Fiber Breakage Research Articles (Page 1)

Influence of cellulose content, lignin content, and fiber breakage on the tensile strength of fiber bundles extracted for different bamboo stem regions

Scaling of multiphase flow with elastic fiber breakage employing CFD-DEM framework

Motorcyclist safety is highly influenced by the effectiveness of head protection, making material innovation in helmet manufacturing a primary focus particularly through the use of composite materials. This study aims to evaluate the mechanical properties of hybrid composites made from ramie, jute, and E-glass fibers, formulated with varying fiber mass fractions and epoxy resin as the matrix. The specimens were fabricated using the hand lay-up method and tested according to ASTM D638 for tensile strength and ASTM D790 for bending strength. The results showed that specimen D, which contained the highest mass fraction of E-glass fiber, demonstrated the best performance in tensile testing with an elastic modulus of 3062.244 MPa, tensile strength of 91.714 MPa, and the lowest strain at 2.14%, indicating high stiffness but brittle behavior. In contrast, specimen C, with the highest jute fiber content, had the lowest tensile modulus and strength, but the highest strain, indicating greater flexibility. In bending tests, specimen D showed the highest modulus of elasticity at 586.016 MPa, while specimen C recorded the highest flexural strength at 34.921 MPa. Macro observations revealed defects such as delamination, fiber breakage, and voids. The study concludes that fiber mass fraction significantly affects the mechanical properties of hybrid composites, and placing E-glass fibers in the outer laminate layers enhances flexural strength. This research contributes to the development of SNI-compliant helmets that are lightweight, strong, and environmentally friendly.

ABSTRACT Carbon fiber‐reinforced polyamide composites (PA612/CF) are vital for lightweight, high‐performance applications. This study evaluates a novel screw (ES) with periodic channel geometry against conventional screws (CS) for processing PA612/CF. Through rheological, fiber retention, crystallization, and mechanical analyses, the ES screw's elongational flow–dominated shear field enhanced melt flowability (longer spiral flow) and reduced molecular entanglement. Processing PA612 with the ES screw yields higher molecular weight due to shear optimization that minimizes degradation, thereby enhancing mechanical properties and crystallization potential. The ES screw minimized fiber breakage, achieving longer retention lengths than CS, while improving fiber dispersion and interfacial bonding. Impact strength tests revealed the ES screw outperformed the CS screw at 0–40 wt% fiber content due to reduced stress concentration and delayed crack propagation. The ES screw combines efficient mixing with low fiber damage, offering a breakthrough for high‐fiber‐content engineering plastics. This work provides critical insights for optimizing screw design to enhance composite performance in aerospace and automotive industries. This research integrates equipment design (ES screw), process innovation (elongational–shear hybrid flow fields), and performance breakthrough (high retained fiber length/low entanglement melt), enabling cross‐scale innovation from basic theory to industrial applications.

This contribution examines the usefulness of Acoustic Emissions (AE) as a non-destructive testing (NDT) method for detecting and distinguishing damages in carbon fiber reinforced polymer (CFRP) structures. Despite the widespread use of CFRP materials in various industries due to their favorable strength-to-weight ratio, the susceptibility to concealed internal damages necessitates advanced inspection techniques. Acoustic Emission, describing the use of ultrasonic waves emitted during deformation or damage events, is a proven and promising solution for real-time and reliable damage assessment. The study focuses on comparing two approaches: 1) a one-class Support Vector Machine (SVM) for initial damage detection, followed by detailed damage classification, and 2) a direct classification approach using five classes (four representing the material specific damage types and one for background noise). Both approaches undergo a systematic evaluation under diverse loading conditions to assess their reliability. A comprehensive experimental setup subjects CFRP specimens to controlled loading conditions, inducing various damage types and severities. Signal analysis reveals characteristic patterns associated with different damage modes, including matrix cracking, fiber breakage, debonding, and delamination. The investigation considers the influence of loading conditions on the detection and classification results to examine the robustness of the approach. The comparison between methodologies involves established metrics and analyses the posterior probability of the trained models, considering the impact of loading conditions on performance. The experimental results show AE’s effectiveness in detecting and classifying damages in CFRP structures, offering insights into technique sensitivity and specificity for different damage types. These findings contribute new knowledge to the NDT field, presenting a promising path for the advancement of CFRP structural health monitoring and maintenance practices in engineering applications. The study’s nuanced understanding of the strengths and limitations of the two classification approaches, considering loading conditions, contributes to the optimization of NDT strategies for diverse operation scenarios.

