ANGERS, France

Guenael Germain

This inventor holds 1 USPTO granted patent. Top assignee: Airbus Operations (s.a.s.). Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Guenael Germain. Retrieved from https://idiyas.com/inventor/guenael-germain

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile


Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Guenael Germain: Inventor in Machining and Residual Stress Estimation

Introduction

Guenael Germain is an inventor based in Angers, France, whose patent work addresses residual stress estimation during machining. His listed patent is associated with Airbus Operations (S.A.S.).

Latest Patents

Germain is credited with 1 patent: “Method for Estimating a Residual Stress Field in a Workpiece During Machining and Machining Process Using Said Method.”

The method estimates residual stress in a workpiece by measuring its geometry during machining and using that geometric profile to predict subsequent deformation. A machine-learning estimation model is trained on data from other workpieces, while a generic physical simulation model relates manufacturing parameters of the raw workpiece to variations in residual stress. Together, these models provide an estimate of the stress field during the machining process.

Career Highlights

Germain’s listed invention combines machine learning with physical simulation to estimate how a workpiece may deform as machining continues. The approach uses measurements taken during production, with the aim of informing understanding of residual stresses in the material.

Collaborations

The provided information lists Hugo Chabeauti and Mathieu Ritou as Germain’s coworkers. His patent is associated with Airbus Operations (S.A.S.).

Conclusion

Guenael Germain’s patent focuses on estimating residual stress during machining by combining workpiece measurements, machine-learning predictions, and physical simulation.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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