Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks

Thankachan, Titus; Prakash, K. Soorya; Malini, R; Ramu, S.; Sundararaj, Prabhu; Rajandran, Sivakumar; Rammasamy, Devaraj; Jothi, Sathiskumar

doi:10.1016/j.apsusc.2018.06.117

Cited by 62 publications

(29 citation statements)

References 46 publications

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“…Similarly, with higher input current, MRR increases but SR increases initially and afterwards it reduces considerably. With higher wire feed, SR increases drastically [31], but MRR reduces until 4 mm/min and then upsurges with higher feed rate of wire electrode. With higher wire tension, SR increases significantly, but MRR tends to reduce.…”

Section: Resultsmentioning

confidence: 93%

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

Saravanan*,

Thiyagarajan,

Somasundaram

2020

IJITEE

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In this work, the effect of brass wire coated with zinc as electrode wire towards maximizing MRR and minimizing SR for the considered input parameters was studied in elaborate during profile cutting of tool steel AISI D3. Experiments were designed based on the L18(21 ,37 ) Taguchi’s orthogonal array considering cutting speed, Ton and Toff, wire tension, servo feed rate and voltage, current and electrode feed rate. The outputs MRR and SR were analyzed and optimized using Grey Relational Analysis (GRA) simultaneously. For both SR and MRR, the drawn chart shows that, the raw data’s lies within upper and lower control limits. The optimum input conditions obtained for higher MRR and lower SR were: 75% of cutting speed, 128 µsec of Ton, 54µsec of Toff, 190A of input current, electrode feed of 3 mm/min with 7 gm of electrode tension, 10V of servo voltage and 2060 mm/min of 10V. Execution of ANOVA on grey grade identifies that, wire tension was the parameter that influences more by 25.89%, followed by Ton and servo voltage by 21.37% and feed rate of wire by 11.89%, with an R2 value of 96.55%. For validating the results, confirmation experiment was also done

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Section: Resultsmentioning

confidence: 93%

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

Saravanan*,

Thiyagarajan,

Somasundaram

2020

IJITEE

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“…The material properties such as hardness, tensile strength, impact strength and density were measured. The mechanical properties mainly depend on the percentage of reinforcement particles which were added in to the composite [44]. Brinell hardness tester, universal testing machine, izod testing machine and Archimedes principle was used to measure the hardness, tensile strength impact strength and density of the metal matrix respectively.…”

Section: Properties Of Niobium Metal Matrixmentioning

confidence: 99%

“…A novel aluminium alloy were fabricated and analysed for various healing purpose. Various parameters such as Pulse On time (PON), Pulse Off time (POFF), wire feed rate (WFR) along with the material elemental composition parameters Sn wt% and SiC wt% using Taguchi coupled Grey Relational Analysis were carried out [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Material characterization and unconventional machining on synthesized Niobium metal matrix

Sivakandhan¹,

Loganathan

Murali

et al. 2020

Mater. Res. Express

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The Purpose of this work is to develop a niobium based metal matrix alloy through sintering based powder metallurgy technique. The 2, 4 and 6 weight percentage of Titanium Carbide (TiC) is added to the alloy. Various material properties such as hardness, tensile strength, impact strength and density are measured after the addition of TiC particle. The characterization, microstructure and particle size of the developed metal matrix are learnt through Scanning Electron Microscope (SEM) and Energy

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“…Ajith Arul Daniel et al (17) used an artificial neural network (ANN) to carry out prediction and parameter optimization research on Taguchi quality and grey relational analysis (GRA) to examine milling machine performance. Thankachan et al (18) used the Taguchi method, GRA, and an ANN to predict and optimize the surface roughness of products made of aluminum alloys and the material removal rate. Tamiloli et al (19) performed GRA using Taguchi factorial experiments and developed an adaptive neuro-fuzzy inference system (ANFIS) model to optimize parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Lathe Cutting Parameters Using Taguchi Method and Grey Relational Analysis

Jian¹,

Wang²,

Yau³

et al. 2020

Sensors and Materials

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In the current precision industry, the rapid production of high-quality parts in bulk quantities has led to high competitiveness. In this study, the Taguchi method and grey relational analysis (GRA) approach were used in a practical investigation of precision lathe processing. The purpose was to find optimal parameters for single-target and multitarget cutting. The production of targets of the highest quality was the research focus, with the aim of strengthening the links between this study and the application to the processing industry. Precision, surface roughness, and material removal rate were selected as targets for improvement. The parameters commonly used for lathe processing were set as control factors, and cutting depth, spindle speed, feed rate, and material elongation were set as experimental factors. The results showed that in the cutting of materials, cutting precision was mainly affected by the depth of cut and spindle speed, surface roughness by spindle speed, and the material removal rate by the cutting depth. In a comparison of the quality loss for the same materials using previous parameters, the cutting precision has about 64 to 99% optimization, the surface roughness has 69 to 96% optimization, and the material removal rate has more than 90% optimization. GRA was also employed to analyze the sequences of parameters from the Taguchi experiments to obtain the target relationships and to find the various combinations of factors for improvement.

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Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks

Cited by 62 publications

References 46 publications

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

Material characterization and unconventional machining on synthesized Niobium metal matrix

Optimization of Lathe Cutting Parameters Using Taguchi Method and Grey Relational Analysis

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