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In this article, bamboo nonwoven fabric (BF) is introduced as reinforcement into the vinyl ester (VE) matrix for the manufacturing of BF/VE composites through Vacuum Assisted Resin Transfer Mold process. Eco-friendly (NaHCO3) and other common treatments (NaOH and Silane) are applied to BF to modify the surface chemistry, thereby improving the mechanical strength (tensile modulus 10(NHTB), 11(NCTB) and 11% (SiTB); flexural modulus 11(NHTB) and 90% (SiTB) and impact strength 23% (SiTB)) and thermal properties. Besides, the three flame retardants (FRs) (chitosan (CS), ammonium polyphosphate (APP), and zinc borate) are incorporated with various percentages (3 and 6) for the improvement of flame retardancy of the BF composites and the experiments are proceeded by Taguchi L9 orthogonal array (OA) through the layup method. The nature of pure and modified BF is analyzed using Fourier transform infrared spectroscopy, x-ray diffraction and scanning electron microscopy exhibited the removal of the constituents and formation of reactive sites, etc. The effect of treatment on the thermal and flame retardant properties is analyzed by thermogravimetric analysis, horizontal burning test, and microcalorimeter, respectively. The thermal stability, burning rate, and peak heat release rate were significantly changed than that of pure BF composites.

In this article, bamboo nonwoven fabric (BF) is introduced as reinforcement into the vinyl ester (VE) matrix for the manufacturing of BF/VE composites through Vacuum Assisted Resin Transfer Mold process. Eco-friendly (NaHCO3) and other common treatments (NaOH and Silane) are applied to BF to modify the surface chemistry, thereby improving the mechanical strength (tensile modulus 10(NHTB), 11(NCTB) and 11% (SiTB); flexural modulus 11(NHTB) and 90% (SiTB) and impact strength 23% (SiTB)) and thermal properties. Besides, the three flame retardants (FRs) (chitosan (CS), ammonium polyphosphate (APP) and zinc borate) are incorporated with various percentages (3 and 6) for the improvement of flame retardancy of the BF composites and the experiments are proceeded by Taguchi L9 orthogonal array (OA) through the layup method. The nature of pure and modified BF is analyzed using Fourier transform-infrared spectroscopy, x-ray diffraction and scanning electron microscopy exhibited the removal of the constituents and formation of reactive sites, etc. The effect of treatment on the thermal and flame retardant properties is analyzed by thermogravimetric analysis, horizontal burning test, and microcalorimeter, respectively. The thermal stability, burning rate, and peak heat release rate were significantly changed than that of pure BF composites.

This paper presents a novel plant-derived core-shell fiber carrier that can be used in self-healing applications. The study used abaca fiber lumens as a self-healing carrier for the first time. The fibers were characterized under SEM at different positions to know the lumen structure and achieved a 39.19% lumens fraction and 16.13 µm lumen diameter. Healing resins were embedded in the lumens using modified VARTM. The core was confirmed using FESEM-EDX. Thermal degradation was determined via TGA under a nitrogen atmosphere. The mechanical and self-healing properties, as well as a single-fiber tensile test, were conducted with a cell load of 5 kgf. The healing resin (VE-CN)-embedded fibers showed a strength improvement of 14.78% compared with the strength of pure abaca fibers. The improved strength is attributed to the reorientation of the crystalline cellulose in the tensile direction. Additionally, some of the tensile-fractured fibrils self-healed after 24 h and showed a restored strength of 100.71 MPa. The restored strength explains that the fiber lumens are the potential to carry a healing resin and release upon the damage.

The novel aligned polyvinylidene fluoride (PVDF) green core–shell nanofibers were reinforced to carbon fiber/epoxy prepregs and were manufactured through the vacuum bagging technique. Aligned nanofibers were achieved by suspending a grounded needle between the nozzle and the collector of electrospinning. The self-healing properties were tested through a periodic three-point bending test at an interval of 24 h at room temperature. The healing behavior was further confirmed through field-emission scanning electron microscopy coupled with dispersion X-ray spectroscopy (EDX) and an electrical conductivity test. The self-healing prepregs (1038.42 MPa) regained 66% of their original strength (1577.85 MPa) after the initial damage. EDX analysis confirmed the elements of the resin (VE (C, O)) and hardener (MEKP (C, O), CN (C, O, Co)) from the ruptured healing carriers. The damaged carbon prepregs healed by showing electrical conductivity of around 83%. The mechanical properties of self-healing composites were tested by tensile, flexural, and Izod impact tests and showed an increment in both flexural (7–12%) and impact strength (5–7%) with the addition of nanofibers. Overall, the research findings provided a design of eco-friendly carriers for carbon fiber-reinforced composites to obtain decent self-healing properties without deteriorating the mechanical strength.

