І. V. Konovalenko

873 total citations
50 papers, 639 citations indexed

About

І. V. Konovalenko is a scholar working on Materials Chemistry, Mechanics of Materials and Mechanical Engineering. According to data from OpenAlex, І. V. Konovalenko has authored 50 papers receiving a total of 639 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Materials Chemistry, 34 papers in Mechanics of Materials and 34 papers in Mechanical Engineering. Recurrent topics in І. V. Konovalenko's work include Material Properties and Failure Mechanisms (41 papers), Engineering Diagnostics and Reliability (30 papers) and Industrial Engineering and Technologies (11 papers). І. V. Konovalenko is often cited by papers focused on Material Properties and Failure Mechanisms (41 papers), Engineering Diagnostics and Reliability (30 papers) and Industrial Engineering and Technologies (11 papers). І. V. Konovalenko collaborates with scholars based in Ukraine, Slovakia and Russia. І. V. Konovalenko's co-authors include Pavlo Maruschak, Janette Brezinová, Olegas Prentkovskis, Jakub Brezina, Р. Т. Бищак, Ján Viňáš, С. В. Панин, Mykola Chausov, Anna Guzanová and Tomaž Vuherer and has published in prestigious journals such as Materials, The International Journal of Advanced Manufacturing Technology and Applied Sciences.

In The Last Decade

І. V. Konovalenko

48 papers receiving 630 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
І. V. Konovalenko Ukraine 16 326 278 222 181 96 50 639
Shun Yao China 15 601 1.8× 114 0.4× 144 0.6× 70 0.4× 62 0.6× 61 790
Marcus Klein Germany 12 469 1.4× 126 0.5× 332 1.5× 65 0.4× 45 0.5× 61 751
Guan-Qiang Wang China 13 380 1.2× 201 0.7× 288 1.3× 100 0.6× 58 0.6× 33 597
Craig Przybyla United States 18 725 2.2× 410 1.5× 724 3.3× 35 0.2× 60 0.6× 53 1.2k
Anton Shterenlikht United Kingdom 17 568 1.7× 187 0.7× 515 2.3× 21 0.1× 127 1.3× 55 885
Pedro Prates Portugal 18 696 2.1× 181 0.7× 660 3.0× 34 0.2× 31 0.3× 68 860
Weijun Liu China 15 982 3.0× 174 0.6× 120 0.5× 88 0.5× 43 0.4× 93 1.2k
Zhengmao Yang China 16 232 0.7× 130 0.5× 239 1.1× 22 0.1× 21 0.2× 49 522
Hongkun Wu China 14 629 1.9× 61 0.2× 141 0.6× 124 0.7× 49 0.5× 21 796
Steven Cooreman Belgium 13 600 1.8× 181 0.7× 467 2.1× 17 0.1× 322 3.4× 51 941

Countries citing papers authored by І. V. Konovalenko

Since Specialization
Citations

This map shows the geographic impact of І. V. Konovalenko'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 І. V. Konovalenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites І. V. Konovalenko more than expected).

Fields of papers citing papers by І. V. Konovalenko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by І. V. Konovalenko. 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 І. V. Konovalenko. The network helps show where І. V. Konovalenko may publish in the future.