Continuous fibre-reinforced polymer composite fused deposition modelling (CFRP-FDM) is an emerging additive manufacturing technology that enables the cost-effective production of high-performance, lightweight structures. However, the underlying mechanism of this material co-extrusion process remains poorly understood, resulting in challenges related to part quality, e.g. low fibre content and inadequate impregnation, as well as process stability issues, e.g. nozzle clogging and fibre breakage, persist. Focusing on pre-impregnated CFRP-FDM, this research presents a Computational Fluid Dynamics (CFD) model to simulate the co-extrusion process and proposes a surrogate model-based optimisation framework to improve the structural design of the hot-end channel, a critical component of the printer. The numerical simulations reveal the mechanisms of polymer melt flow and its interaction with pre-impregnated fibres. Additionally, a performance metric for evaluating the hot-end design is introduced for the first time and used as the design objective in optimisation. The results of geometric optimisation highlight specific design patterns, providing valuable insights for future improvement of the CFRP-FDM process.

Legacy fiber passive optical networks (PONs) require not only high-speed data access but also regular in-service monitoring. Optical time domain reflectometry (OTDR) is a fundamental technique for PON in-service monitoring. Traditional OTDR is limited to detecting fiber breakpoints along the feeder fiber, failing to achieve PON monitoring of the point-to-multipoint (P2MP) structure. In this work, we exploit the Fresnel reflection points in access networks as fiber-optic sensors for PON branch sensing. Linear frequency modulation (LFM)-based phase sensitive OTDR (φ-OTDR) is employed for distributed sensing and power profile estimation of the PON feeder fiber, while Fresnel reflections from fiber connectors are utilized for event detection and trace recognition in optical network unit (ONU) branches. The system achieves a spatial resolution of 1 m for distributed sensing along the feeder fiber, with a sensing sensitivity of 56 p ε /Hz. The longitudinal power profile is also acquired by the averaging of the long-term φ -OTDR trace and indicates a fiber attenuation coefficient of 0.207 dB/km for the standard single-mode feeder fiber at 1550.13 nm. By adjusting the fiber lengths and the positions of the reference reflectors, traces from four distinct branches are recognized, and a spatial resolution of 0.2 m is achieved. Experimental results demonstrate the precise detection of fiber break points and bending loss, as well as the ability to monitor variations in strain and temperature. The system exhibits a vibration detection sensitivity of 93 p ε /Hz, with a coefficient of 15.11 rad/mK for phase changes induced by temperature variations. In addition, we discuss the implementation of in-service PON monitoring based on the proposed scheme, comparing cost, feeder fiber sensing, and branch recognition performance with the existing OTDR-based PON monitoring schemes.

Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset quasi-isotropic CFRPs subjected to quasi-static tensile loading until failure. Acoustic emission (AE) signals acquired from an experiment were leveraged to analyze and classify these real-time signals into the failure modes using machine learning (ML) techniques. Due to the extensive number of AE signals recorded during testing, manually classifying these failure mechanisms through waveform inspection was impractical. ML, alongside ensemble learning, algorithms were implemented to streamline the classification, making it more efficient, accurate, and reliable. Conventional AE parameters from the data acquisition system and feature extraction techniques applied to the recorded waveforms were implemented exclusively as classification features to investigate their reliability and accuracy in classifying failure modes in CFRPs. The classification models exhibited up to 99% accuracy, as depicted by evaluation metrics. Further studies, using cross-correlation techniques, ascertained the presence of fiber break events occurring in the bundles as the thermoset CFRP composite approached failure. These findings highlight the significance of integrating machine learning into SHM for the early detection of real-time damage and effective monitoring of residual life in composite materials.

