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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ORCID ID
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WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
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ZERO HUNGER2
ZERO HUNGER
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
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CLIMATE ACTION13
CLIMATE ACTION
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LIFE BELOW WATER14
LIFE BELOW WATER
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LIFE ON LAND15
LIFE ON LAND
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
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PARTNERSHIPS FOR THE GOALS17
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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

Patents

0

Projects

0

WoS Citations per Publication

3.20

Scopus Citations per Publication

4.60

Open Access Source

0

Supervised Theses

1

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: Ugurer, DorukSubject: ANN

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    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; Ayll, Ece
    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.