Numerical Methodology Development and Validation for Fatigue Life Prediction of PA-6 Long Fibre Thermoplastic Material Manufactured Using Compression Moulding

Abstract This paper presents a numerical methodology developed for the modelling of fatigue life of PA6 long fibre thermoplastic (LFT) material with 40% glass fibre content (PA6-LFT40) produced using compression moulding for the incorporation of long fibre, which enhances the mechanical properties compared to injection-moulding. Firstly, compression moulding process simulations were performed to predict fibre orientation using Autodesk Moldflow. Model parameters for process simulation were calibrated until the predicted fibre orientation matched the orientations obtained using micro X ray-Computer Tomography on PA6-LFT40 plates. The calibrated fibre orientations were then mapped to a finite element method (FEM) solver mesh to perform standard static three point bending test. Tensile tests served as the corresponding material model inputs for the simulations. Using mapped FEM simulation, the deviation between simulation and tests were less than 10% that is much better than without mapping. Finally, using the experimentally determined Haigh diagram, three point bending fatigue testing of PA6-LFT40 at different load levels at a stress ratio of R = -1 were performed and evaluated by using FEMFAT. The predicted fatigue life at different load level showed good correlation with test results. In conclusion, the developed numerical methodology, enhances the predictive modelling of fatigue life of compression moulded LFT Composites.

Authors

Institutions

Publication Details

Journal
Applied Composite Materials
Published
2025-04-09
DOI
https://doi.org/10.1007/s10443-025-10328-2
Citations
2
Primary Topic
Mechanical Behavior of Composites
Type
article
Field-Weighted Citation Impact
1.73

Funders

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

Numerical Methodology Development and Validation for Fatigue Life Prediction of PA-6 Long Fibre Thermoplastic Material Manufactured Using Compression Moulding

Sharath Christy Anand, Xiangfan Fang
2 citations
Applied Composite Materials
Mechanical Behavior of Composites
1.73
article

Numerical Methodology Development and Validation for Fatigue Life Prediction of PA-6 Long Fibre Thermoplastic Material Manufactured Using Compression Moulding

Sharath Christy Anand, Xiangfan Fang
article en
2 citations

Abstract

Abstract This paper presents a numerical methodology developed for the modelling of fatigue life of PA6 long fibre thermoplastic (LFT) material with 40% glass fibre content (PA6-LFT40) produced using compression moulding for the incorporation of long fibre, which enhances the mechanical properties compared to injection-moulding. Firstly, compression moulding process simulations were performed to predict fibre orientation using Autodesk Moldflow. Model parameters for process simulation were calibrated until the predicted fibre orientation matched the orientations obtained using micro X ray-Computer Tomography on PA6-LFT40 plates. The calibrated fibre orientations were then mapped to a finite element method (FEM) solver mesh to perform standard static three point bending test. Tensile tests served as the corresponding material model inputs for the simulations. Using mapped FEM simulation, the deviation between simulation and tests were less than 10% that is much better than without mapping. Finally, using the experimentally determined Haigh diagram, three point bending fatigue testing of PA6-LFT40 at different load levels at a stress ratio of R = -1 were performed and evaluated by using FEMFAT. The predicted fatigue life at different load level showed good correlation with test results. In conclusion, the developed numerical methodology, enhances the predictive modelling of fatigue life of compression moulded LFT Composites.

Applied Composite MaterialsVol. 32(4)
University of Siegen (DE)
Universität Siegen, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 19%
Mechanical Behavior of Composites
1.73
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.