Glass fiber-reinforced polypropylene (PP/GF) is used widely in lightweight automotive applications, but it is affected adversely by fiber breakage and matrix degradation during recycling. This study investigates the effects of carbon nanofiber (CBNF) addition on the recyclability of PP/GF composites subjected to repeated extrusion. Homo-type PP was compounded with GF and CBNFs and was processed for up to nine extrusion cycles. Melt viscosity, fiber morphology, flexural properties, interfacial shear strength, and notched Charpy impact strength were evaluated. Neat PP showed a pronounced increase in the melt volume-flow rate (MVR) with cumulative cycles, indicating molecular degradation. By contrast, CBNF-containing composites exhibited superior viscosity stability, with MVR increasing only 2.9-fold after nine cycles compared with 5.4-fold for GF-only systems. Fiber length was well maintained (96–98% retention). The flexural strength and modulus were preserved, respectively, as greater than 92% and 95%. The interfacial shear strength remained stable, whereas the impact strength decreased moderately but retained 84% of its initial value. These results underscore that a slight addition of CBNFs (5 wt%) suppresses viscosity loss effectively and stabilizes mechanical performance, offering a viable strategy for sustainable recycling of PP/GF composites in transportation applications.

ABSTRACTThis paper experimentally investigates the thermal and mechanical performance of woven phenolic laminates reinforced with carbon (PF‐CFRP), glass (PF‐GFRP), basalt (PF‐BFRP), and Kevlar (PF‐KFRP), aiming to evaluate their suitability for aircraft baggage structures under fire and blast loads. Phenolic resin cured under single‐stage high‐temperature conditions (PF100) demonstrated a superior glass transition temperature of 150.0°C, tensile modulus of 2.61 GPa, and tensile strength of 32.51 MPa. Thermogravimetric analysis revealed that PF‐CFRP retained 87.9% mass at 800°C under nitrogen, while PF‐BFRP retained the highest mass in air (79.1%), followed by PF‐GFRP (66.3%). PF‐KFRP exhibited poor thermal stability (47.7%), even lower than the neat resin (58.9%) under nitrogen. PF‐CFRP exhibited the highest modulus and strength, with 68.50 GPa and 776.78 MPa, respectively, and an in‐plane shear strength of 110.46 MPa. PF‐BFRP showed the highest flexural strength of 362.93 MPa, excellent tensile strength of 587.63 MPa, and improved bending failure strain of 2.25%. PF‐GFRP exhibited moderate mechanical performance, with tensile and flexural strengths of 224.11 MPa and 292.59 MPa, respectively, with consistent shear performance. PF‐KFRP exhibited the highest failure strain (2.76% in tension), but weak interfacial bonding. Scanning electron microscopy revealed distinctive failure modes, including fiber breakage, pull‐out, delamination, and matrix cracking. Radar plots were used for comparative visualization, identifying PF‐BFRP as optimal for blast/fire resilience and PF‐CFRP for stiffness‐critical zones. Overall, the study highlights the potential of basalt‐phenolic composites and recommends functionally graded hybrid composites as next‐generation materials for aircraft baggage structures under combined mechanical and thermal conditions.

Banana cultivation generates approximately 30–40 million tons of agro-waste globally each year, with pseudo-stems constituting a significant portion. These pseudo-stems are a rich source of high-strength natural fibers, yet they remain largely underutilized, often discarded as waste. Banana fiber has garnered attention for its biodegradability, tensile strength, and potential applications in textiles, composites, and eco-friendly products. This study presents the design, fabrication, and performance evaluation of a mechanized banana stem fiber extractor aimed at addressing the limitations of labor-intensive manual extraction methods. The developed machine features a robust frame, AC motor, rollers, pulleys, and metal stripping components, designed to ensure durability, user safety, and optimal fiber yield. Critical design parameters—including roller diameter, blade geometry, motor rating, and belt coupling—were systematically optimized to achieve reliable operation, minimize fiber breakage, and reduce processing time. The comparative performance tests revealed that the mechanized extractor produced a fiber yield of 65–70%, substantially higher than the 47–50% yield obtained manually. Labor requirements decreased from 2–5 operators to a single user, while processing time per kilogram of pseudo-stem was reduced from 21 minutes to 6 minutes. Waste generation dropped from 50–55% to 25–30%, demonstrating improved resource efficiency. The extracted fibers were uniform in size and quality, suitable for diverse industrial applications. By converting agricultural residues into high-value materials, the mechanized extractor supports sustainable practices, rural income diversification, and scalable green manufacturing. This study highlights the transformative potential of banana fiber mechanization, contributing to environmental sustainability and economic development in banana-growing communities. The findings underscore the importance of integrating technological innovation with agro-waste management to create eco-friendly and economically viable solutions.

