Titanium Alloy AI/ML Patent Snapshot 2026
The application of AI and machine learning to titanium alloy research is an emerging, lightly-patented field with 16 patent families in scope, where RTX Corp leads a fragmented field that is otherwise dominated by Chinese academic institutions. Activity has accelerated sharply from 2022 onward, making this a nascent space with significant open territory for industrial players.
RTX Corp leads a fragmented, academically dominated field
RTX Corp holds the top position with 3 patent families, ahead of Sardar Beant Singh State University Gurdaspur with 2 patent families; all remaining ranked applicants hold 1 patent family each. The top five filers together account for 47% of the ranked applicants visible in this query’ combined total, indicating moderate concentration at the apex but a long, flat tail of single-family holders.
No second industrial player yet matches RTX Corp’s position. The remaining 13 ranked applicants are entirely composed of universities and one precision forging company, signaling that industrial IP development outside RTX Corp is essentially absent at this stage.
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 1 | RTX Corp | 3 | |
| 2 | Sardar Beant Singh State University Gurdaspur | 2 | |
| 3 | UNIV OF SCI & TECH BEIJING | 1 | |
| 4 | Xi’an Jiaotong University | 1 | |
| 5 | Baoji Baoti Precision Forging Co., Ltd. | 1 | |
| 6 | Jilin University | 1 | |
| 7 | NANJING UNIV OF AERONAUTICS & ASTRONAUTICS | 1 |
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 8 | TIANJIN UNIV OF TECH & EDUCATION (TEACHER DEV CENT… | 1 | |
| 9 | Xi’an University of Architecture and Technology | 1 | |
| 10 | Beijing Institute of Technology | 1 | |
| 11 | Central South University | 1 | |
| 12 | Zhongbei University | 1 | |
| 13 | Xingtai University | 1 | |
| 14 | Shenyang University of Technology | 1 |
RTX Corp’s lead, though modest in absolute terms, represents the only coherent multi-family industrial program in the field. For competitors, this means the defensive evidence snapshot is thin and freedom-to-operate is relatively open across most technical approaches.
The most recent filings (2025–2026) are subject to publication lag and likely undercount actual activity; the apparent acceleration should be read as a floor, not a ceiling. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Activity surged from 2022; AI computing methods dominate the technology mix
The filing trend and technology composition together reveal a field that ignited in 2022 and is still in its formative phase, with computational AI methods far outweighing downstream materials or manufacturing classes.
Annual filing trend
Zero filings were recorded from 2017 through 2021; activity began in 2022 and has grown each subsequent year through 2025. The 2025 and 2026 bars are subject to publication lag and should be treated as minimums. The overall trajectory is one of rapid ignition from a zero base rather than a maturing plateau.
↗ Hover for values · click a bar to ask EurekaTechnology composition
G06N (Computing based on AI models) and G16C (Computational chemistry) are visible in the branch mix, reflecting a field focused on property prediction and materials informatics rather than process engineering. Alloy composition (C22C) and non-ferrous metal treatment (C22F) appear at much lower counts, confirming that the AI layer is largely disconnected from hands-on materials IP so far. Additive manufacturing (B33Y) and powder metallurgy (B22F) together represent a small but technically coherent cluster.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
Generation of microstructural images of titanium a…
The present disclosure provides for the generation of microstructural images of components (e.g., titanium alloys) using machine learning frameworks. More particularly, the present disclosure provides for the generation of microstructural images of components (e.g., titanium alloys) as a function of heat treatment conditions using conditional generative… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | 一种基于机器学习的钛合金力学性能预测方法及装置 | 14 |
| 2 | 基于深度学习的增材制造镍钛合金件力学性能预测的方法 | 2 |
| 3 | TC11钛合金厚壁管径锻工艺参数寻优方法 | 1 |
| 4 | 一种基于机器学习设计高温高强韧钛合金的方法及制备方法 | 1 |
| 5 | 一种基于热膨胀曲线进行机器学习的钛合金屈服强度预测方法 | 1 |
| 6 | 基于物理信息驱动的近β钛合金多性能预测方法 | 1 |
| 7 | Generation of microstructural images of titanium a… | 1 |
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
Assignee snapshot from the current evidence set
The applicants below are visible in this query result. Because the evidence set is relatively small, read this section as a directional snapshot rather than a full competitive ranking.
RTX Corp
RTX Corp holds 3 patent families and is the only industrial organization with a multi-family program in this field. Its technical emphasis spans G06N (AI computing models), G06F (digital data processing), and G06T (image data processing and generation), pointing to a focus on AI-driven microstructure imaging and classification rather than alloy composition design. Momentum is classified as a new entrant, consistent with the field’s post-2022 ignition; its 3 recent families represent its entire portfolio here.
families: 3Sardar Beant Singh State University Gurdaspur
Sardar Beant Singh State University Gurdaspur holds 2 patent families and is the only academic institution with more than one family in the ranking. Its technical focus is concentrated in A61L (sterilizing and disinfecting, reflecting biomedical implant applications) and C22C (alloys), making it the sole filer with meaningful IP bridging AI methods and physical alloy composition in a biomedical context. Like RTX Corp, it is classified as a new entrant, with both families filed within the recent window.
families: 2Frequently asked questions
The current evidence covers 16 patent families in scope. This is a small corpus consistent with an emerging, nascent field that only began generating filings from 2022 onward.
RTX Corp is the top-ranked applicant with 3 patent families, making it the only industrial organization with a multi-family program. All other applicants in the ranking are universities or research institutions holding 1–2 families each.
China is the lead filing jurisdiction. India, the United States, and Europe (EPO) are also represented, but at significantly lower levels. The Chinese activity is driven predominantly by academic institutions.
The evidence does not support a firm life-cycle classification at this stage; the corpus is too small and too recently initiated. Filing activity was zero from 2017 through 2021 and only began accelerating from 2022, placing the field at the very earliest stage of IP development.
G06N (Computing based on AI models) and G16C (Computational chemistry) are the most represented branches, reflecting a focus on property prediction and materials informatics. Downstream materials and process branches such as C22C (alloys), C22F (non-ferrous metal treatment), and B33Y (additive manufacturing) are sparsely covered.
The most technically plausible under-served areas identified in the evidence are additive manufacturing process optimization (B33Y, 2 records) and AI-guided heat treatment and phase transformation prediction (C22F, 3 records). Both are sparse relative to the visible AI computing classes yet address well-established industrial needs in titanium alloy manufacturing.
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Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
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