Adhesion studies during generative hybridization of textile-reinforced thermoplastic composites via additive manufacturing

Research output: Contribution to journalResearch articleContributedpeer-review

Abstract

Generative hybridization enables the efficient production of lightweight structures by combining classic manufacturing processes with additive manufacturing technologies. This type of functionalization process allows components with high geometric complexity and high mechanical properties to be produced efficiently in small series without the need for additional molds. In this study, hybrid specimens were generated by additively depositing PA6 (polyamide 6) via fused layer modeling (FLM) onto continuous woven fiber GF/PA6 (glass fiber/polyamide 6) flat preforms. Spe-cifically, the effects of surface pre‐treatment and process‐induced surface interactions were investigated using optical microscopy for contact angle measurements as well as laser profilometry and thermal analytics. The bonding characteristic at the interface was evaluated via quasi‐static tensile pull‐off tests. Results indicate that both the bond strength and corresponding failure type vary with pre‐treatment settings and process parameters during generative hybridization. It is shown that both the base substrate temperature and the FLM nozzle distance have a significant influence on the adhesive tensile strength. In particular, it can be seen that surface activation by plasma can significantly improve the specific adhesion in generative hybridization.

Details

Original languageEnglish
Article number3888
Number of pages12
JournalMaterials
Volume14
Issue number14
Publication statusPublished - 12 Jul 2021
Peer-reviewedYes

External IDs

Scopus 85110922686
ORCID /0000-0003-2834-8933/work/142238228
ORCID /0000-0003-1370-064X/work/142243385
WOS 000676329600001
PubMed 34300806

Keywords

Keywords

  • additive manufacturing, functionalization, thermoplastic composite, adhesion, fused layer modeling, generative hybridization, multi-material design