Akar, Samet

Profile Picture
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
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

2

ZERO HUNGER
ZERO HUNGER Logo

0

Research Products

14

LIFE BELOW WATER
LIFE BELOW WATER Logo

0

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

0

Research Products

5

GENDER EQUALITY
GENDER EQUALITY Logo

0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

0

Research Products

4

QUALITY EDUCATION
QUALITY EDUCATION Logo

0

Research Products

6

CLEAN WATER AND SANITATION
CLEAN WATER AND SANITATION Logo

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

0

Research Products

10

REDUCED INEQUALITIES
REDUCED INEQUALITIES Logo

0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

2

Research Products

1

NO POVERTY
NO POVERTY Logo

0

Research Products

3

GOOD HEALTH AND WELL-BEING
GOOD HEALTH AND WELL-BEING Logo

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
RESPONSIBLE CONSUMPTION AND PRODUCTION Logo

0

Research Products

13

CLIMATE ACTION
CLIMATE ACTION Logo

0

Research Products

15

LIFE ON LAND
LIFE ON LAND Logo

0

Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

5

Articles

4

Views / Downloads

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

Patents

0

Projects

0

WoS Citations per Publication

3.20

Scopus Citations per Publication

4.60

Open Access Source

0

Supervised Theses

1

Google Analytics Visitor Traffic

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
Current Page: 1 / 1

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

20231
Reset filters

Settings

Subject: ANN

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 1
    Prediction of the Onset of Shear Localization Based on Machine Learning
    (Cambridge Univ Press, 2023) Akar, Samet; Ayli, Ece; Ulucak, Oguzhan; Ugurer, Doruk
    Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R-2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors' knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.