Cem Sınanoğlu

524 total citations
26 papers, 421 citations indexed

About

Cem Sınanoğlu is a scholar working on Mechanical Engineering, Mechanics of Materials and Industrial and Manufacturing Engineering. According to data from OpenAlex, Cem Sınanoğlu has authored 26 papers receiving a total of 421 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Mechanical Engineering, 5 papers in Mechanics of Materials and 5 papers in Industrial and Manufacturing Engineering. Recurrent topics in Cem Sınanoğlu's work include Tribology and Lubrication Engineering (14 papers), Hydraulic and Pneumatic Systems (12 papers) and Gear and Bearing Dynamics Analysis (7 papers). Cem Sınanoğlu is often cited by papers focused on Tribology and Lubrication Engineering (14 papers), Hydraulic and Pneumatic Systems (12 papers) and Gear and Bearing Dynamics Analysis (7 papers). Cem Sınanoğlu collaborates with scholars based in Türkiye and Italy. Cem Sınanoğlu's co-authors include Şahin Yıldırım, Hüseyin Rıza Börklü, Fehmi Nair, M.B. Karamış, Erdem Koç, Emel Kızılkaya Aydoğan, Ayşegül Güven, Hasan Badem and Abdullah Çalışkan and has published in prestigious journals such as Journal of Materials Processing Technology, Journal of Intelligent Manufacturing and Tribology Letters.

In The Last Decade

Cem Sınanoğlu

25 papers receiving 399 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Cem Sınanoğlu Türkiye 13 309 113 96 42 36 26 421
Alžbeta Sapietová Slovakia 13 323 1.0× 78 0.7× 140 1.5× 12 0.3× 40 1.1× 61 457
Shu-Han Juang Taiwan 5 597 1.9× 86 0.8× 108 1.1× 8 0.2× 12 0.3× 8 716
Shunuan Liu China 11 314 1.0× 89 0.8× 57 0.6× 13 0.3× 12 0.3× 30 454
Vladimír Dekýš Slovakia 12 230 0.7× 95 0.8× 55 0.6× 10 0.2× 32 0.9× 60 354
Auteliano Antunes dos Santos Brazil 12 344 1.1× 237 2.1× 40 0.4× 24 0.6× 23 0.6× 80 466
Ching‐Kao Chang Taiwan 6 265 0.9× 29 0.3× 79 0.8× 7 0.2× 15 0.4× 9 366
Miroslav Dovica Slovakia 11 278 0.9× 47 0.4× 46 0.5× 8 0.2× 94 2.6× 23 353
Huamin Zhou China 11 194 0.6× 44 0.4× 87 0.9× 7 0.2× 19 0.5× 37 335
Yii-Wen Hwang Taiwan 11 345 1.1× 59 0.5× 60 0.6× 15 0.4× 106 2.9× 12 394

Countries citing papers authored by Cem Sınanoğlu

Since Specialization
Citations

This map shows the geographic impact of Cem Sınanoğlu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Cem Sınanoğlu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cem Sınanoğlu more than expected).

Fields of papers citing papers by Cem Sınanoğlu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cem Sınanoğlu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Cem Sınanoğlu. The network helps show where Cem Sınanoğlu may publish in the future.

