A Fast Analytical Method for Elastic–Plastic Analysis of Threaded Connections

Threaded connections are fundamental in engineering structures, yet their elastic–plastic behavior under load remains challenging to model analytically. The yield limit can be reached under relatively small external loads, and elastic–plastic behavior has predominantly been studied using finite element models. While these models are highly valuable, they are often restricted to specific cases. This paper presents a novel extension of Maduschka’s classical method, offering a fast and efficient analytical approach to evaluate the behavior of screw–nut–washer assemblies. The method tracks plastic strain progression from initial yielding to full yield conditions and is validated against high-fidelity axisymmetric and 3D finite element analyses (FEAs) across a range of thread dimensions (M16–M36). Results demonstrate strong agreement with FEA benchmarks while achieving significant computational speedups, making the method suitable for iterative and large-scale analyses. In addition, the comparison with results available in the literature further supports the reliability of the proposed method. Its robustness to variations in geometry, friction, and thread count positions it as a foundation for reduced-order models, ready for integration into complex finite element frameworks commonly used in structural health monitoring and digital twin technologies.

Authors

Institutions

Publication Details

Journal
Applied Mechanics
Published
2025-06-06
DOI
https://doi.org/10.3390/applmech6020042
Primary Topic
Engineering Structural Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Fast Analytical Method for Elastic–Plastic Analysis of Threaded Connections

Corrado Groth, C. Brutti, Marco Evangelos Biancolini
Applied Mechanics
Engineering Structural Analysis Methods
article

A Fast Analytical Method for Elastic–Plastic Analysis of Threaded Connections

Corrado Groth, C. Brutti, Marco Evangelos Biancolini
article en

Abstract

Threaded connections are fundamental in engineering structures, yet their elastic–plastic behavior under load remains challenging to model analytically. The yield limit can be reached under relatively small external loads, and elastic–plastic behavior has predominantly been studied using finite element models. While these models are highly valuable, they are often restricted to specific cases. This paper presents a novel extension of Maduschka’s classical method, offering a fast and efficient analytical approach to evaluate the behavior of screw–nut–washer assemblies. The method tracks plastic strain progression from initial yielding to full yield conditions and is validated against high-fidelity axisymmetric and 3D finite element analyses (FEAs) across a range of thread dimensions (M16–M36). Results demonstrate strong agreement with FEA benchmarks while achieving significant computational speedups, making the method suitable for iterative and large-scale analyses. In addition, the comparison with results available in the literature further supports the reliability of the proposed method. Its robustness to variations in geometry, friction, and thread count positions it as a foundation for reduced-order models, ready for integration into complex finite element frameworks commonly used in structural health monitoring and digital twin technologies.

Applied MechanicsVol. 6(2)
University of Rome Tor Vergata (IT)
Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro
Openalex Percentile: Top 88%
Engineering Structural Analysis Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.