Akar, Samet

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Profile URL
https://hdl.handle.net/20.500.14411/8347
Name Variants
S., Akar
Akar, Samet
Samet, Akar
A., Samet
Akar,S.
S.,Akar
A.,Samet
Job Title
Doktor Öğretim Üyesi
Email Address
samad.nadimi@atilim.edu.tr
Main Affiliation
Department of Mechanical Engineering
Status
Former Staff
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WoS Researcher ID

Sustainable Development Goals

2

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0

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14

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17

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5

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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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8

DECENT WORK AND ECONOMIC GROWTH
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4

QUALITY EDUCATION
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6

CLEAN WATER AND SANITATION
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7

AFFORDABLE AND CLEAN ENERGY
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10

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11

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9

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2

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1

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3

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Scholarly Output

5

Articles

4

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1/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

16

Scopus Citation Count

23

WoS h-index

2

Scopus h-index

3

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0

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0

WoS Citations per Publication

3.20

Scopus Citations per Publication

4.60

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0

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1

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JournalCount
Artificial Intelligence for Engineering Design, Analysis and Manufacturing1
International Journal of Mechatronics and Manufacturing Systems1
Machining Science and Technology1
The International Journal of Advanced Manufacturing Technology1
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Author: Akar,S.

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  • Thumbnail Image
    Article
    Citation - Scopus: 5
    Micro-Wedm of Ni55.8ti Shape Memory Superalloy: Experimental Investigation and Optimisation
    (Inderscience Publishers, 2021) Meshri,H.A.M.; Akar,S.; Seyedzavvar,M.; Kiliç,S.E.
    Nickel-titanium superalloy has gained significant acceptance for engineering applications as orthotropic implants, orthodontic devices, automatic actuators, etc. Considering the unique properties of these alloys, such as high hardness, toughness, strain hardening, and development of straininduced martensite, micro-wire electro-discharge machining (μ-WEDM) process has been accepted as one of the main options for cutting intricate shapes of these alloys in micro-scale. This paper presents the results of a comprehensive study to address the material removal rate (MRR) and surface integrity of Ni55.8Ti shape memory superalloy (SMA) in the μ-WEDM process. The effects of discharge current, pulse on-time, pulse off-time, and servo voltage on the performance of this process, including MRR, white layer thickness, surface roughness, and micro-hardness of the machined surface, were investigated by multi-regression analysis using response surface methodology (RSM). The optimisation of input parameters based on the gradient and the swarm optimisation algorithms were also conducted to maximise the MRR and minimise the white layer thickness, surface roughness, and micro-hardness of the machined samples. © 2021 Inderscience Enterprises Ltd.. All rights reserved.