Aluminum SLM Powder Feedstock Patents: Leaders & Filing Trends 2026
A data-backed look at aluminum alloy selective laser melting powder feedstock patents: who is filing, how concentrated the field is, where the classification white space sits, and what the most-cited records claim, throu
Filing growth = 2021 (6 records) → 2024 (9); 2024 is the last year we treat as complete. Top-5 share = the 5 largest assignees ÷ all 52 records in scope (CR5), not the ranked leaders only.
What this landscape covers
This landscape covers 52 published records filed between 2015 and the 2026 data cut-off that combine aluminium alloy chemistry with selective laser melting powder feedstock, drawn from IPC classes covering additive manufacturing, powder metallurgy and alloys. Filing is concentrated but not consolidated: the top 5 assignees hold 38.5% of records and the top 10 hold 59.6%, leaving a long tail of single-filing universities and materials companies, overwhelmingly filed through the Chinese patent office.
The technology composition skews heavily toward core powder metallurgy and additive manufacturing classification, with alloy composition claims (C22C) present in 84.6% of records. Adjacent branches — joining processes, ceramic composites, casting and radiation-shielding applications — each appear in only one or two records, marking them as the thinnest layers of claimed prior art in this dataset.
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Filing trends and technology composition
The dataset covers 52 published records dated 2015-01-01 through 2026-08-31, with China accounting for the large majority of receiving offices. Because publication lags filing by roughly 18 months, the most recent years in the trend below are still filling in.
Filing activity is recent and still rising
No records appear before 2017. Filing climbed to a peak of 13 records in 2023, and the 2021-to-2024 window shows a +50% increase (6 records to 9), with 2024 the last year that can be treated as complete.
Publication lags filing by roughly 18 months, so 2025 onwards are still filling in. Growth rates on this page therefore end at 2024; running them to the last bar would understate the field.
Powder metallurgy and additive manufacturing dominate the classification mix
B22F (powder metallurgy) appears in all 52 records and B33Y (additive manufacturing) in 96.2% of them, confirming the dataset's core scope. C22C (alloys) reaches 84.6% of records, while C22F (non-ferrous metal treatment) sits at 25.0%. A handful of adjacent classes — casting, welding, ceramics and radiation shielding — appear in only one or two records each.
Shares are the percentage of the 52 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Aluminum Alloy Selective Laser Melting Powder Feedstock Patent Landscape with Eureka
This page is one run against one query. Ask Eureka your own question about aluminum alloy selective laser melting powder feedstock patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaRepresentative filing and the most-cited records
CN117920990A — 7075 aluminium alloy composite powder and selective laser melting forming method
The filing describes a 7075 aluminium alloy composite powder made from 1.0-2.0 wt.% Ti powder and 1.0-2.0 wt.% Ta powder with the balance as 7075 aluminium alloy powder, together with the selective laser melting method that processes it. The solid spherical Ti and Ta particles act as nucleating agents, refining grain structure and suppressing hot cracking while improving the alloy's corrosion resistance.Filed 2024-04-26 by Fujian University of Technology.


| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | CN111496244A | 一种增材制造高强铝合金粉及其制备方法和应用 | 50 |
| 2 | CN110756806A | 一种基于激光选区熔化技术的Ti/Al异种合金的成形方法 | 34 |
| 3 | CN105112708A | 一种激光重熔扫描碳化物弥散增强铝合金的快速制造方法 | 32 |
| 4 | CN110257657A | 基于激光选区熔化技术制备石墨烯增强铝合金材料的方法 | 29 |
| 5 | CN108486429A | 稀土铒元素增强SLM专用AlSi7Mg铝合金粉末及其应用 | 21 |
| 6 | CN110184512A | 一种激光选区熔化用铝合金粉及其制备共晶强化铝合金的方法 | 20 |
| 7 | CN111155007A | 一种基于选择性激光熔化成形技术的高强度2000系铝合金制备方法 | 19 |
| 8 | US20220033946A1 | Composition design optimization method of aluminum alloy for selective laser melting | 12 |
| 9 | CN116445776A | 一种适用于选区激光熔化技术的高强铝合金粉末及工艺方法 | 11 |
| 10 | CN109290583A | 一种消除7075铝合金选择性激光熔化成型裂纹的方法 | 11 |
Citation counts reflect the searched corpus and favour older filings; treat them as a measure of influence on later claim drafting rather than of current commercial importance.
Patent titles are shown in the language they were filed in, not translated, so that each record stays verifiable against the original filing — a translated title will not match in Eureka or in any national register. Each row carries its publication number; clicking a row searches Eureka by that number.
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Four findings stand out once the filing, classification and citation data are put side by side. Together they mark where claim space is dense, where it is thin, and where influence in the corpus concentrates.
