Turan GURGENC

Dr. Turan GURGENC

orcid

Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 January 2019 in Friction
Verified
Journal Article
Open access
PDF file
OAOsman Altay
Osman Altay
TGTuran GURGENC
Turan GURGENC
MUMustafa Ulas
Mustafa Ulas
CÖCihan Özel
Cihan Özel
In this study, experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with (wt.%) 50FeCrC-20FeW-30FeB and 70FeCrC-30FeB powder mixtures by plasma transfer arc welding were determined. The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory. The linear regression (LR), support vector machine (SVM), and Gaussian process regression (GPR) algorithms are used for predicting wear quantities. A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.

8 May 2020 in Journal of Applied Physics
Verified
Journal Article
Closed access
PDF file
TGTuran GURGENC
Turan GURGENC
OAOsman Altay
Osman Altay
MUMustafa Ulas
Mustafa Ulas
COCihan Ozel
Cihan Ozel
Magnesium alloys are popular in the aerospace and automotive industries due to their light weights and high specific strengths. The major disadvantages of magnesium alloys are their weak wear and corrosion resistances. Surface coating is one of the most efficient methods of making material surfaces resistant to wear. Experimental determination of wear loss is expensive and time-consuming. These disadvantages can be eliminated by using machine learning algorithms to predict wear loss. This study used experimentally obtained wear loss data for AZ91D magnesium alloy samples coated via two different spray coating methods (plasma and high velocity oxy-fuel spraying) using various parameters. Support vector regression (SVR) and extreme learning machine (ELM) methods were used to predict wear loss quantities. In models tested using 10-k cross-validation, R2 was calculated as 0.9601 and 0.9901 when the SVR and ELM methods were applied, respectively. The ELM method was more successful than SVR. Thus, the ELM method has excellent potential to support the production of wear-resistant parts for various applications via spray coating.

12 May 2020 in Friction
Verified
Journal Article
Open access
PDF file
MUMustafa Ulas
Mustafa Ulas
OAOsman Altay
Osman Altay
TGTuran GURGENC
Turan GURGENC
CÖCihan Özel
Cihan Özel
Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear. Wear tests involve high cost and lengthy experiments, and require special test equipment. The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost, labor, and time. In this study, wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding (PTAW) method with FeCrC, FeW, and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group. The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests. The wear tests were performed at three different loads (19.62, 39.24, and 58.86 N) over a sliding distance of 900 m. In this study, models have been developed by using four different machine learning algorithms (an artificial neural network (ANN), extreme learning machine (ELM), kernel-based extreme learning machine (KELM), and weighted extreme learning machine (WELM)) on the data set obtained from the wear test experiments. The R2 value was calculated as 0.9729 in the model designed with WELM, which obtained the best performance [with 11among the models evaluated.

11 December 2024 in Case Studies in Thermal Engineering
Verified
Journal Article
Open access
EGEzgi Gurgenc
Ezgi Gurgenc
MGMuhammed Gur
Muhammed Gur
HCHakan Cosanay
Hakan Cosanay
TGTuran GURGENC
Turan GURGENC
HOHakan F. Oztop
Hakan F. Oztop

1 March 2022 in Materials Testing
Verified
Journal Article
Closed access
TGTuran GURGENC
Turan GURGENC
OAOsman Altay
Osman Altay
Magnesium (Mg) alloy parts have become very interesting in industries due to their lightness and high specific strengths. The production of Mg alloys by conventional manufacturing methods is difficult due to their high affinity for oxygen, low melting points, and flammable properties. These problems can be solved using nontraditional methods such as wire electric discharge machining (WEDM). The parts with a quality surface have better properties such as fatigue, wear, and corrosion resistance. Determining the surface roughness (SR) by analytical and experimental methods is very difficult, time-consuming, and costly. These disadvantages can be eliminated by predicting the SR with artificial intelligence methods. In this study, AZ91D was cut with WEDM in different voltage (V), pulse-on-time (µs), pulse-off-time (µs), and wire speed (mm s−1) parameters. The SR was measured using a profilometer, and a total of 81 data were obtained. Multilayer perceptron, ensemble neural network and optimization-based evolving product-unit neural network (EPUNN) were used to predict the SR. It was observed that the EPUNN method performed better than the other two methods. The use of this model in industries producing Mg alloys with WEDM expected to provide advantages such as time, material, and cost.

15 October 2025 in Case Studies in Thermal Engineering
Verified
Journal Article
Open access
EGEzgi Gurgenc
Ezgi Gurgenc
HÖHakan F. Öztop
Hakan F. Öztop
HYHalil İbRahim Yamaç
Halil İbRahim Yamaç
CCCanan Aksu Canbay
Canan Aksu Canbay
ŞŞŞafak Melih Şenocak
Şafak Melih Şenocak
MGMuhammed Gür
Muhammed Gür