To compare adverse events (AEs) associated with Thulium Fiber Lasers (TFLs) and Holmium: YAG (Ho: YAG) lasers reported in the FDA MAUDE database, and to examine changes in TFL-related AEs following the FDA's 2021 Class II recall. The FDA MAUDE database was searched for events between 2018 and 2024 using "LUMENIS MOSES," "LUMENIS VERSAPULSE," "SOLTIVE," and "TFL + {YEAR}." Events were classified as device, patient, staff or environmental, and graded using the Gupta system (Levels I-IV). Exclusions included non-urologic procedures, insufficient detail, or duplicates. Subgroup analyses considered prostate vs. non-prostate, TFL pre- vs. post-recall, and Ho: YAG by pulse modulation (MOSES vs. standard). Chi-square or Fisher's exact tests were used for categorical data, and Wilcoxon rank-sum tests for Gupta comparisons. 954 events were included (467 TFL, 487 Ho: YAG). Console malfunctions were more common with Ho: YAG (44.4%, p < 0.0001), while fiber breaks more with TFLs (57%, p < 0.0001). Patient-involving AEs occurred more with Ho: YAG (35.3%) compared to TFLs (13.5%, p < 0.0001). Most events were Level I: 83.9% TFL vs. 59.3% Ho: YAG (p < 0.0001). Level II events were higher with Ho: YAG (39.8%), and Level III with TFLs (2.1%, p = 0.0436); one Level IV event occurred (Ho: YAG group). Post FDA recall, TFL-related Level II and III events decreased significantly. Prostate vs. non-prostate stratification showed no differences. MOSES use in Ho: YAG had fewer fiber issues, but more Level II events compared to standard holmium. Lasers in urology appear safe, with most AEs being free of patient or staff harm and classified as minor. Post-recall improvements in TFL safety profiles suggest effective corrective action.

Abstract The use of biodegradable materials in all types of applications has increased, benefiting from reduced environmental impact and increasing sustainable development. This work presents the mechanical comparison between a henequen—epoxidized vegetal oil (EVO) bio-composite and a henequen—synthetic epoxy composite. Tensile (ASTM D3039), flexural (ASTM D7264 ), and low-energy impact (ASTM D7136) tests were carried out to infer the mechanical properties. For the production of laminates a two layers of henequen plain weave were used. On one part, for EVO, the Vacuum Assisted Resin Infusion was used. On the other hand, RTM was used for bio-synthetic composites with EPOLAM 2015 resin. For both processes, a 24-h curing reaction at 25 °C is carried out to obtain laminates of 300 mm × 300 mm × 3 mm thickness. Biocomposite and bio-synthetic composite show a flexural modulus of 5.7 GPa and 5.65 GPa, and 88 and 89 MPa of flexural strength, respectively. However, the nature of the resin greatly influenced the tensile and impact properties. Even, when biocomposite and synthetic composite showed a similar elasticity modulus, around 6.3 GPa, the difference can be appreciated for the ultimate tensile strength, showing 72 MPa and 60 MPa, respectively. Positive results were found on the low-energy impact test, where the maximum absorption occurred at 5J, exhibiting 124 kJ m−1 for the bio-composite and 104 kJ m−2 for the bio-synthetic composite. Scanning Electron Microscopy shows a visible separation of the fiber-matrix interface as well as fiber pull-out and fiber breakage as dominant failure mechanisms. With these results, bio-composites based on henequen fiber can be an alternative material for structural applications.

The effect of variable fiber diameters in unidirectional fiber-reinforced bundles on stress redistributions around fiber breaks

Advanced flame-retardant poly-lactic acid biocomposites reinforced with eco-friendly electroless coated fibers: Performance evaluation of thermal, mechanical, and water absorption properties.

A numerical simulation study of soft tissue resection for low-damage precision cancer surgery.

Selenium alleviates cadmium-induced Golgi stress via HSPB7/GM130/CX-43 axis in the heart of sheep.

