LPBF Digital Twin Patent Snapshot 2026
The LPBF digital twin field is highly concentrated, with RTX Corp alone holding nearly half of all patent families in scope and the top five filers accounting for a majority of the hundred largest filers’ combined output. The field is in a Growth stage on a multi-year basis, with annual volume easing from its 2023 peak while remaining well above pre-2023 levels, and meaningful white space persists in process-control, AI-model, and materials characterization branches.
RTX Corp leads a nascent, heavily concentrated field
RTX Corp ranks first by a wide margin, holding 16 patent families — more than all other filers combined — making it the unambiguous technology leader in LPBF digital twin IP.
The top five filers collectively account for 53% of the ranked applicants visible in this query’ combined total, signaling extreme concentration for a field this size. Bull SA is the only clear second-tier player at 2 patent families; every other named filer holds exactly 1, leaving a pronounced tier gap between RTX Corp and the rest of the evidence snapshot.
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 1 | RTX Corp | 16 | |
| 2 | Bull SA | 2 | |
| 3 | Carnegie Mellon University | 1 | |
| 4 | HONEYWELL FEDERAL MANUFACTURING & TECHNOLOGIES LLC | 1 | |
| 5 | Wisconsin Alumni Research Foundation | 1 | |
| 6 | Siemens AG | 1 | |
| 7 | Northwestern Polytechnical University | 1 | |
| 8 | Beijing Power Machinery Institute | 1 | |
| 9 | The Research Foundation for the State University of New York | 1 | |
| 10 | Ansys Digital Engine (Hangzhou) Information Technology Services Co., Ltd. | 1 |
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 11 | BEIJING AERONAUTIC SCI & TECH RES INST OF COMAC | 1 | |
| 12 | AECC Optimal Materials (Zhenjiang) Additive Manufacturing Co., Ltd. | 1 | |
| 13 | Mangalam College of Engineering | 1 | |
| 14 | Guizhou Honglin Machinery | 1 | |
| 15 | Wuhan Institute of Technology | 1 | |
| 16 | HEBEI UNIV OF SCI & TECH | 1 | |
| 17 | Wuxi Shichao Intelligent Manufacturing Technology Co., Ltd. | 1 | |
| 18 | Hubei Sanjiang Aerospace Group Hongyang Electromechanical Co., Ltd. | 1 | |
| 19 | University of Virginia Patent Foundation | 1 | |
| 20 | Commercial Aircraft Corporation of China Ltd. | 1 |
RTX Corp’s visible position implies that any new entrant or challenger seeking freedom to operate or a licensing position will need to navigate a dense core portfolio held by a single aerospace incumbent, while the long tail of single-family filers suggests the field remains open to early movers in adjacent technical branches.
Filing counts for 2024 and 2025 are likely understated due to the standard 18–24 month publication lag; the apparent plateau in those years should not be read as a demand signal. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
A 2023 inflection point anchors multi-year growth; process and data-science branches dominate
Annual filings were minimal before 2023, then surged sharply, reflecting the field’s transition from exploratory research to active IP building. The technology mix is anchored in powder-metallurgy and additive-manufacturing process classes, with a secondary cluster in digital and AI branches.
Annual filing trend
Filings were negligible from 2017 through 2022, then jumped to 11 in 2023 before settling at 10 in 2024 and 7 in 2025 — the latter two years are further understated by publication lag, so the apparent easing from the 2023 peak should be treated cautiously rather than as a confirmed decline.
↗ Hover for values · click a bar to ask EurekaTechnology composition
B22F (powder metallurgy) and B33Y (additive manufacturing) are co-visible, reflecting the core LPBF process layer; G06F (digital data processing) forms the primary computational branch, while G05B (control systems) and G06N (AI models) are present but sparse — precisely the branches where digital-twin simulation and closed-loop control logic would be expected to appear.
↗ 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.
System and method for local PORE detection in lase…
In one aspect, the disclosure relates to detection of pore defects in laser powder bed fusion additive manufacturing. A convolutional neural network is trained based upon a training data set. The training data set can be generated using simulations or experiments performed on a metal sample by capturing side-view x-ray imagery as well as top-view thermal… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | Method for automatically preventing defects potent… | 25 |
| 2 | 一种基于选区激光熔化工艺的航空叶片拓扑优化设计方法 | 17 |
| 3 | 整流罩零件及激光选区熔化成形薄壁整流罩零件的方法 | 16 |
| 4 | Thermal stress and substrate damage reducing addit… | 8 |
| 5 | 基于有限元及轨迹追踪的增材制造场量宏观特性预测方法 | 7 |
| 6 | 选区激光熔化优化成型质量三元耦合调控方法 | 4 |
| 7 | 一种航空零部件的增材制造方法 | 3 |
| 8 | Predicting system in additive manufacturing proces… | 3 |
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 16 patent families and entered the LPBF digital twin space as a new entrant in the recent period, with a trend of new entrant momentum across 15 recent families. Its technology focus is concentrated in additive manufacturing process control (B33Y50, B33Y10) and powder metallurgy (B22F10), indicating a strategy of protecting the core LPBF process layer rather than the surrounding simulation or AI software stack.
patent families: 16Honeywell Federal Manufacturing & Technologies
Honeywell Federal Manufacturing & Technologies holds 1 patent family as a new entrant in the recent window, but its significance lies in being the sole co-filing hub, with joint filings alongside Carnegie Mellon University, Wisconsin Alumni Research Foundation, and the University of Virginia Patent Foundation. Its technical focus spans powder metallurgy (B22F10, B22F3) and plastics shaping (B29C64), suggesting a cross-material digital-twin approach developed with academic simulation expertise.
patent families: 1Frequently asked questions
The corpus in scope contains 34 patent families, reflecting a field that began meaningful IP activity only around 2020 and accelerated sharply in 2023.
RTX Corp leads with 16 patent families, more than all other filers combined. It entered as a new entrant in the recent filing window and concentrates its filings in additive manufacturing process control and powder metallurgy classes.
The field is in a Growth stage. Recent three-year filings sit well above the prior three-year window, confirming multi-year expansion. Annual volume has eased from its 2023 peak, but the most recent years are further understated by the standard publication lag, so the apparent slowdown should not be interpreted as a confirmed decline.
China, Europe (EPO), and the United States are near-equal in patent-record counts at roughly 10–11 each, suggesting visible assignees are pursuing broad tri-office protection. WIPO (PCT) and India account for smaller counts.
Honeywell Federal Manufacturing & Technologies is the only observed co-filing hub, having filed jointly with Carnegie Mellon University, Wisconsin Alumni Research Foundation, and the University of Virginia Patent Foundation — one co-filed family with each partner. No other multi-party collaborations are evident in the evidence.
G05B (control and regulating systems) and G06N (AI and machine-learning models) each carry only 4 patent records, well below the visible process-layer classes. C22C (alloys) and G01N (material analysis and testing) each hold only 1 patent record. These branches — particularly closed-loop control and AI-based surrogate modeling — represent areas where the IP landscape is sparse relative to their technical relevance to a complete LPBF digital twin workflow.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
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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