Augmented geometry assurance digital twin with physics-based incremental learning
Artikel i vetenskaplig tidskrift, 2025

This paper presents a novel digital twin framework employing batch incremental learning for geometry assurance. Addressing quality issues caused by part and process variation, the method evaluates three critical tasks: part matching, locator adjustments, and joining sequence. The proposed framework utilizes deep learning architectures, each trained on recursive simulation data. Employing incremental learning, the models adapt to new batch characteristics while maintaining predictive accuracy. A spot welded assembly demonstrated the proposed approach efficiency, achieving prediction accuracies with errors as low as 0.02 mm for part matching and 0.1 mm for locator adjustments.

Tolerancing

digital twin

quality assurance

Författare

Roham Sadeghi Tabar

Chalmers, Industri- och materialvetenskap, Produktutveckling

Rikard Söderberg

Chalmers, Industri- och materialvetenskap, Produktutveckling

Dariusz Ceglarek

The University of Warwick

Pasquale Franciosa

The University of Warwick

Lars Lindkvist

Chalmers, Industri- och materialvetenskap, Produktutveckling

CIRP Annals - Manufacturing Technology

0007-8506 (ISSN) 17260604 (eISSN)

Vol. In Press

Digital synkronisering av geometridata för effektiva värdekedjor (DigiSync)

VINNOVA (2024-02505), 2024-11-14 -- 2027-10-31.

Ämneskategorier (SSIF 2025)

Produktionsteknik, arbetsvetenskap och ergonomi

Datavetenskap (datalogi)

DOI

10.1016/j.cirp.2025.03.008

Mer information

Senast uppdaterat

2025-04-29