Co-authorship network of co-authors of І. V. Konovalenko

This figure shows the co-authorship network connecting the top 25 collaborators of І. V. Konovalenko. A scholar is included among the top collaborators of І. V. Konovalenko 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 І. V. Konovalenko. І. V. Konovalenko 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.
Maruschak, Pavlo, et al.. (2024). Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network. Applied Sciences. 14(6). 2591–2591. 15 indexed citations
2.
Konovalenko, І. V., Pavlo Maruschak, Janette Brezinová, Olegas Prentkovskis, & Jakub Brezina. (2022). Research of U-Net-Based CNN Architectures for Metal Surface Defect Detection. Machines. 10(5). 327–327. 47 indexed citations
3.
Konovalenko, І. V., et al.. (2021). Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network. Metals. 11(4). 549–549. 26 indexed citations
4.
Konovalenko, І. V., et al.. (2021). Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis. Metals. 11(11). 1851–1851. 20 indexed citations
5.
Konovalenko, І. V., Pavlo Maruschak, Janette Brezinová, Ján Viňáš, & Jakub Brezina. (2020). Steel Surface Defect Classification Using Deep Residual Neural Network. Metals. 10(6). 846–846. 80 indexed citations
6.
Konovalenko, І. V., et al.. (2018). Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks. Materials. 11(12). 2467–2467. 27 indexed citations
7.
Konovalenko, І. V., Pavlo Maruschak, & Olegas Prentkovskis. (2018). Automated Method for Fractographic Analysis of Shape and Size of Dimples on Fracture Surface of High-Strength Titanium Alloys. Metals. 8(3). 161–161. 27 indexed citations
8.
Maruschak, Pavlo, І. V. Konovalenko, Mykola Chausov, et al.. (2018). Impact of Dynamic Non-Equilibrium Processes on Fracture Mechanisms of High-Strength Titanium Alloy VT23. Metals. 8(12). 983–983. 9 indexed citations
9.
Konovalenko, І. V., Pavlo Maruschak, Mykola Chausov, & Olegas Prentkovskis. (2017). Fuzzy Logic Analysis of Parameters of Dimples of Ductile Tearing on the Digital Image of Fracture Surface. Procedia Engineering. 187. 229–234. 16 indexed citations
10.
Maruschak, Pavlo, et al.. (2016). Analysis and automated fatigue damage evaluation of a 17Mn1Si pipeline steel. Procedia Structural Integrity. 2. 1928–1935. 11 indexed citations
11.
Maruschak, Pavlo, et al.. (2015). Defectometry Analysis of Surface Condition Damaged with Corrosion Pitting. Materials science forum. 818. 153–157. 2 indexed citations
12.
Maruschak, Pavlo, et al.. (2014). Effect of Long Term Operation on Degradation of Material of Main Gas Pipelines. Materials science forum. 782. 279–283. 17 indexed citations
13.
Konovalenko, І. V., et al.. (2014). Fractographic and Defect Measurement Analysis of Multiple Pitting Corrosion Parameters. Chemical and Petroleum Engineering. 50(7-8). 457–463. 3 indexed citations
14.
Панин, С. В., et al.. (2014). Application of meso- and fracture mechanics to material affected by a network of thermal fatigue cracks. International Journal of Fatigue. 76. 33–38. 4 indexed citations
15.
Konovalenko, І. V. & Pavlo Maruschak. (2012). Computer analysis of digital images with quasiperiodical structure. International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science. 419–419. 2 indexed citations
16.
Maruschak, Pavlo, et al.. (2012). Physical regularities in the cracking of nanocoatings and a method for an automated determination of the crack-network parameters. Materiali in tehnologije. 46(5). 525–529. 6 indexed citations
17.
Maruschak, Pavlo, et al.. (2012). Automated diagnostics of damage of aluminum alloy under conditions of high-cycle fatigue. ELARTU (Ternopil National Technical University). 6 indexed citations
18.
Maruschak, Pavlo, І. V. Konovalenko, & Р. Т. Бищак. (2012). Effect of thermal fatigue cracks on brittle-ductile deformation and failure of cbcm roller surface layers. Metallurgist. 56(1-2). 30–36. 13 indexed citations
19.
Maruschak, Pavlo, et al.. (2010). Local strains and mesoscopic mechanisms of crack growth in 25Kh1M1F steel. 6(4). 169–175. 1 indexed citations
20.
Maruschak, Pavlo, et al.. (2008). Computer analysis of surface cracks in structural elements. Materials Science. 44(6). 833–839. 17 indexed citations

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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