Jay Airao

846 total citations
29 papers, 622 citations indexed

About

Jay Airao is a scholar working on Mechanical Engineering, Electrical and Electronic Engineering and Biomedical Engineering. According to data from OpenAlex, Jay Airao has authored 29 papers receiving a total of 622 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Mechanical Engineering, 22 papers in Electrical and Electronic Engineering and 10 papers in Biomedical Engineering. Recurrent topics in Jay Airao's work include Advanced machining processes and optimization (27 papers), Advanced Machining and Optimization Techniques (22 papers) and Advanced Surface Polishing Techniques (10 papers). Jay Airao is often cited by papers focused on Advanced machining processes and optimization (27 papers), Advanced Machining and Optimization Techniques (22 papers) and Advanced Surface Polishing Techniques (10 papers). Jay Airao collaborates with scholars based in India, Denmark and Poland. Jay Airao's co-authors include Chandrakant K. Nirala, Navneet Khanna, Grzegorz Królczyk, Rachele Bertolini, Hussien Hegab, Anish Roy, Luís Norberto López de Lacalle, Bhavesh Chaudhary, Vivek Bajpai and Ramin Aghababaei and has published in prestigious journals such as Wear, Materials and Materials Letters.

In The Last Decade

Jay Airao

27 papers receiving 604 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jay Airao India 14 581 371 246 88 66 29 622
Asit Kumar Parida India 15 594 1.0× 365 1.0× 267 1.1× 88 1.0× 89 1.3× 27 641
Ízaro Ayesta Spain 16 533 0.9× 420 1.1× 280 1.1× 74 0.8× 79 1.2× 38 608
G. Le Coz France 6 570 1.0× 373 1.0× 285 1.2× 75 0.9× 57 0.9× 8 598
N. Suresh Kumar Reddy India 12 657 1.1× 417 1.1× 267 1.1× 84 1.0× 90 1.4× 20 716
Chandrakant K. Nirala India 18 837 1.4× 579 1.6× 436 1.8× 97 1.1× 65 1.0× 62 877
Rüstem Binali Türkiye 10 390 0.7× 257 0.7× 130 0.5× 98 1.1× 51 0.8× 39 428
Witold Habrat Poland 10 466 0.8× 210 0.6× 202 0.8× 96 1.1× 84 1.3× 44 489
W. Li United States 7 469 0.8× 327 0.9× 279 1.1× 52 0.6× 50 0.8× 14 499
И. Н. Ердаков Russia 11 416 0.7× 205 0.6× 92 0.4× 78 0.9× 76 1.2× 28 478
B. Izquierdo Spain 16 923 1.6× 781 2.1× 675 2.7× 80 0.9× 75 1.1× 42 993

Countries citing papers authored by Jay Airao

Since Specialization
Citations

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

Fields of papers citing papers by Jay Airao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay Airao. 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 Jay Airao. The network helps show where Jay Airao may publish in the future.