Filing sits with a small group, not one company
The leading assignee holds 6 records, and the top 5 combined account for 20 records — 38.5% of all 52 records in scope. The top 10 extend that to 59.6%. No single filer dominates outright, which leaves room for new entrants to build a credible position.
Recent growth is real, not a data artefact
Filings rose from 6 in 2021 to 9 in 2024, a +50% increase, with 2023 the peak year so far at 13 records. Because publication lags filing by roughly 18 months, 2025-2026 figures are still filling in and should not be read as a decline.
Alloy chemistry is the crowded layer
B22F and B33Y classifications appear in essentially every record, confirming the dataset's scope, while C22C alloy classification reaches 84.6% of records. Casting, welding, ceramics and radiation-shielding classes each appear in only one or two records.
Citation weight sits with the older, foundational filings
The most-cited record covers a high-strength aluminium alloy powder for additive manufacturing, cited 50 times; the next most-cited cover dissimilar-alloy forming and carbide-reinforced processing. High citation counts here mark influence on later drafting, not current commercial relevance.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to aluminum alloy selective laser melting powder feedstock patent landscape, with the prior art for and against each one.
| Assignee | Co-assignee | Shared families |
|---|---|---|
| Central South University | CRRC Industry Institute Co., Ltd. | 2 |
| South China University of Technology | China General Nuclear Power Research Institute Co., Ltd. | 1 |
| China General Nuclear Power Research Institute Co., Ltd. | China General Nuclear Power Group Co., Ltd. | 1 |
| China General Nuclear Power Research Institute Co., Ltd. | China General Nuclear Power Corporation | 1 |
Only 4 co-assignee pairs appear across the dataset, the strongest linking two Chinese research institutes on 2 shared records. Most filers, including universities with several solo records, are not co-filing with corporate partners.
Go from landscape to claim-level detail
The figures above show where filing concentrates and where it is thin. The next step is testing a specific composition or process claim against the records that sit closest to it.
Search the full record set
Run a targeted search across the 52 records to pull the specific claims and citation chains behind any composition or processing route you are evaluating.
Search in EurekaMap white space before you draft
Use the classification breakdown to confirm which adjacent branches — joining, ceramics, regulated end-use — are genuinely under-claimed before committing a first claim to one of them.
Explore white space in EurekaTrack new filings as they publish
Filing is still rising and 2025-2026 records are only partially published; set up monitoring so new entrants and citing filings surface as soon as they appear.
Set up monitoring in EurekaFrequently asked questions
The assignee ranking covers 37 companies and research institutes across the 52 records in scope, led by a single filer with 6 records. The top 5 combined account for 20 records, or 38.5% of all records in scope, while the top 10 combined reach 31 records, or 59.6%. Beyond that concentration at the top there is a long tail of entities with only one or two filings, mostly Chinese universities and materials companies, so the field is not dominated by a handful of large corporates in the way some adjacent materials fields are.
Filing activity rose from zero records in 2017 to a peak of 13 in 2023, and the three-year window from 2021 to 2024 shows a +50% increase, from 6 to 9 records. That growth tracks the broader push to qualify aluminium alloys for additive manufacturing in structural and lightweighting applications, where feedstock composition directly determines print quality, porosity and mechanical properties. Because publication typically lags filing by about 18 months, the 2025 and 2026 figures in any trend chart understate real filing activity and should not be read as a slowdown.
Two routes recur most often among the most-cited records: high-strength aluminium alloy powders engineered specifically for additive manufacturing, and reinforcement-particle approaches that add carbide, graphene or rare-earth elements to control grain structure and reduce cracking during laser melting. A related but narrower route covers dissimilar-alloy forming, such as titanium-aluminium combinations processed by selective laser melting. Alloy composition claims generally (C22C) appear in 84.6% of the 52 records in scope, making that the densest area of prior art to search before proposing a new feedstock chemistry.
The thinnest branches by classification are welding and brazing integration, ceramic or refractory composite feedstocks, radiation-shielding applications, and casting-adjacent processing — each appearing in only one or two of the 52 records, compared to near-universal coverage of powder metallurgy and additive manufacturing classes. This suggests the gap sits in joining steps and end-use framing rather than in core alloy chemistry, which is already heavily claimed. Co-assignee filing is also thin, with only 4 pairs recorded across the dataset, meaning even collaborative claims in these adjacent branches are largely unexplored.
Not necessarily. Citation counts inside a searched patent corpus tend to favour older records simply because they have had more time to be cited by later filings, so a high count is better read as a signal of influence on subsequent claim drafting than as evidence the technology is still the commercial frontier. The most-cited record in this dataset, for example, has 50 citations and covers foundational high-strength aluminium alloy powder composition, which later filers had to draft around. Anyone assessing current commercial relevance should weigh citation count alongside filing recency and the specific claim scope, not treat it as a standalone importance score.
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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.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
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.