Co-authorship network of co-authors of Cem Sınanoğlu

This figure shows the co-authorship network connecting the top 25 collaborators of Cem Sınanoğlu. A scholar is included among the top collaborators of Cem Sınanoğlu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Cem Sınanoğlu. Cem Sınanoğlu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Aydoğan, Emel Kızılkaya, et al.. (2021). Investigation of different denim fabrics with fabric touch tester and sensory evaluation. Journal of Natural Fibers. 19(13). 5551–5565. 14 indexed citations
2.
Sınanoğlu, Cem, et al.. (2020). Prediction of Leakage from an Axial Piston Pump Slipper with Circular Dimples Using Deep Neural Networks. Chinese Journal of Mechanical Engineering. 33(1). 18 indexed citations
3.
Sınanoğlu, Cem, et al.. (2019). Prediction of Slipper Pressure Distribution and Leakage Behaviour in Axial Piston Pumps Using ANN and MGGP. Mathematical Problems in Engineering. 2019(1). 9 indexed citations
4.
Sınanoğlu, Cem, et al.. (2018). Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm. Mathematical Problems in Engineering. 2018. 1–14. 20 indexed citations
5.
Sınanoğlu, Cem, et al.. (2014). EXPERIMENTAL ANALYSES OF TBE EFFECTS OF SLIPPER BEARING GEOMETRY AND WORKING CONDITIONS ON THE SYSTEM LOAD CARRYING CAPACITY IN AXIAL PISTON PUMPS. 4 indexed citations
6.
Sınanoğlu, Cem. (2009). Investigation of load carriage capacity of journal bearings by surface texturing. Industrial Lubrication and Tribology. 61(5). 261–270. 29 indexed citations
7.
Koç, Erdem, et al.. (2009). Design of artificial neural networks for slipper analysis of axial piston pumps. Industrial Lubrication and Tribology. 61(2). 67–77. 19 indexed citations
8.
Sınanoğlu, Cem, et al.. (2009). Experimental analysis of frictional power loss of hydrostatic slipper bearings. Industrial Lubrication and Tribology. 61(3). 123–131. 32 indexed citations
9.
Sınanoğlu, Cem. (2008). Design of An Artificial Neural Network for Assembly Sequence Planning System. International journal of industrial engineering. 15(1). 92–103. 6 indexed citations
10.
Sınanoğlu, Cem, et al.. (2008). Effect of micro and macro pits of journal surface on radial pressure distribution of journal bearing. Indian Journal of Engineering and Materials Sciences. 15(4). 300–310. 3 indexed citations
11.
Sınanoğlu, Cem. (2006). The analysis of effects of shaft surface porosity on journal bearing using experimental and neural network approach. Industrial Lubrication and Tribology. 58(1). 15–31. 1 indexed citations
12.
Sınanoğlu, Cem, Fehmi Nair, & M.B. Karamış. (2005). Effects of shaft surface texture on journal bearing pressure distribution. Journal of Materials Processing Technology. 168(2). 344–353. 56 indexed citations
13.
Sınanoğlu, Cem. (2004). The analysis of the effects of surface roughness of shafts on journal bearings using recurrent hybrid neural network. Industrial Lubrication and Tribology. 56(6). 324–333. 6 indexed citations
14.
Sınanoğlu, Cem & Hüseyin Rıza Börklü. (2004). An approach to determine geometric feasibility to assembly states by intersection matrices in assembly sequence planning. Journal of Intelligent Manufacturing. 15(4). 543–559. 7 indexed citations
15.
Sınanoğlu, Cem, et al.. (2004). Analysis of pressure variations on journal bearing system using artificial neural network. Industrial Lubrication and Tribology. 56(2). 74–87. 22 indexed citations
16.
Yıldırım, Şahin, et al.. (2004). Design of an Artificial Neural Network for Analysis of Frictional Power Loss of Hydrostatic Slipper Bearings. Tribology Letters. 17(4). 887–899. 26 indexed citations
17.
Sınanoğlu, Cem, et al.. (2004). Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system. KSME International Journal. 18(3). 432–442. 25 indexed citations
18.
Sınanoğlu, Cem, et al.. (2004). Design of neural network model for analysing hydrostatic circular recessed bearings with axial piston pump slipper. Industrial Lubrication and Tribology. 56(5). 288–299. 8 indexed citations
19.
Sınanoğlu, Cem, et al.. (2004). Neural network analysis of leakage oil quantity in the design of partially hydrostatic slipper bearings. Industrial Lubrication and Tribology. 56(4). 231–243. 28 indexed citations
20.
Sınanoğlu, Cem & Hüseyin Rıza Börklü. (2002). Montaj sırası planlamada öncelik ilişkileri kullanımı. 15(1). 141–152.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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