Co-authorship network of co-authors of Jay Airao

This figure shows the co-authorship network connecting the top 25 collaborators of Jay Airao. A scholar is included among the top collaborators of Jay Airao 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 Jay Airao. Jay Airao 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.
Truong, Tam T., et al.. (2025). Image-based machine learning model for tool wear estimation in milling Inconel 718. Wear. 571. 205865–205865. 4 indexed citations
2.
Malekan, Mohammad, et al.. (2025). Numerical assessment of tool geometry for improving productivity in milling stainless steel 316 L. The International Journal of Advanced Manufacturing Technology. 136(7-8). 3451–3463.
3.
Airao, Jay, et al.. (2024). Tribological performance in micro-milling of Ti6Al4V under nanofluid-based minimum quantity lubrication. International Journal on Interactive Design and Manufacturing (IJIDeM). 19(3). 1807–1819. 7 indexed citations
4.
Airao, Jay, Mohammad Malekan, Michal K. Budzik, & Ramin Aghababaei. (2024). Effect of Friction on Critical Cutting Depth for Ductile–Brittle Transition in Material Removal Mechanism. Journal of Tribology. 146(11).
5.
Khanna, Navneet, et al.. (2024). Comparison of sustainable cooling/lubrication strategies for drilling of wire arc additively manufactured Inconel 625. Tribology International. 200. 110068–110068. 11 indexed citations
6.
Airao, Jay, et al.. (2024). Bayesian neural networks modeling for tool wear prediction in milling Al 6061 T6 under MQL conditions. The International Journal of Advanced Manufacturing Technology. 135(5-6). 2777–2788. 4 indexed citations
7.
Khanna, Navneet, et al.. (2023). Sustainability analysis of new hybrid cooling/lubrication strategies during machining Ti6Al4V and Inconel 718 alloys. Sustainable materials and technologies. 36. e00606–e00606. 24 indexed citations
8.
Airao, Jay, et al.. (2023). Comparative analysis of tool wear in micro-milling of wrought and selective laser melted Ti6Al4V. Wear. 523. 204788–204788. 32 indexed citations
9.
Khanna, Navneet, et al.. (2023). Life cycle assessment to reduce environmental and carbon footprints of ultrasonic-assisted turning. Sustainable materials and technologies. 37. e00674–e00674. 10 indexed citations
10.
Airao, Jay & Chandrakant K. Nirala. (2022). Machinability analysis of Titanium 64 using ultrasonic vibration and vegetable oil. Materials and Manufacturing Processes. 37(16). 1893–1901. 20 indexed citations
11.
Airao, Jay, Chandrakant K. Nirala, & Navneet Khanna. (2022). Novel use of ultrasonic-assisted turning in conjunction with cryogenic and lubrication techniques to analyze the machinability of Inconel 718. Journal of Manufacturing Processes. 81. 962–975. 42 indexed citations
12.
Airao, Jay, et al.. (2022). Measurement and analysis of tool wear and surface characteristics in micro turning of SLM Ti6Al4V and wrought Ti6Al4V. Measurement. 206. 112281–112281. 29 indexed citations
13.
Airao, Jay & Chandrakant K. Nirala. (2022). Effect of microstructure on tool wear in micro-turning of wrought and selective laser melted Ti6Al4V. Materials Letters. 327. 133078–133078. 11 indexed citations
14.
Airao, Jay & Chandrakant K. Nirala. (2022). Finite Element Modeling and Experimental Validation of Tool Wear in Hot-Ultrasonic-Assisted Turning of Nimonic 90. Journal of Vibration Engineering & Technologies. 11(8). 3687–3705. 11 indexed citations
15.
Khanna, Navneet, Jay Airao, Chandrakant K. Nirala, & Grzegorz Królczyk. (2022). Novel sustainable cryo-lubrication strategies for reducing tool wear during ultrasonic-assisted turning of Inconel 718. Tribology International. 174. 107728–107728. 57 indexed citations
16.
Airao, Jay, Chandrakant K. Nirala, J.C. Outeiro, & Navneet Khanna. (2022). Surface integrity in ultrasonic-assisted turning of Ti6Al4V using sustainable cutting fluid. Procedia CIRP. 108. 55–60. 13 indexed citations
17.
Airao, Jay, Chandrakant K. Nirala, Rachele Bertolini, Grzegorz Królczyk, & Navneet Khanna. (2022). Sustainable cooling strategies to reduce tool wear, power consumption and surface roughness during ultrasonic assisted turning of Ti-6Al-4V. Tribology International. 169. 107494–107494. 116 indexed citations
18.
Airao, Jay, et al.. (2022). Performance Measurement and Discharge Data Based Analysis of Ultrasonic Assisted μEDM for Ti6Al4V. Journal of Micro and Nano-Manufacturing. 10(3). 3 indexed citations
19.
Airao, Jay & Chandrakant K. Nirala. (2021). Analytical Modeling of Machining Forces and Friction Characteristics in Ultrasonic-Assisted Turning Process. Journal of Manufacturing Science and Engineering. 144(2). 11 indexed citations
20.
Khanna, Navneet, Jay Airao, Munish Kumar Gupta, et al.. (2019). Optimization of Power Consumption Associated with Surface Roughness in Ultrasonic Assisted Turning of Nimonic-90 Using Hybrid Particle Swarm-Simplex Method. Materials. 12(20). 3418–3418. 34 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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