PMID- 35888844
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220731
IS  - 2072-666X (Print)
IS  - 2072-666X (Electronic)
IS  - 2072-666X (Linking)
VI  - 13
IP  - 7
DP  - 2022 Jun 28
TI  - Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape 
      Memory Alloy.
LID - 10.3390/mi13071026 [doi]
LID - 1026
AB  - Nitinol-shape memory alloys (SMAs) are widely preferred for applications of 
      automobile, biomedical, aerospace, robotics, and other industrial area. 
      Therefore, precise machining of Nitinol SMA plays a vital role in achieving 
      better surface roughness, higher productivity and geometrical accuracy for the 
      manufacturing of devices. Wire electric discharge machining (WEDM) has proven to 
      be an appropriate technique for machining nitinol shape memory alloy (SMA). The 
      present study investigated the influence of near-dry WEDM technique to reduce the 
      environmental impact from wet WEDM. A parametric optimization was carried out 
      with the consideration of design variables of current, pulse-on-time (T(on)), and 
      pulse-off-time (T(off)) and their effect were studied on output characteristics 
      of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of 
      nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to 
      investigate the impact of design variables on response measures. ANOVA results 
      depicted the significance of the developed quadratic model for both MRR and SR. 
      Current, and T(on) were found to be major contributors on the response value of 
      MRR, and SR, respectively. A teaching-learning-based optimization (TLBO) 
      algorithm was employed to find the optimal combination of process parameters. 
      Single-response optimization has yielded a maximum MRR of 1.114 mm(3)/s at T(on) 
      of 95 micros, T(off) of 9 micros, current of 6 A. Least SR was obtained at T(on) of 35 
      micros, T(off) of 27 micros, current of 2 A with a predicted value of 2.81 microm. Near-dry 
      WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while 
      the performance of SR has been substantially improved by 41.56%. As per the 
      obtained results from SEM micrographs, low viscosity, reduced thermal energy at 
      IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM 
      has provided better surface morphology over the wet-WEDM process in terms of 
      reduction in surface defects and better surface quality of nitinol SMA. Thus, for 
      obtaining the better surface quality with reduced surface defects, near-dry WEDM 
      process is largely suitable.
FAU - Chaudhari, Rakesh
AU  - Chaudhari R
AUID- ORCID: 0000-0001-6904-2362
AD  - Department of Mechanical Engineering, School of Technology, Pandit Deendayal 
      Energy University, Raysan, Gandhinagar 382007, India.
FAU - Kevalramani, Aniket
AU  - Kevalramani A
AD  - Department of Mechanical Engineering, School of Technology, Pandit Deendayal 
      Energy University, Raysan, Gandhinagar 382007, India.
FAU - Vora, Jay
AU  - Vora J
AUID- ORCID: 0000-0002-7543-903X
AD  - Department of Mechanical Engineering, School of Technology, Pandit Deendayal 
      Energy University, Raysan, Gandhinagar 382007, India.
FAU - Khanna, Sakshum
AU  - Khanna S
AUID- ORCID: 0000-0001-7918-3206
AD  - Journal of Visualized Experiments, Delhi 110016, India.
FAU - Patel, Vivek K
AU  - Patel VK
AUID- ORCID: 0000-0001-7508-186X
AD  - Department of Mechanical Engineering, School of Technology, Pandit Deendayal 
      Energy University, Raysan, Gandhinagar 382007, India.
FAU - Pimenov, Danil Yurievich
AU  - Pimenov DY
AUID- ORCID: 0000-0002-5568-8928
AD  - Department of Automated Mechanical Engineering, South Ural State University, 
      Lenin Prosp. 76, 454080 Chelyabinsk, Russia.
FAU - Giasin, Khaled
AU  - Giasin K
AUID- ORCID: 0000-0002-3992-8602
AD  - School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth 
      PO1 3DJ, UK.
LA  - eng
PT  - Journal Article
DEP - 20220628
PL  - Switzerland
TA  - Micromachines (Basel)
JT  - Micromachines
JID - 101640903
PMC - PMC9320167
OTO - NOTNLM
OT  - near-dry wire electric discharge machining (WEDM)
OT  - nitinol
OT  - optimization
OT  - shape memory alloys
OT  - teaching-learning based optimization (TLBO) algorithm
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/28 06:00
MHDA- 2022/07/28 06:01
PMCR- 2022/06/28
CRDT- 2022/07/27 01:32
PHST- 2022/05/23 00:00 [received]
PHST- 2022/06/25 00:00 [revised]
PHST- 2022/06/27 00:00 [accepted]
PHST- 2022/07/27 01:32 [entrez]
PHST- 2022/07/28 06:00 [pubmed]
PHST- 2022/07/28 06:01 [medline]
PHST- 2022/06/28 00:00 [pmc-release]
AID - mi13071026 [pii]
AID - micromachines-13-01026 [pii]
AID - 10.3390/mi13071026 [doi]
PST - epublish
SO  - Micromachines (Basel). 2022 Jun 28;13(7):1026. doi: 10.3390/mi13071026.