Investigation of Electrochemical Micromachining Process Using Ultrasonic Heated Electrolyte

Soundarrajan, M.; Thanigaivelan, R.

doi:10.1007/978-981-32-9425-7_38

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…(i) Leaders position, Each weight tries to achieve the best position during the position updating procedure. (7) Where X i and X i+1 represents the position of the current and the next iterations.…”

Section: Exploration Stagementioning

confidence: 99%

Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

Kanniyappan

Sekar²

2022

Polish Journal of Chemical Technology

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In our work, the process efficiency of the ECMM should be improved by using different combinations of nano-particles and added electrolytes. The superior aim of this work is to improve and predict the ECMM machining characteristics of die hardened steel, namely material removal rate (MRR), Tool wear rate (TWR) and Surface Roughness (Ra). The machining conditions are optimized using Response Surface Methodology (RSM) based on Box Behnken Design. The better Nano electrolyte is optimized using Deer Hunting Optimization (DHO) based on the machined outcomes, and the performances are predicted using a hybrid Deep Neural Network (DNN) based DHO. The hybrid DNN-DHO based predicted outcome of MRR is 0.361 mg/min, TWR is 0.272 mg/min and Ra is 2.511 μm. The validation results show that our proposed DNN-DHO model performed well and obtained above 0.99 regression for both training and validation of DNN-DHO, where the root mean square error ranges between 0.018 and 0.024.

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Section: Exploration Stagementioning

confidence: 99%

Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

Kanniyappan

Sekar²

2022

Polish Journal of Chemical Technology

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“…Optimisation of the process parameters through TOPSIS technique is one of the prominent solutions for determining the optimal parameter combination from the experimental plan. The following procedures were considered in TOPSIS [18,19,22].…”

Section: Multi-objective Optimisation Techniquementioning

confidence: 99%

“…Very few researchers have investigated the heating of the machining zone, and no studies have investigated ultraviolet (UV)-and hot air (HA)-assisted conventional machining. These heating techniques, however, have been successfully used in other manufacturing sectors to improve the machining performance of non-traditional machining processes, such as biomedicine and electrical sectors [17][18][19][20]. In this study, UV and IR rays and HA are used to preheat a workpiece, and the results are compared with those obtained under non-heated machining conditions.…”

Section: Introductionmentioning

confidence: 99%

Performance optimisation of the turning process along with multi-surface heating process

Kumar¹,

Thanigaivelan

Natarajan³

2022

Materials Science-Poland

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Materials that are difficult to cut possess excellent qualities and machinability, though conventional machining techniques require additional energy to circumvent the problems associated with the turning process. In this study, heat-assisted turning of duplex stainless steel (SS) was carried out. Various heating techniques such as infrared (IR)-, ultraviolet (UV)- and hot air (HA)-assisted heating were adopted. The experiment used an L16 orthogonal array with the most significant parameters such as heating method, feed rate in millimetres per revolution (mm/rev), depth of cut (millimetres [mm]) and cutting speed (metres per minute [m/min])on the cutting force and surface roughness. The technique for order performance by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), were used to optimise the output performance. The results of TOPSIS showed that the 16th experimental combination, i.e., the HA heating method, with feed rate = 0.175 mm/rev, depth of cut = 0.1 mm and cutting speed = 150 m/min, required a smaller cutting force and resulted in lower surface roughness. In case of the GRA method, the best output performance was observed for the 15th experimental combination, that is, the HA heating method, with feed rate = 0.15 mm/rev, depth of cut = 0.2 mm and cutting speed = 200 m/min. Compared to the non–heat-assisted turning process, the HA- and UV-assisted processes required 10.25% and 7.69% lesser cutting force, respectively, and the surface roughness in case of the HA method was 15.13% lesser.

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“…TOPSIS is an appropriate technique to identify the suitable parametric solution from the set-off experimental combinations. The procedures followed in this method are provided below [30] and [31].…”

Section: Technique For Order Of Preference By Similarity To Ideal Solution (Topsis)mentioning

confidence: 99%

“…With the GRA method, the output responses of different units should be converted into a homogeneous form (i.e., unit-less number). Therefore, the experimental results are converted from zero to one through the below-mentioned equations [30] and [31]. The output values (i.e., MRR, TWR and SR values) are estimated using Eqs.…”

Section: Grey Relational Analysis Technique (Gra)mentioning

confidence: 99%

Performance Study of EDM Process Parameters Using TiC/ZrSiO4 Particulate-Reinforced Copper Composite Electrode

Saminatharaja¹,

Periyakgounder²,

Selvaraj³

et al. 2021

SV-JME

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Electrical discharge machines (EDM) are widely employed in machining components containing complex profiles of hard-to-cut and machining materials. However, the fabrication-of-tool time for the EDM process is excessively high in the traditional machining method, which significantly affects the machining rate. Therefore, in this paper, a powder metallurgy (PM) technique is employed to fabricate the tool electrode using copper (Cu), titanium carbide (TiC), and zirconium silicate (ZrSiO4) for different combinations. An L18 orthogonal array (OA) is planned using the following input parameters: three types of tools (Cu, Cu90, Cu80), peak current (PC) [A], pulse on time (PT) [µs], and gap voltage (GV) [V]. The performance of EDM is evaluated through the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The process parameters are optimized using two different techniques: the technique for order of preference by similarity to the ideal solution (TOPSIS) and grey relational analysis (GRA). TOPSIS and GRA optimization techniques produce the same optimal parametric solution for less TWR, SR, and higher MRR with the combination of the Cu90 tool, E8 APC, 15 µs pulse PT, and 75 V GV. Based on the ANOVA table of TOPSIS, pulse on time plays a major role, contributing 46.8 % of the machining performance; peak current shows the most significant contribution of 39.3 % of the machining performance using GRA values. Furthermore, the scanning electron microscope (SEM) image analyses are carried out on the machined workpiece surface to understand the effect of tools on machining quality.

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Investigation of Electrochemical Micromachining Process Using Ultrasonic Heated Electrolyte

Cited by 11 publications

References 11 publications

Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

Performance optimisation of the turning process along with multi-surface heating process

Performance Study of EDM Process Parameters Using TiC/ZrSiO4 Particulate-Reinforced Copper Composite Electrode

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