Experimental and numerical investigations on the mechanical properties of overmolded hybrid fiber reinforced thermoplastic composites

Pingyang Tan; Zhanyu Zhai; Ningfeng Liu; Wuping Shi

doi:10.1515/polyeng-2024-0064

Abstract

In this work, an anti-collision beam was manufactured through a thermoplastic composite overmolding (TCO) process. This process includes thermoforming of continuous glass fiber reinforced thermoplastic composite (CGFR-PP) and overmolding of short glass fiber reinforced thermoplastic composite (SGFR-PP). Double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain the interfacial bonding fracture toughness between CGFR-PP and SGFR-PP, which was then used to establish a cohesive zone model (CZM). A continuum damage model (CDM) based on Tsai-Wu criterion was established to simulate the damage behavior of CGFR-PP. Tensile and bending tests on CGFR-PP and single lap shear (SLS) tests were conducted to verify the validity of the CDM and CZM. At last, the finite element model was used to predict the bending properties of the anti-collision beam, and the error of maximum load is approximately 5 %. Results reveal that the simulation results demonstrated a good agreement with the experimentally obtained force-displacement curves in terms of stiffness and maximum load.

Keywords: thermoplastic composite overmolding; cohesive zone model; interfacial properties; failure prediction; anti-collision beam

Corresponding author: Zhanyu Zhai, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China; and State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China, E-mail: zhanyuzhai@csu.edu.cn

Funding source: Fundamental Research Funds for the Central Universities of Central South University

Award Identifier / Grant number: 2024ZZTS0791

Funding source: the Natural Science Foundation of Changsha

Award Identifier / Grant number: kq242214

Acknowledgments

The authors thank the valuable support of the Fundamental Research Funds for the Central Universities of Central South University; the Natural Science Foundation of Changsha.

Research ethics: The local Institutional Review Board deemed the study exempt from review.
Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
Author contributions: P.T. designed the experiment, conducted data curation, developed the software, and drafted the manuscript. Z.Z. conceptualized the study, validated the findings, visualized the data, supervised the project, and acquired funding. N.L. contributed to methodology and software development. W.S. conducted the investigation. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, Al and Machine Learning Tools: The authors utilized OpenAI’s ChatGPT to assist in drafting and editing sections of this manuscript, such as refining language and formatting. All content generated by AI tools was critically reviewed and verified by the authors to ensure its accuracy and alignment with the research objectives.
Conflict of interest: The authors state no conflict of interest.
Research funding: This work was supported by the Fundamental Research Funds for the Central Universities of Central South University (2024ZZTS0791); the Natural Science Foundation of Changsha (kq242214).
Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/polyeng-2024-0064).

Received: 2024-04-12

Accepted: 2024-11-11

Published Online: 2025-01-29

Published in Print: 2025-02-25

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Supplementary Material