This study introducing self-healing concept into thermoplastic panels for engineering applications. Hence, the base self-healing square shaped panels were manufactured using a common thermoplastic, polylactic acid (PLA), and glass/in-situ vascular channels as healing carriers via 3D printing additive process. 3D printing is the best approach for inserting glass vascular channels and creating insitu channels inside the panels. Two types of healing resins were prepared with polymethyl methacrylate (PMMA) and Acrylonitrile 1–3 Butadiene Styrene (ABS) solutions to adjust the chemistry of PLA and healing channels for effective healing without any trouble. A drop weight impact test with the low-velocity mode is conducted on prepared PLA panels. To study the self-healing behaviour of panels 23.05 N-m impact energy was applied to create the initial damage of the PLA panels. The impacted specimens were examined through ultrasonic C-scan for observing the healing process at damaged portion. The experimental results show the effective healing behavior of PLA panels were (after self-healing) about 75% and 80% of PLA-PMMA and PLA-ABS, respectively. This study also deals on the damage model-based numerical study was established on PLA panels using the Ansys workbench to investigate the failure mechanisms of PLA self-healing panels.

Abstract This study aimed to produce a new potential flame retardant thermoset resin from epoxidised corn oil through a one-pot method using liquid inorganic catalysed with hydrogen peroxide. Using a gas chromatography–mass selective detector, attenuated total reflectance-fourier transform infrared spectroscopy, proton nuclear magnetic resonance imaging, optical microscopy, and scanning emission microscopy, we synthesised a bio-based resin based on newly designed parameters. The flame retardant capacity was fully established using thermogravimetric analysis and a micro calorimeter. The produced epoxidised corn oil had a relative percentage conversion of oxirane of approximately 91.70%, wherein the amount of double bonds converted into epoxides was calculated. A significant reduction from 17 to 40% in peak heat rate release (pHRR) and 26–30% in total heat release was observed, confirming its flame retardant property. Thus, the potential of epoxidised corn oil was demonstrated.

The main purpose of this study is to compare the effects of two advanced materials, namely shape memory alloys (SMAs) and carbon nanotubes (CNTs) on the fundamental natural frequencies of rectangular graphite fiber/epoxy (GF/EP) composite plates. The performance of each of these materials is evaluated for different geometric conditions and boundary conditions as well as operating temperatures. Moreover, the glass transition temperature is considered as a limiting factor in the study. The governing equations are derived based on first-order shear deformation theory (FSDT) and then solved by generalized differential quadrature (GDQ) technique. The effective thickness-to-width ratio and temperature ranges are determined for both SMA wires and CNTs. The results of this study show that in some conditions, the use of CNTs is preferable over SMA wires and vice versa. Moreover, unlike SMA wires, the positive effect of the CNTs is limited to a certain extent. Furthermore, the numerical results indicate that depending on the different parameters, the use of SMAs may have constructive or destructive effects on the structural vibrations. Interestingly, it is shown that in some circumstances, the co-incorporation of SMAs and CNTs leads to a significant enhancement in the natural frequencies of the structure.

Self-healing panels were prepared using vinyl ester (VE) and vascular abaca fibers (unidirectional) through the hand lay-up process. Initially, two sets of abaca fibers (AF) were prepared by filling the healing resin VE and hardener and stacking both core-filled unidirectional fibers in a 90° direction to obtain sufficient healing. The experimental results demonstrated that the healing efficiency increased by approximately 3%. SEM-EDX analysis further confirmed the healing process by exhibiting spill-out resin and the respective fibers’ major chemical elements at the damaged site after self-healing. The tensile, flexural, and Izod impact strengths of self-healing panels indicated improved strengths of 7.85%, 49.43%, and 53.84%, respectively, compared with fibers with empty lumen-reinforced VE panels due to the presence of a core and interfacial bonding between the reinforcement and matrix. Overall, the study proved that abaca lumens could effectively serve as healing carriers for thermoset resin panels.

Abstract Self-healing on the engineering applications is smart, decisive research for prolonging the life span of the materials and the innovations have been mounting still smarter. Connecting to advancements in self-healing carriers, in altering the chemical structure by optimizing the brittleness for self-healing performance and introducing the bio-degradability, for the first time TPS was blended to PVDF for the synthesis of nanofibers, as carriers of a vinyl ester (VE) resin (medication), by the coaxial electrospinning technique. TPS was mechanically mixed with PVDF base polymer and optimized the TPS content (10 wt%) based on mechanical performance. The novel nanofibers were characterized via field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy, X-ray diffraction, thermal, moisture analysis, and a mechanical line with FESEM and energy-dispersive X-ray analysis studied the self-healing. The TPS/PVDF fibers having hydrogen bonding and increased the crystallinity (40.57 → 44.12%) and the diameter (115 → 184 nm) along with the surface roughness of the fibers with increasing the TPS content. Microanalysis presented the flow-out of the VE resin at the scratched parts in the pierced fibers; interestingly, after some time, the etched part was cured automatically by the curing of the spread resin. Mechanical stretching of the nanofibers in the tensile tests up in the plastic region showed a decrement in the elasticity (TPS/PVDF fibers) and an increment in the brittle nature (cured VE resin) with the increase in Young’s modulus at each stretching, clearly elucidating the healing performance.

