Melt-Pool Monitoring Patents: Top Companies & Filing Trends 2026
Filing growth compares 2021 (32 records) with 2024 (32) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 339 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
This landscape tracks patent filings at the intersection of melt-pool and powder-bed monitoring and metal additive manufacturing — the sensing, imaging and control art that watches the laser-material interaction in powder bed fusion machines. The scope spans 339 published records filed between 2015 and the 2026 cut-off, drawn from families that claim melt-pool monitoring, powder-bed monitoring, LPBF melt-pool detection, or laser melt-pool sensing in combination with metal 3D printing or powder bed fusion terminology.
Coverage runs across major receiving offices, with the United States, the EPO and the WIPO PCT route carrying the bulk of filings. Because publication trails filing by roughly 18 months, the final one to two years in any trend chart will read lower than the true filing rate once those applications publish.
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Filing trend and technology composition
Two views of the same 339-record dataset: how filing activity has moved year over year, and which IPC subclasses carry the claims.
A decade of filings, flattening after 2022
Filings rose from 32 in 2017 to a peak of 59 in 2022, then settled back to 32 in both 2021 and 2024 — a flat three-year span with 0% net change. The 2025 and 2026 figures are still filling in as publications catch up to filing dates, so they should not be read as a slowdown yet.
Additive manufacturing and powder metallurgy classes dominate
B33Y (additive manufacturing) appears on 85.0% of the 339 records and B22F (powder metallurgy) on 74.6% — expected given the search scope. Sensing- and control-adjacent classes are thinner: G01N material analysis sits at 10.6%, G02B optics at 8.6%, and G05B control systems at 7.4%, each well below half the density of the two lead classes.
Shares are the percentage of the 339 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Metal Additive Manufacturing — Laser-Powder-Bed Melt-Pool Monitoring Patent Landscape with Eureka
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Try EurekaMost-cited records and a representative filing
US20240253126A1 — Rules Based Scan Strategy for Powder Bed Fusion
Laser powder bed fusion additive manufacturing of parts is provided. The method comprises converting a 3D geometry for a part into a number of 2D layers, wherein the 2D layers contain information about the local 3D geometry. A number of laser scan parameters are specified according to preexisting empirical melt pool data for a specified build material. Laser energy levels are specified according to unique characteristics of a specific powder bed fusion machine. Laser and laser beam steering are controlled in the specific powder bed fusion machine according to the specified laser scan parameters and specified laser energy levels to additively manufacture the part.Filed by Board of Regents, The University of Texas System, published 2024-08-01.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US9956612B1 | Additive manufacturing using a mobile scan area | 182 |
| 2 | US10022795B1 | Large scale additive machine | 127 |
| 3 | US10022794B1 | Additive manufacturing using a mobile build volume | 114 |
| 4 | US20180200962A1 | Additive manufacturing using a dynamically grown build envelope | 108 |
| 5 | US20170232515A1 | Additive Manufacturing Simulation System And Method | 103 |
| 6 | US10478893B1 | Additive manufacturing using a selective recoater | 81 |
| 7 | US20180200792A1 | Additive manufacturing using a mobile build volume | 53 |
| 8 | US20200333295A1 | Enhanced non-destructive testing in directed energy material processing | 41 |
| 9 | WO2019125970A1 | Convolutional neural network evaluation of additive manufacturing images, and additive manufacturing system b… | 41 |
| 10 | US20180120260A1 | In-Process Quality Assessment for Additive Manufacturing | 39 |
Citation counts favour older filings that have had more time to accumulate citations within the searched corpus; treat them as a signal of influence on the field, not of current technical importance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the numbers mean for a filing decision
Three read-outs from the dataset that matter more for a filing or freedom-to-operate decision than the raw counts alone.
Five assignees hold two-thirds of the field
The top 5 combined account for 226 of the 339 records in scope, 66.7% of the whole dataset, and the top 10 reach 78.5%. A field this concentrated at the top with a long tail of single- or few-filing entrants below it means new entrants need to design deliberately around the leaders' claim sets rather than assume open ground.
