PLA-based composites can offer environmental advantages over composites based on conventional petroleum-derived polymers, owing to the renewable origin and potential biodegradability of PLA. The objective of this research is to compare the capability of predictive models such as ANN, RSM, and Bezier curve on the mechanical performance of polylactic acid (PLA) composites reinforced with activated carbon (AC) derived from coconut shell at different filler loadings from 0 to 10 wt.%. The composites were manufactured using melt blending, extrusion, palletizing, and compression moulding. The mechanical characterizations, like tensile behavior, surface hardness, and impact strength, were studied and scanning electron microscopy (SEM) was utilized to study the morphology of the developed composites. The experimental results showed that the tensile and impact strengths of the composites reduced with the increasing AC content. Among the investigated predictive models, the Bezier-based model provided close agreement with the experimental data, with R 2 values of 0.9995, 0.9450, and 0.9657 for tensile strength, hardness, and impact strength. The results show the potential of smooth curve-based modelling for representing nonlinear composition–property relationships in small experimental datasets. The Bezier model is intended for interpolation within the investigated experimental range only and should not be used for extrapolation beyond the available experimental data. The combined experimental and predictive framework provides insight into the mechanical performance of PLA–AC thermoplastic composites and supports the development of data-driven approaches for the design of sustainable particulate-filled polymer composites.
Polyoxymethylene (POM) is increasingly used to replace metals in moving components because of its corrosion resistance and high specific strength; however, its insufficient heat resistance and wear resistance limit its application under demanding service conditions. In this study, bisphenol-A/aniline-based benzoxazine (BZ) was used as a low-loading modifier, and BZ/POM composites were prepared by melt blending (210 °C) and compression molding (220 °C) while exploiting the thermal ring-opening polymerization of BZ within the POM processing range. The effects of BZ content (0–0.5 wt%) on the mechanical, thermal, and tribological properties were investigated. All properties showed non-monotonic behavior: the optimum mechanical performance was obtained at 0.3–0.4 wt% BZ (tensile strength +15.2% and flexural strength +62.7%), the decomposition temperature increased monotonically (temperature at the maximum decomposition rate +19 °C), and the coefficient of friction decreased in a threshold-like manner to a minimum of 0.1192 at 0.4 wt% BZ (a reduction of 29.3%). A BZ content of 0.4 wt% provided the best overall balance among the mechanical, thermal, and tribological properties, offering a new route for modifying thermoplastic engineering plastics through the in-situ polymerization of thermosetting monomers.
Additive manufacturing, specifically fused filament fabrication (FFF), has transformed the manufacture of complex thermoplastic components; nonetheless, weak interlayer bonding and anisotropic behaviour remain major limitations on mechanical performance. Graphene-reinforced polymers and Taguchi-based FFF parameter optimization have each been studied extensively, but their combined effect on ABS composites — specifically how graphene nanoplatelet (GNP) loading interacts with layer thickness, build orientation, infill pattern, and infill density to govern tensile and flexural performance — has not been systematically quantified; this study addresses that gap. Melt blending and single-screw extrusion were used to prepare ABS/GNP composite filaments at 0.5, 1, and 1.5 wt% GNP loadings, and for each composition a Taguchi L27 orthogonal array was used to evaluate four FFF process parameters (layer thickness, build orientation, infill pattern, and infill density). Specimens were tested per ASTM D638 (tensile) and ASTM D790 (flexural). Among the GNP loadings, ABS/0.5 wt% GNP produced the highest mean tensile (21.52 MPa) and flexural (39.42 MPa) strengths, indicating that the lower GNP content achieved better dispersion under the melt-blending conditions used, while the 1 and 1.5 wt% loadings showed evidence of agglomeration-related reductions in strength. Within the ABS/0.5 wt% GNP composition, the parameter combination of 0.19 mm layer thickness, on-long-edge orientation, grid infill pattern, and 90% infill density produced the best confirmed tensile and flexural strengths of 40.1 MPa and 56.9 MPa, respectively, with a prediction error below 3%. A correctly parameterized ANOVA (two degrees of freedom per three-level factor) identified build orientation as the dominant parameter, contributing 81.8% to tensile strength and 50.7% to flexural strength, followed by infill density (10.8%/29.1%) and infill pattern (3.1%/16.7%), while layer thickness was not statistically significant.
Studies show that thermoplastic composites manufactured using in-situ Automated Fiber Placement (AFP) exhibit lower mechanical properties compared to those consolidated in an autoclave, particularly in terms of interlaminar shear strength and flexural properties. This performance gap can be attributed to the short manufacturing time in the AFP process, which restricts the degree of healing that can be achieved. To address this, vibration-assisted thermal bonding (VATB) was previously proposed as an approach to enhance bonding strength. While promising results were observed, optimizing the VATB process requires a mathematical model for prediction of bonding strength during the manufacturing process. To achieve this, in the current study, a new analytical relation has been developed to predict the bonding strength during VATB process. This theoretical model demonstrates the impact of vibratory consolidation forces on the degree of healing. To validate this model, experiments were conducted to examine the effects of frequency-dependent vibratory pressure on consolidation quality, welding time, and the healing evolution of carbon fiber/poly ether ether ketone (CF/PEEK) thermoplastic composites. The results indicate that substituting static pressure with vibratory pressure significantly improves bonding strength, with increases of up to 85% at 355°C and 22% at 375°C, and reduces welding time—the duration required to achieve maximum strength—by approximately 90% at lower temperatures and 50% at higher temperatures. Additionally, the degree of healing, which is dependent on welding time, is influenced by both the frequency and amplitude of the vibration, with higher frequencies accelerating the healing rate.
This study presents a comprehensive multimodal evaluation of flexible Polyvinyl Chloride (PVC) ternary nanocomposites (TNCs) reinforced with Boron Carbide (B4C), Bismuth Oxide (Bi2O3), and a Graphene Oxide (GO) scaffold supported by MXene (Ti3C2Tx) sheets. Fabricated via solution casting, the TNC series (TNC-1 to TNC-4, with up to 25 wt% Bi2O3) aims to resolve the critical trade-offs between high mixed-field radiation attenuation, thermal stability, and mechanical embrittlement typically associated with heavy-element-loaded polymers. Structural and morphological analyses (XRD, SEM-EDX) validated the formation of a stable, two-dimensional GO/MXene network that effectively optimized particle dispersion and prevented Bi2O3 agglomeration even at maximum loading. Robust interfacial chemical bonding between the polymer matrix and the functionalized scaffold, evidenced by significant shifts in FTIR spectra, translated into a 27 degrees C elevation in the initial TGA degradation temperature and an 11.3 degrees C shift in the DSC glass transition temperature, confirming restricted segmental chain dynamics. Most significantly, this engineered interface mitigated high-Z loading embrittlement; while TNC-4 achieved an impressive four-fold reduction in both the Half-Value Layer (HVL) and Tenth-Value Layer (TVL) at 0.662 MeV and systematic suppression of gamma-ray buildup factors (EBF and EABF) via efficient scattering capture in the Compton region, it simultaneously doubled the Young's Modulus and maintained substantial tensile toughness and flexibility. Finally, meticulous MCNP6 version 6.2 simulations and an optimized predictive Machine Learning framework (Random Forest Regression) yielded robust correlation statistical, with R2 values exceeding 0.9985 and a relative deviation below 3% for all targets, establishing this ternary system as a systematic optimization for demanding multifunctional shielding designs in mixed-field environments.
