using EG nano-powder compared to the conventional WEDM process.

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“…During the process, serial sparks are formed between tool and component which erodes the little amount of material through melting and vaporization [ 20 , 21 ]. Dielectric fluid is used in the machining zone which helps to remove the eroded material particles [ 22 ]. This phenomenon of dielectric flushing and sparking forms a hard and uneven machined surface [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

et al. 2022

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Shape memory alloy (SMA), particularly those having a nickel–titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al2O3 nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (Ton), pulse-off time (Toff), and Al2O3 nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi’s three levels L9 approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al2O3 nanopowder was found to have the highest contributing effect of 76.13% contribution, Ton was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at Ton of 90 µs, Toff of 5 µs, and powder concentration of 2 g/L), the lowest SR value of 3.13 µm, and the lowest RLT value of 10.24 (both responses at Ton of 30 µs, Toff of 25 µs, and powder concentration of 2 g/L). A specific multi-objective Teaching–Learning-Based Optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effectiveness of both the regression model and the TLBO algorithms. Confirmatory trials have shown an extremely close relation which shows the suitability of both the regression model and the TLBO algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA. A considerable reduction in surface defects owing to the addition of Al2O3 powder was observed in surface morphology analysis.

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Effect of Different Tool Electrodes (Wire) of WEDM Process of Inconel 718

Cited by 8 publications

References 21 publications

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Multi-Response Optimization and Influence of Expanded Graphite on Performance of WEDM Process of Ti6Al4V

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

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