Flat, not falling, after the 2022 peak
Filing volume peaked at 59 in 2022 and settled back to 32 by 2024, matching the 2021 level exactly for 0% net change over that three-year span. Some of the leading assignees show 0 filings and -100% year-on-year in the most recent year, which is consistent with publication lag rather than an actual pullback from the technology.
Control-loop integration is the thin class
B33Y and B22F between them touch the large majority of records, but classes that describe closing the loop from sensor signal to process control — G05B at 7.4%, G02B optics at 8.6% — cover a much smaller slice. High density in the printing and powder classes does not mean the control layer is equally claimed.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to metal additive manufacturing — laser-powder-bed melt-pool monitoring patent landscape, with the prior art for and against each one.
| Assignee | Co-assignee | Shared families |
|---|---|---|
| General Electric Co | Concept Laser GmbH | 22 |
| Concept Laser GmbH | GE ADDITIVE GERMANY GMBH | 6 |
| General Electric Co | GE Avio S.r.l. | 2 |
| General Electric Co | GE ADDITIVE GERMANY GMBH | 2 |
Only 4 co-assignee pairs appear in the dataset, with the strongest pairing recorded at 22 shared filings — most patenting here happens under a single assignee rather than through joint filing.
Where to take this
The dataset points to a field with a dominant leader, a flat but not shrinking filing rate, and specific thin spots in the control and sensing classes.
Map freedom-to-operate against the top five
With 66.7% of records held by five assignees, any new filing in melt-pool sensing or scan-strategy control should be checked against those portfolios first, not the field average.
Run a freedom-to-operate check in EurekaWatch the control-loop classes for entry points
G05B and G02B sit well below the density of the core additive-manufacturing classes, suggesting sensor-to-control integration claims have more open ground than powder-bed or build-process claims.
Explore white space in EurekaQuestions practitioners ask about this field
The ranked assignee list covers 56 companies, and one leader holds 125 of the 339 records in scope — well ahead of the field. The top 5 assignees combined hold 226 records, 66.7% of all records in scope, and the top 10 reach 78.5%. This is a concentrated field: a small number of established additive-manufacturing machine builders and industrial gas or materials firms account for most of the claim space, with a long tail of single-digit filers behind them.
Filing peaked at 59 in 2022 and had returned to 32 by 2024, exactly matching the 2021 level for 0% net change over that three-year span. That reads as a plateau rather than growth or decline. Figures for 2025 and 2026 are understated because publication typically lags filing by around 18 months, so recent-looking dips should not be interpreted as the technology losing momentum.
The dominant classes are B33Y (additive manufacturing / 3D printing), present on 85.0% of the 339 records, and B22F (powder metallurgy), on 74.6%. B29C (shaping of plastics) appears on 49.3% and B23K (welding, soldering and brazing) on 25.4%. Sensing- and control-specific classes are comparatively thin: G01N material analysis at 10.6%, G02B optics at 8.6%, and G05B control systems at 7.4%. Because records often carry several IPC codes, these shares add up to more than 100%.
The clearest gap sits between sensing and control: classes covering optical systems (G02B, 8.6%) and closed-loop control (G05B, 7.4%) are far less densely claimed than the core additive-manufacturing and powder-metallurgy classes. That suggests claims tying a specific sensor signal directly to a real-time process-control action, rather than sensing or printing alone, face less prior art density. Co-assignee filing is also rare, with only 4 pairs recorded in the dataset, indicating little claimed collaborative work in this space.
US20240253126A1, filed by Board of Regents, The University of Texas System and published 2024-08-01, claims a laser powder bed fusion method that sets scan parameters and laser energy levels from preexisting empirical melt-pool data tied to a specific build material and machine. It matters as a representative example of scan-strategy claims that reference empirical melt-pool behaviour rather than raw sensor output, a pattern worth checking against before filing new scan-control claims in this area. It should be read alongside the top-cited records in the dataset, several of which cover build-volume and build-envelope methods from the same competitive set.
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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.