Analysis and Optimization of Wire EDM Process of Titanium by Using GRA Methodology

Sahoo, Sarat Kumar; Bara, A.; Sahu, Anshuman Kumar; Mahapatra, Siba Sankar; Kiran, D. S. Sai Ravi; Teja, G. Sai; Teja, E.S.; Reddy, Seelam Pichi

doi:10.4028/www.scientific.net/msf.969.678

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…e formation of surface cracks was due to high uctuating stresses applied during the process of machining. Similar kind of observation was reported on wire EDM machine surfaces [27][28][29][30][31].…”

Section: Field Emission Scanning Electron Microscopy (Fesem)supporting

confidence: 84%

Experimental Investigation of Heat-Treated Tool on Wire Electric Discharge Machining of Titanium Alloy (Ti-6Al-4V)

Samson

Sridhar

Ranjith

et al. 2022

Advances in Materials Science and Engineering

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Ti-6Al-4V is known for its lightweight, high tensile strength, exceptional corrosion resistance, and low thermal coefficient of expansion due to which it finds its application in precision surgical instruments and aeronautical and marine engine parts. In this study, Ti-6Al-4V has been machined using wire EDM machine with two different wire materials. The wire thickness of 0.25 mm heat-treated brass and zinc-coated brass tool and super cool oil as dielectric fluid were utilized for the machining process. Taguchi L9 orthogonal array was used. Ton (Pulse ON time), Toff (Pulse OFF time), wire feed, and Ip (Current) were the input parameters, and the response parameters such as surface roughness (Ra), material removal rate (MRR), tool wear rate (TWR), and recast layer thickness were optimized by TOPSIS optimization. From the results, higher tool wear rate was observed in brass tool. The titanium percentage after machining was found to be 55.05% for zinc-coated brass wire, whereas it was 51.58% for brass wire. Better productivity and surface integrity were observed in zinc-coated brass tool compared to heat-treated brass. MRR increased to 54.93%, surface roughness decreased to 2.40%, recast layer thickness increased to 3.44%, and tool wear rate was increased to 47.96%.

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Section: Field Emission Scanning Electron Microscopy (Fesem)supporting

confidence: 84%

Experimental Investigation of Heat-Treated Tool on Wire Electric Discharge Machining of Titanium Alloy (Ti-6Al-4V)

Samson

Sridhar

Ranjith

et al. 2022

Advances in Materials Science and Engineering

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“…Wire Electrical Discharge Machining (WEDM) parameters have been the subject of extensive research. Some noteworthy studies include the application of grey relational analysis (GRA) for optimizing WEDM of Titanium (grade 2) while taking into account multiple output parameters (Sahoo et al 2019), the use of vegetable oil as a dielectric fluid with Taguchi-based hybrid optimization (Singaravel et al 2020), and the investigation of the effects of various process parameters on material removal rate, surface roughness, kerf width, and dimensional deviation in WEDM of Ti49.5Ni50.6 SMA (Takale & Chougule 2019). The use of distilled water as a dielectric fluid, a brass wire with a 0.25mm diameter tool, and molybdenum high-speed tool steel as a workpiece has also been investigated in research (Deshpande 2019).…”

Section: Proceedings Of the 6th International Conference On Industrialmentioning

confidence: 99%

“…It has also been researched how process factors affect the pace at which material is removed when cutting aluminum alloy (5086) with WEDM (Kumar & Sharma 2020). To reduce wire lifetime (WLT) and increase material removal rate, it has also been investigated to optimize the Wire EDM process parameters using the Taguchi technique and the creation of Jaya algorithms (Fakkir Mohamed & Lenin 2020;Sahoo et al 2019). A metal matrix composite (MMC) composed of aluminum 7075, boron carbide, and graphite (Al/B4C/Gr) has also been noted for its effect on output responsiveness due to process variables (Rizwee & Rao 2021).…”

Section: Proceedings Of the 6th International Conference On Industrialmentioning

confidence: 99%

Optimization of Wire EDM Process Parameters in the Fabrication of Spur Gear Using Taguchi Technique

Kaosar Saad,

Al Javed,

Rahman

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Wire EDM is a specialized thermal machining method that uses a thin wire electrode to cut conductive materials with high precision, including complicated shapes and sharp edges. The investigation uses a CNC Wire EDM machine with a molybdenum wire electrode and an AISI 4140 steel workpiece. The aim was to achieve superior surface finish and dimensional accuracy for spur gears. Control parameters like current, Pulse on (T-on) time, and pulse off (T-off) time were chosen to experiment with the performance measure in terms of surface roughness. 9 experimental runs (L9 OA) were conducted in total, using an orthogonal array, and for the Surface Roughness, the appropriate combination of control factor levels was identified. The Minitab-18 program is used to apply the Taguchi method to determine the level of importance and optimum machining parameters. The findings offer valuable insights for manufacturers to enhance product quality and optimize manufacturing processes.

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“…Yusoff et al 10 applied the ANN-GA SLO methodology to optimize the WEDM performance of Ti–48Al intermetallic alloys and observed that the 5–6–6–4 feed-forward back propagation neural network (FFNN) is the most precise architecture with very good prediction accuracy. Sahoo et al 11 used GRA methodology for optimization of the WEDM performance of titanium alloy (grade 2) and found that the contribution of peak current to overall performance is the highest compared to other input variables. In a different investigation on Ti64 (grade 5) alloy, the ANN-GA strategy was used to maximize MRR and reduce SR values.…”

Section: Introductionmentioning

confidence: 99%

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

Biswas,

Singh,

Mukherjee

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the field of machining different grades of alloys using wire electrical discharge machining (WEDM), developing efficient techniques has been a challenge. Typically, specific supervised learning and optimization (SLO) methods are created for individual alloys, resulting in a time-consuming process. To address this issue, a unified SLO approach capable of handling various grades of alloys with different compositions and physical characteristics is proposed. In this study, three stochastic optimization tools are combined with a multi-layer neural network to determine the optimal SLO strategy. Five different alloy grades are considered, with inputs consisting of six variables, including chemical composition, and physical properties. The neural network architecture used is 19-500-500-4, and it is combined with a gray wolf stochastic algorithm, proving to be the most effective SLO methodology. The results indicate that regardless of the alloy grade, the pulse on time significantly affects the material removal rate, kerf width, and thickness of the recast layer (RL). Surface roughness is primarily influenced by alloy properties such as density, specific heat, and elements like Cr, Mo, and Ni. Additionally, the recast layer is dependent on factors such as V, Ti, thermal conductivity, and thermal diffusivity of the materials. Validation experiments demonstrate that the mean squared error of all responses for each alloy remains within 5.0%. This unified SLO approach provides a promising solution for optimizing the machining process of different alloy grades, reducing the need for separate techniques for each material.

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Analysis and Optimization of Wire EDM Process of Titanium by Using GRA Methodology

Cited by 9 publications

References 15 publications

Experimental Investigation of Heat-Treated Tool on Wire Electric Discharge Machining of Titanium Alloy (Ti-6Al-4V)

Experimental Investigation of Heat-Treated Tool on Wire Electric Discharge Machining of Titanium Alloy (Ti-6Al-4V)

Optimization of Wire EDM Process Parameters in the Fabrication of Spur Gear Using Taguchi Technique

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

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