Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques.

Autor: Ashebir DA; School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.; Aerostructures Innovation Research Hub (AIR Hub), Swinburne University of Technology, Hawthorn, VIC 3122, Australia., Hendlmeier A; Aerostructures Innovation Research Hub (AIR Hub), Swinburne University of Technology, Hawthorn, VIC 3122, Australia., Dunn M; School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia., Arablouei R; Data61, Commonwealth Scientific and Industrial Research Organisation, Pullenvale, QLD 4069, Australia., Lomov SV; Department of Materials Engineering, KU Leuven, 3001 Leuven, Belgium., Di Pietro A; Aerostructures Innovation Research Hub (AIR Hub), Swinburne University of Technology, Hawthorn, VIC 3122, Australia., Nikzad M; School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.
Jazyk: angličtina
Zdroj: Polymers [Polymers (Basel)] 2024 Oct 24; Vol. 16 (21). Date of Electronic Publication: 2024 Oct 24.
DOI: 10.3390/polym16212986
Abstrakt: Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10-15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite's overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25-30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components.
Databáze: MEDLINE
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