Automated Author Profile

Rouvinet, Julien

Current S-Index

1.3

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.7

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

84.6%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Automated rigid bodies synthesis for AM compliant mechanisms

Compliant mechanisms achieve motion through elastic deformation rather than traditional rigid-body joints, eliminating wear, backlash, friction, and the need for lubrication. These advantages make them ideal for high-precision applications and harsh environments. While the design of compliant joints is well-studied, the design of the rigid bodies – connecting the joints and transmitting forces/motions – is often overlooked. Existing approaches such as manual modelling, parametric design, and topology optimisation are inadequate for automation due to their fragmented workflows, limited flexibility, and lack of real-time responsiveness. This paper introduces a computational framework for the design of rigid bodies in compliant mechanisms, considering both functional, non-functional objectives and additive manufacturing constraints. Building on guiding curve-based design approaches, the method enables seamless integration of the rigid bodies' synthesis into a fully automated compliant mechanism design pipeline. The process involves: (1) initialising a curve network to connect interfaces while minimising mass, (2) optimising the network to avoid mechanical interferences, maximise non-functional criteria, and satisfy AM constraints, (3) synthesising 3D tubes with locally tuned cross-sections to eliminate critical overhangs, and (4) generating smooth geometries with integrated non-sacrificial supports to reduce post-processing. The proposed methodology ensures manufacturable, reliable, and high-performance designs, advancing the automation of functional AM-enabled compliant mechanisms.

Authors

  • Lang, Guilain ;
  • Rouvinet, Julien ;
  • Kiener, Lionel ;
  • Meboldt, Mirko
1 Citation0 Mentions85% FAIR0.7 Dataset Index
10.6084/m9.figshare.301725562025

Automated rigid bodies synthesis for AM compliant mechanisms

Compliant mechanisms achieve motion through elastic deformation rather than traditional rigid-body joints, eliminating wear, backlash, friction, and the need for lubrication. These advantages make them ideal for high-precision applications and harsh environments. While the design of compliant joints is well-studied, the design of the rigid bodies – connecting the joints and transmitting forces/motions – is often overlooked. Existing approaches such as manual modelling, parametric design, and topology optimisation are inadequate for automation due to their fragmented workflows, limited flexibility, and lack of real-time responsiveness. This paper introduces a computational framework for the design of rigid bodies in compliant mechanisms, considering both functional, non-functional objectives and additive manufacturing constraints. Building on guiding curve-based design approaches, the method enables seamless integration of the rigid bodies' synthesis into a fully automated compliant mechanism design pipeline. The process involves: (1) initialising a curve network to connect interfaces while minimising mass, (2) optimising the network to avoid mechanical interferences, maximise non-functional criteria, and satisfy AM constraints, (3) synthesising 3D tubes with locally tuned cross-sections to eliminate critical overhangs, and (4) generating smooth geometries with integrated non-sacrificial supports to reduce post-processing. The proposed methodology ensures manufacturable, reliable, and high-performance designs, advancing the automation of functional AM-enabled compliant mechanisms.

Authors

  • Lang, Guilain ;
  • Rouvinet, Julien ;
  • Kiener, Lionel ;
  • Meboldt, Mirko
1 Citation0 Mentions85% FAIR0.7 Dataset Index
10.6084/m9.figshare.30172556.v12025