Electron Beam Melting Design for AM Patents: Trends & Gaps 2026
- Flat since 2018: filings peaked at 16 that year and sat at 11 by the 2022 midpoint, with no evidence of a renewed filing wave.
- Turbines dominate the applied claims: F01D and F02C each carry 25 records, showing the design method's primary commercial pull is aerospace and power-generation hardware.
- Combustion-chamber claims stay thin: F23R holds just 22 records against a 45-record process core, marking one of the clearer under-claimed branches in the dataset.
How electron beam melting design claims are structured
Electron beam melting design for additive manufacturing sits at the intersection of a physical process — electron beam powder bed fusion — and a design discipline: geometries that reduce or remove the need for sacrificial supports during a build. Across the 120 families in this dataset, the technical core is consistently powder metallurgy and additive manufacturing process claims (B22F and B33Y), with a smaller but distinct set of filings applying that design logic to turbine components, combustion chambers, and even sports equipment.
Filing activity peaked in 2018 and has trended flat to lower since, which reads less as an abandoned field and more as one where the core support-free and lattice design methods reached a stable claim set early. The applied-industry filings — turbines above all — are where recent competitive attention has concentrated, while the computational-design layer tied to G06N shows where the field may move next.
Filing trends and technology composition
The 120 families in this dataset span a decade of filing activity and cluster tightly around powder-bed metal processing and turbine hardware, with a smaller computational-design layer alongside them.
Filing trend, 2017-2026
Filings peaked in 2018 at 16 records and have not returned to that level since; 2022 sits at the dataset midpoint with 11 records, and the most recent year is necessarily undercounted because publication lags filing by roughly 18 months.
Technology composition by IPC subclass
Powder metallurgy (B22F) and additive manufacturing (B33Y) each account for 45 of the 120 records and form the technical core; plastics shaping (B29C, 40) runs close behind, while turbine-specific subclasses F01D and F02C (25 each) show where the design method gets applied to hardware, and G06N (24) marks the computational-design layer.
Shares are the percentage of the 120 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Electron Beam Melting Design for AM with Eureka
This page is one run against one query. Ask Eureka your own question about electron beam melting design for am and every answer comes back with the patent numbers behind it.
Try EurekaA representative claim in this space
Computer-implemented method of reducing support structures in topology optimized design for additive manufacturing
The method imports an optimised topology, identifies boundaries and overhang features enclosing 45 degrees or less to the build plane, adds support-free trusses to those overhangs, applies a density filter to the trusses, and verifies the resulting structure against a volume-fraction criterion.Filed by Siemens Energy Global GmbH & Co. KG, published 2022-06-23.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20180341248A1 | Real-time adaptive control of additive manufacturing processes using machine learning | 334 |
| 2 | US20200166909A1 | Real-time adaptive control of manufacturing processes using machine learning | 235 |
| 3 | US20170372480A1 | Systems, Media, and Methods for Pre-Processing and Post-Processing in Additive Manufacturing | 190 |
| 4 | US20200257933A1 | Machine Learning to Accelerate Alloy Design | 186 |
| 5 | WO2018217903A1 | Real-time adaptive control of additive manufacturing processes using machine learning | 143 |
| 6 | US10278823B1 | Lightweight femoral stem for hip implants | 46 |
| 7 | US20150345298A1 | Gas turbine engine component having vascular engineered lattice structure | 45 |
| 8 | US20200096970A1 | Real-time adaptive control of additive manufacturing processes using machine learning | 36 |
| 9 | WO2014105113A1 | Gas turbine engine component having vascular engineered lattice structure | 32 |
| 10 | US10234848B2 | Real-time adaptive control of additive manufacturing processes using machine learning | 30 |
Citation counts favour earlier-filed process-control patents; treat them as a measure of influence within this corpus, not as a ranking of current design relevance.
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Browse MCP servers →What the filing data actually indicates
Three figures in this dataset matter more than the raw family count: where the filing trend has gone since its 2018 peak, which industries pull the design method into applied claims, and how thin the citation-heavy records are relative to the whole set.
Filing activity has not recovered its 2018 peak
Sixteen records filed in 2018 remains the high point; by the 2022 midpoint the annual count had already fallen to 11. The zero shown for the latest year is a publication-lag artefact, not a stop, but the multi-year decline before it is real.
Turbine hardware is the dominant applied claim area
Gas-turbine and non-positive-engine subclasses each hold 25 records, roughly matched, indicating the support-free and lattice design methods are consistently being claimed against turbine blades, vanes and related components rather than staying purely process-level.
