Sajith Soman: h-index, Total Citations, and Citation Map
Sajith Soman's h-index is 10 (10 i10-index, 336+ total citations across 21+ publications) according to Google Scholar as of May 2026. Sajith Soman is affiliated with Assistant Research Professor, National Center for Additive Manufacturing Excellence (NCAME), Auburn.
Sajith Soman is a researcher affiliated with Assistant Research Professor, National Center for Additive Manufacturing Excellence (NCAME), Auburn, specializing in Fatigue and Fracture, Additive Manufacturing, Numerical Simulations. Their work has been cited 336 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Sajith Soman's Citation Metrics
Bibliometric impact based on 21 indexed publications. Of these, 16 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 10
- i10-Index
- 10
- Total Citations
- 336
- Citing Countries
- 34
As of May 2026.
Sajith Soman has an h-index of 10 and 336 total citations across 21 publications, with research cited by institutions in 34 countries.
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Top Cited Works
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Experimental and numerical investigation of mixed mode fatigue crack growth models in aluminum 6061-T6
202098
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
47 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for mixed-mode fatigue crack growth in aluminum, subsequently extending these predictive models to additive manufacturing and comprehensive life prediction reviews.
The researcher identified critical defect features governing fatigue failure in additively manufactured Ti-6Al-4V, establishing a foundational framework for understanding microstructural impacts on component durability.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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