The use of halogen-based materials has been regulated since toxic substances are released during combustion. In this study, polyurethane foam was coated with cationic starch (CS) and montmorillonite (MMT) nano-clay using a spray-assisted layer-by-layer (LbL) assembly to develop an eco-friendly, high-performance flame-retardant coating agent. The thickness of the CS/MMT coating layer was confirmed to have increased uniformly as the layers were stacked. Likewise, a cone calorimetry test confirmed that the heat release rate and total heat release of the coated foam decreased by about 1/2, and a flame test showed improved fire retardancy based on the analysis of combustion speed, flame size, and residues of the LbL-coated foam. More importantly, an additional cone calorimeter test was performed after conducting more than 1000 compressions to assess the durability of the flame-retardant coating layer when applied in real life, confirming the durability of the LbL coating by the lasting flame retardancy.

In this report, layer by layer (LBL) fire retardant coatings were produced on wood ply and Polypropylene Homopolymer/Flax fiber composites. FE-SEM and EDAX analysis was carried out to analyze the surface morphology, thickness, growth rate and elemental composition of the samples. Coatings with a high degree of uniformity were formed on Polypropylene composite (PP/flax), while coatings with highest thickness were obtained on wood ply (wood). FTIR and Raman spectroscopy were further used for the molecular identifications of the coatings, which confirmed the maximum deposition of the solution components on the wood substrate. A physiochemical analysis and model was proposed to explain the forces of adhesion between the substrate and solution molecules. Fire protection and thermal properties were studied using TGA and UL-94 tests. It was explored, that the degradation of the coated substrates was highly protected by the coatings as follows: wood > PP/flax > PP. From the UL-94 test, it was further discovered that more than 83% of the coated wood substrate was protected from burning, compared to the 0% of the uncoated substrate. The flammability resistance of the samples was ranked as wood > PP/flax > PP.

This study is on the dynamic analysis of long heavy-duty roller chain drive systems, which is used in a bucket elevator of continuous ship unloader. Currently, most dynamic analysis is focused on part of the driving system, such as a chain tight span. However, it is not enough for the practical application. To simulate the unloading process more precisely, the dynamic response of the roller chain during working was simulated used a geometric modeling with Automatic Dynamic Analysis of Mechanical System. The major objective of this work is to study the factor analysis of long heavy-duty roller chain system for bucket elevator of continuous ship unloader. The aim of this article is to analyze the dynamic behavior of the complex chain system. The results are expressed in terms of parameters such as angular velocity, angular acceleration, contact force, driving sprocket torque, roller chain tension, and displacement. It shows that as a multi-body model, the roller chain system has a complicated dynamic characteristic. The results from the dynamic simulation can help to better the choice of parameters. Maximum contact force and chain tension positions of the chain system model are determined and can be used in the following finite element method analysis. In addition, influence of the speeds on the kinetic parameters is also simulated and critical speed is deducted through the lateral displacement analysis.

The primary objective of this study was to demonstrate the effectiveness of machine learning (ML) in predicting the mechanical behavior of sandwich structures. To achieve this, the study focused on assessing the bending response of environmentally friendly sandwich beams consisting of auxetic cores made from polylactic acid (PLA) and flax/epoxy composite face sheets reinforced with halloysite nanotubes (HNTs). Two ML techniques, specifically shallow neural networks (SNNs) and deep neural networks (DNNs), were employed to predict the specific energy absorption (SEA) and load-deflection curves of these sandwich beams, respectively. The key design parameters under consideration included the HNT content in the face sheets and three geometric characteristics of the auxetic cells. Subsequently, 16 distinct specimens were meticulously designed for manufacturing, following Taguchi's experimental design principles. The cores of the structures were produced using 3D printing techniques, while the face sheets were meticulously fabricated using a hand layup process. These prepared specimens were then subjected to a three-point bending test, and the collected data were employed to train the aforementioned neural networks. The outcomes of this study revealed that an SNN with a single hidden layer comprising seven neurons effectively predicted the SEA of the structures across various design parameter values. Additionally, the remarkable performance of a DNN, consisting of five hidden layers with 128, 64, 32, 16, and 8 units, respectively, was demonstrated by comparing its predicted results with the experimental results for a randomly designed sandwich beam. Highlights • ML techniques were used for bending behavior of sandwich beams. • The beams were made of flax/epoxy/HNT face sheets and 3D-printed auxetic core. • SNN was successfully employed to predict the SEA of the structures. • DNN effectively predicted the load-deflection curves. • HNT content and auxetic cells significantly impacted the bending properties. [ABSTRACT FROM AUTHOR]