Citation weight sits on older, process-level filings
The most-cited records in this set are machine-learning process-control patents rather than design-specific ones, meaning citation counts here favour general manufacturing-control claims filed earlier, not the newer design-for-AM method claims.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to electron beam melting design for am, with the prior art for and against each one.
Who holds the claims, and where the gaps sit
No single assignee dominates this 120-family dataset; instead, a set of industrial and aerospace-linked filers hold overlapping claims across the powder-bed and turbine-application clusters, with one identified academic co-filing relationship.
Filing spread across industrial and academic filers
The assignee set includes turbine and engine manufacturers, an industrial gases and metals group, and at least one university, rather than a single company holding a majority of the claim space.
One identified industry-academic pairing
The strongest co-assignee relationship in the dataset links Siemens Energy Global with University of Waterloo, appearing together on two records — the only co-filing pair strong enough to stand out from the rest.
Recent-year filings read as flat across the board
Every named assignee in the momentum data shows zero filings in the latest tracked year, including firms with prior activity such as General Electric and United Technologies. Given the roughly 18-month publication lag, this most likely understates real activity rather than indicating a full stop.
| Assignee | Recent year | YoY |
|---|---|---|
| Relativity Space | 0 | — |
| Siemens | 0 | — |
| Cobra Golf, Inc. | 0 | -100% |
| Raytheon Technologies | 0 | -100% |
| United Technologies Corporation | 0 | — |
| General Electric Company | 0 | — |
| Siemens Energy Global GmbH & Co. KG | 0 | — |
| Ricoh Company, Ltd. | 0 | — |
Where to take this analysis next
The filing and IPC data point to specific follow-up questions rather than a single conclusion — each one is answerable with a deeper pull against this same dataset.
Check freedom-to-operate against the core support-free method
The representative WO2022128361A1 claim sequence is narrow but specific; any workflow automating overhang detection and truss insertion should be checked against it and its family before deployment.
Run a claim chart in EurekaTrack the turbine-application cluster for renewed activity
F01D and F02C together account for 50 of the 120 records; watching for any uptick here would be the clearest early signal of renewed commercial interest in the design method.
Set up a filing alert in EurekaEvaluate the combustion-chamber and sports-equipment gaps
Both F23R and A63B show meaningfully fewer records than the process core, with no dominant assignee — worth a deeper prior-art search before drafting a first claim.
Explore white space in EurekaCommon questions about electron beam melting design patents
It refers to methods for designing parts specifically for the electron beam powder bed fusion process, including lattice structures and support-free geometries that reduce or eliminate the need for sacrificial support material during a build. Because electron beam melting operates at higher temperatures and in a vacuum compared with laser powder bed fusion, the design rules for overhangs, thermal gradients and support removal differ from other metal 3D printing methods. This patent dataset covers 120 families combining electron beam or powder-bed process claims with lattice, support-free or design-for-additive-manufacturing claims.
Filing activity in this dataset concentrates around a group of industrial and aerospace-adjacent assignees rather than a single dominant player, with named entities including Siemens Energy Global, General Electric, United Technologies, and Caterpillar appearing in the recent-year momentum data. Most of these show no filings in the latest tracked year, which given the roughly 18-month publication lag likely reflects reporting delay rather than an actual halt in R&D. The strongest identified co-filing relationship links Siemens Energy Global with University of Waterloo, suggesting at least one active industry-academic research pairing.
The trend is flat to declining rather than growing. Filings peaked in 2018 at 16 records, dropped to 11 by the 2022 midpoint, and show zero in the most recent tracked year — though that final figure is understated because of publication lag. Read together, the pattern suggests the core support-free and lattice-design methods reached a filing plateau some years ago rather than being in an active land-grab phase.
Turbine and gas-turbine hardware is the clearest applied industry, shown by 25 records each in F01D and F02C alongside 22 in combustion-chamber-specific F23R — together indicating aerospace and power-generation components as the primary application. Powder metallurgy and general additive manufacturing subclasses (B22F, B33Y, each at 45 records) form the underlying process core that those industry-specific filings build on. A smaller but notable cluster in A63B (17 records) shows the same design methods reaching sports equipment, and G06N (24 records) marks where AI-based computational design intersects with the physical process claims.
The combustion-chamber-specific branch (F23R, 22 records) and the sports-equipment branch (A63B, 17 records) are both thin relative to the 45-record core in B22F and B33Y, and neither shows a dominant assignee locking up the space. Given the flat overall filing trend and zero recent-year activity across the named assignees, these adjacent branches look under-claimed rather than contested. A first filing that ties the established support-free truss method to a specific component class in one of these areas would be entering relatively open territory, though a freedom-to-operate check against the core method claims is still necessary.
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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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Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.