Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach

Manjaiah, M.; Laubscher, R. F.; Kumar, Anil; Basavarajappa, S.

doi:10.1186/s40712-016-0060-4

Cited by 66 publications

(32 citation statements)

References 24 publications

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“…Abbasi et al [13] developed a mathematical model during machining HSLA steel of 38 HRC in WEDM process considering voltage, current, pulse on-time and wire speed and found that, the effect of pulse on-time is greater for SR trailed by wire speed, power and pulse. Manjaiah et al [14] examined the WEDM characteristics of tool steel D2 using OA design and identified that, servo voltage and pulse on-time are the utmost important parameter that affects SR and MRR through utility approach and found that, machined surface hardness value is greater than the unmachined surface because of frequent quenching effect and the surface containing different oxides due to the formation of recast layer.…”

Section: Fig 1wedm Process [9]mentioning

confidence: 99%

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

Saravanan*¹,

Thiagarajan²,

Somasundaram³

2019

IJEAT

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This work elaborates the experimental work on Material Removal Rate (MRR) and Surface Roughness (SR) output conditions of Wire-Electrical Discharge Machining (WEDM) and finding the optimal input conditions through Taguchi method coupled with Grey relational analysis for lower SR and higher MRR during profile cutting of high strength D3 tool steel, which is the need of the hour in industries. The machining factors considered for investigation which influences MRR and SR were: cutting speed, pulse on-time and off-time, input current, wire tension and feed and servo feed and voltage. A L18 orthogonal array was considered for mixed-level experimental design through Taguchi’s approach and for multi-criteria optimization Grey Relational Analysis was applied. Outcome shows that SR increases with the increase of pulse on-time and decreases with increase in pulse off-time and MRR increases as the pulse on-time increases due to longer spark duration. Both SR and MRR are well within the control limits and servo voltage is the most influential parameter contributing by 48.48%, followed by wire feed rate, input current and servo feed rate with an R2 value of 95.85%, identified through Analysis of Variance (ANOVA). With obtained optimum conditions, a validation experiment was conducted to authenticate the results, which indicates a worthy agreement with predicted output characteristics.

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Section: Fig 1wedm Process [9]mentioning

confidence: 99%

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

Saravanan*¹,

Thiagarajan²,

Somasundaram³

2019

IJEAT

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“…Thiagarajan et al [6] reported the combined influence of machining variables like pulse on duration (P on ), pulse off time (P off ), wire feed (WF) and wire tension (WT) at the MRR and SR in electric discharge machining of AISI D3 steel. A detailed study as multi-objective optimization was made by Manajaiah et al [7] for AISI D2 steel in WEDM and inferred that the MRR, SR and recast layer thickness (RLT) are found in direct proportion with P on . Ikram et al [8] carried an insight investigation in WEDM of AISI D2 steel.…”

Section: Introductionmentioning

confidence: 99%

A Detailed Machinability Assessment of DC53 Steel for Die and Mold Industry through Wire Electric Discharge Machining

Khan

Rehman

Farooq

et al. 2021

Metals

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Recently, DC53 die steel was introduced to the die and mold industry because of its excellent characteristics i.e., very good machinability and better engineering properties. DC53 demonstrates a strong capability to retain a near-net shape profile of the die, which is a very challenging process with materials. To produce complex and accurate die features, the use of the wire electric discharge machining (WEDM) process takes the lead in the manufacturing industry. However, the challenge is to understand the physical science of the process to improve surface features and service properties. In this study, a detailed yet systematic evaluation of process parameters investigation is made on the influence of a wire feed, pulse on duration, open voltage, and servo voltage on the productivity (material removal rate) and material quality (surface roughness, recast layer thickness, kerf width) against the requirements of mechanical-tooling industry. Based on parametric exploration, wire feed was found the most influential parameter on kerf width: KW (45.64%), pulse on time on surface roughness: SR (84.83%), open voltage on material removal rate: MRR (49.07%) and recast layer thickness: RLT (52.06%). Also, the optimized process parameters resulted in 1.710 µm SR, 10.367 mm3/min MRR, 0.327 mm KW, and 10.443 µm RLT. Moreover, the evolution of surface features and process complexities are thoroughly discussed based on the involved physical science. The recast layer, often considered as a process limitation, was explored with the aim of minimizing the layers’ depth, as well as the recast layer and heat-affected zone. The research provides regression models based on thorough investigation to support machinists for achieving required features.

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“…The author concluded that pulse on time, pulse off time, peak current and spark voltage had higher impact on surface roughness [9]. Manjaia et al [10] statistically optimized the pulse on time, pulse off time, servo voltage and wire feed for WEDM of AISI D2 tool steel for the response of MRR and SR. The author resulted the higher significance of pulse on time and servo voltage on performance parameters [10].…”

Section: Introductionmentioning

confidence: 99%

Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy

Kumar

Dhanabalan²,

Narayanan

2019

SN Appl. Sci.

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The applications of artificial intelligence (AI) mainly, the hybrid approaches are becoming more popular and the relevant researches have been conducted in every field of engineering and science by using these AI techniques. Therefore, this research aims to examine the influence of wire electric-discharge machining parameters on performance parameters to improve the productivity with a higher surface finish of Titanium alloy (Ti-6Al-4V) by using the artificial intelligent technique. In this experimental analysis, the adaptive network based fuzzy inference system (ANFIS) model has been highly-developed and the multi-parametric optimization has been done to find the optimal solution for the machining of titanium superalloy. The peak current (I p), taper angle, pulse on time (T on), pulse of time (T off) and the dielectric fluid flow rate had selected as operation constraints to conduct experimental trials. The surface roughness and MRR were considered as output responses. The influence on machining performance has been analyzed by the ANFIS model and the developed model was validated with the full factorial regression models. The developed models showed the minimum mean percentage error and the optimized parameters by the GRA method showed the considerable improvement in the process.

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Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach

Cited by 66 publications

References 24 publications

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

Enhancing the Tool Die Steel P rofile C utting P erformance in WEDM P rocess

A Detailed Machinability Assessment of DC53 Steel for Die and Mold Industry through Wire Electric Discharge Machining

Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy

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