Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Chaudhari, Rakesh; Vora, Jay; Prabu, S.S. Mani; Palani, I. A.; Patel, Vivek; Parikh, Dilip M.

doi:10.1007/s40436-019-00267-0

Cited by 70 publications

(39 citation statements)

References 48 publications

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“…An optimized set of parameters was determined to achieve multiresponse variables such as MRR, SR and MH. For obtaining a higher MRR, high discharge energy was required, which could be achieved with higher values of pulse-on time and current [10,25]. However, for obtaining a lower SR, low discharge energy was required thatcould be achieved with lower values of pulse-off time [10,25].…”

Section: Scanning Morphologymentioning

confidence: 99%

“…Materials with superelastic and shape-memory effects are two differentSMA categories. The particular material is said to exhibit a superelastic effect if the shape-recovery temperature is below room temperature; if it is above room temperature, it produces a shape-memory effect [10]. However, the conventional machining of nitinol is challenging due to its superelasticity, high ductility, high wear and corrosion resistance, and severe strain hardening [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Their results revealed that pulse-on time (T on ) and pulse-off time (T off ) highlighted a significant effect on all output variables. In another study, Chaudhari et al [10] concluded that T on , T off , and current are the main influencing factors for the response variables of the material-removal rate(MRR), surface roughness (SR), and microhardness for the WEDMof nickel-based alloys. Soni et al [23] explored the WEDM of Ti50Ni40Co10 SMA with the output variables of MRR and SR. Increasing values of MRR and SR were reported with increasing T on , while both values decreased with increasing T off and servo voltage.…”

Section: Introductionmentioning

confidence: 99%

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Surface Analysis of Wire-Electrical-Discharge-Machining-Processed Shape-Memory Alloys

et al. 2020

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Shape-memory alloys such as nitinol are gaining popularity as advanced materials in the aerospace, medical, and automobile sectors. However, nitinol is a difficult-to-cut material because of its versatile specific properties such as the shape-memory effect, superelasticity, high specific strength, high wear and corrosion resistance, and severe strain hardening. Anunconventional machining process like wire-electrical-discharge-machining (WEDM) can be effectively and efficiently used for the machining of such alloys, although the WEDM-induced surface integrity of nitinol hassignificant impact on material performance. Therefore, this work investigated the surface integrity of WEDM-processed nitinol samples using digital microscopy imaging, scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) analysis. Three-dimensional analysis of the surfaces was carried out in two different patterns (along the periphery and the vertical plane of the machined surface) andrevealed that surface roughness was maximalat the point where the surface was largely exposed to the WEDM dielectric fluid. To attain the desired surface roughness, appropriate discharge energy is required that, in turn, requires the appropriate parameter settings of the WEDM process. Different SEM image analyses showed a reduction in microcracks and pores, and in globule-density size at optimized parameters. EDX analysis revealed the absence of wire material on the machined surface

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Section: Scanning Morphologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface Analysis of Wire-Electrical-Discharge-Machining-Processed Shape-Memory Alloys

et al. 2020

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“…Results found that powder concentration and current mainly influences both objectives. Past studies on Nitinol SMA has shown retention of shape memory effect even after WEDM machining using Molybdenum wire as tool material [ 8 , 20 ]. Hence, the current study focuses more on the effect of Nano-graphene powder concentration on WEDM process parameters of Nitinol SMA and the parametric optimization of the selected responses.…”

Section: Introductionmentioning

confidence: 99%

Parametric Optimization and Effect of Nano-Graphene Mixed Dielectric Fluid on Performance of Wire Electrical Discharge Machining Process of Ni55.8Ti Shape Memory Alloy

et al. 2021

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In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi’s L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm3/s, and minimum SR of 2.68 µm. The Pareto curve was generated which gives the optimal non-dominant solutions.

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“…The author emphasized that POD was found to be the most significant in the responses measured. The response surface method (RSM) and heat transfer search algorithm (HTSA) were applied to select the optimal values of the POD, POF, and AC for the WEDM of the NiTi alloy [3]. The obtained results indicated that the optimal outcomes of the MRR, hardness (MH), and Ra were 1.49 mm 3 /s, 462.52 HVN, and 0.11 µm, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Optimization of the WEDM Process for Surface Characteristics and Productivity

Nguyen

2021

Teh. vjesn.

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Wire-cut electrical discharge machining (WEDM) process is a proficient operation for the precise manufacturing of complex profiles of difficult-to-cut materials. The purpose of the current paper is to determine optimal processing inputs, including the WEDM current (CU), WEDM voltage (DV), pulse duration (Ton), and the speed of the wire electrode (WS) to decrease the depth of the recast layer (DL) as well as the root mean square roughness (RMSR) and enhance the cutting speed (CS) of the WEDM operation. The radius basis function (RBF) approach is employed to develop the predictive models of technical responses. The non-dominated sorting particle swarm optimization (NSPSO) is applied to obtain the optimal values of processing inputs and WEDM performances measured. The findings revealed that the proposed RBF models significantly contribute to the accurate prediction for the WEDM outputs. The optimal values of the CU, Ton, DV, and WS are 5.0 A, 6.0 μs, 33.0 V, and 4 m/min, respectively.The optimized values of the RMSR and DL are decreased by 60.98% and 15.55%, respectively, while the CS is enhanced by 8.90%. This work can be listed as an alternative solution for improving the surface characteristics and productivity of the WEDM process.

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Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Cited by 70 publications

References 48 publications

Surface Analysis of Wire-Electrical-Discharge-Machining-Processed Shape-Memory Alloys

Surface Analysis of Wire-Electrical-Discharge-Machining-Processed Shape-Memory Alloys

Parametric Optimization and Effect of Nano-Graphene Mixed Dielectric Fluid on Performance of Wire Electrical Discharge Machining Process of Ni55.8Ti Shape Memory Alloy

Multi-Attribute Optimization of the WEDM Process for Surface Characteristics and Productivity

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