Abstract

The recent advances in additive manufacturing technology allow the realization of single-process thermoplastic material extrusion (TME) 3D-printed embedded sensors, leading to the easy and inexpensive production of smart structures. While single-process TME dynamic strain sensors have already been researched, vibration durability self-awareness is more than just an additional 3D printed strain sensor and several questions need to be answered. Is the durability self-aware sensors position structure-specific? Is the fatigue life of the sensory element longer than the base structure? Does the fatigue influence the self-awareness capability? Those and several other questions are theoretically and experimentally addressed in this research. Two different fatigue identification methods are researched (i.e. the peak-response and the frequency-drop methods). It was found that the vibration durability self-aware structure printed in a single process is viable and the frequency-drop based method gives reliable fatigue estimation; the fatigue damage was correctly identified even in the case the sensory element was 3D printed in the fatigue zone and already significantly damaged. This research opens up new capabilities for self-aware TME 3D-printed structures.

Keywords

additive manufacturing;thermoplastic material extrusion;vibration fatigue;smart structures;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 004.925.84:621.79
COBISS: 76843523 Link will open in a new window
ISSN: 2214-8604
Views: 187
Downloads: 20
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: dodajalne tehnologije;ekstruzija termoplastičnega materiala;vibracijska poškodovanost;pametne strukture;
Type (COBISS): Article
Embargo end date (OpenAIRE): 2023-09-11
Pages: str. 1-8
Issue: ǂVol. ǂ47
Chronology: Nov. 2021
DOI: 10.1016/j.addma.2021.102303
ID: 13495823