Thermoset fibre-reinforced composites are widely used in high-end industries, but a growing demand for more sustainable and recyclable alternatives conveyed the research efforts towards thermoplastics. To expand their usage, new approaches to their manufacture and mechanical performance must be tackled and tailored to each engineering challenge. The present study designed, manufactured and tested advanced multi-layer laminated composites of thermoplastic polypropylene prepreg reinforced with continuous woven fibreglass with interlayer toughening through thermoplastic polyurethane elastomer (TPU) layers manufactured by fused filament fabrication. The manufacturing process was iteratively optimized, resulting in successful adhesion between layers. Three composite configurations were produced: baseline glass fibre polypropylene (GFPP) prepreg and two multi-layer composites, with solid and honeycomb structured TPU layers. Thermal and mechanical analyses were conducted with both the polyurethane elastomer and the manufactured laminates. Tensile testing was conducted on additively manufactured polyurethane elastomer specimens, while laminated composites were tested in three-point bending. The results demonstrated the potential of the developed laminates. TPU multi-layer laminates exhibit higher thermal stability compared to the baseline GFPP prepreg-based composites. The addition of elastomeric layers decreases the flexural modulus but increases the ability to sustain plastic deformation. Multi-layer laminate composites presenting honeycomb TPU layers exhibit improved geometric and mechanical consistency, lower delamination and fibre breakage, and a high elastic recoverability after testing.

Additive manufacturing (AM) has transformed the production of complex parts, though high-performance composite filament development in this field is still limited. The primary objective of this study is the development of production setup of continuous fiber reinforced polymer (CFRP) composite filament for fused deposition modeling (FDM) applications. Despite their enhanced strength-to-weight ratio and mechanical properties, integrating CFRP composites into AM presents challenges like fiber alignment, breakage, interfacial adhesion, and process optimization. This study aims to address the above-mentioned challenges by developing a robust production setup for CFRP filament tailored for FDM. The design phase of the setup included gear driven CFRP winding system, extrusion system, heating system and pulling spool system. Upon the fabrication of the setup, CFRP was produced using PLA and glass fiber and the technique preserved the integrity and continuity of the reinforcement throughout the filament. Uniaxial tensile testing was performed to assess the mechanical performance of the produced filament. The experimental results demonstrated a significant improvement in tensile strength (146.75 MPa) and Young’s Modulus (4.95 GPa) at a fiber volume fraction of 2.8% for the composite filament and these values were in Line with the theoretical results. The tensile strength of the Continuous Glass Fiber-PLA showed an increase of 2.4 times while the young’s modulus yielded an increase of 1.35 times in comparison to the neat polymer. Scanning electron microscopy analysis of the fractured composite samples showed sufficient polymer impregnation and strong interfacial bonding. The energy dispersive X-ray spectroscopy was conducted confirming the polymer impregnation uniformity. The thermal characterization by differential scanning calorimetry and Thermogravimetric analysis validated the composite filament’s suitability for FDM printing. The outcomes of this study help to push forward the current advancements in AM using composite filament, paving the way for stronger, lightweight, and reliable printed structures. The insights gained are instrumental in expanding the application of composite AM in aerospace, automotive, and industrial sectors where high-performance materials are critical.

Fiber Bragg grating (FBG) sensing systems face significant challenges in resolving overlapping spectral signatures when multiple sensors operate within limited wavelength ranges, severely limiting sensor density and network scalability. This study introduces a novel Transformer-based neural network architecture that effectively resolves spectral overlap in both uniform and mixed-linewidth FBG sensor arrays, operating under bidirectional drift. The system uniquely combines dual-linewidth configurations with reflection and transmission mode fusion to enhance demodulation accuracy and sensing capacity. By integrating cloud computing, the model enables scalable deployment and near-real-time inference even in large-scale monitoring environments. The proposed approach supports self-healing functionality through dynamic switching between spectral modes during fiber breaks and enhances resilience against spectral congestion. Comprehensive evaluation across twelve drift scenarios demonstrates exceptional demodulation performance under severe spectral overlap conditions that challenge conventional peak-finding algorithms. This breakthrough establishes a new paradigm for high-density, distributed FBG sensing networks applicable to land monitoring, soil stability assessment, groundwater detection, maritime surveillance, and smart agriculture.