Volume 45, Issue 8

Fiber reinforced polymer matrix composites (FRPMCs) play an important role in modern engineering due to their strength, light weight, and durability against environmental wear. However, their anisotropic and abrasive nature makes them difficult to machine, often affecting surface finish, dimensional accuracy, and tool life. Their machinability is influenced by factors such as fibre orientation, matrix type, feed rate, cutting speed, and depth of cut. Common issues such as delamination, fibre pull-out, and heat damage can reduce part performance. This review outlines both conventional (drilling, milling, turning) and non-conventional (laser machining, abrasive water jet cutting) methods for machining FRPMCs. It highlights the role of tool materials like polycrystalline diamond (PCD) and coated carbide in reducing wear and improving results. Proposed review also offers a clear comparison of machining techniques, explores recent advances in predictive modelling and machine learning, and identifies key factors influencing the machinability of FRPMCs for more reliable and economical manufacturing.

While biodegradable polylactic acid (PLA) holds promise for medical applications, its inherent brittleness and poor processability limit practical use. Through molecular-level interfacial engineering, we developed electrospun PLA/polyvinyl pyrrolidone (PVP) nanofibers with tunable mechano-rheological properties. The addition of PVP significantly enhanced solution conductivity (354 % increase to 4.50 μs/cm) and reduced thermal degradation activation energy by 40 % (167 kJ/mol). At 50 % PVP content, the nanofibers exhibited optimal mechanical performance, with tensile strength increasing by 66.7 % to 3.0 MPa and fracture energy by 13.7 %–69.8 MJ/m 2 . Morphological control was further achieved at 20 % PVP, minimizing porosity to 23.4 % for potential barrier applications. Meanwhile, the tunable water resistance (92–68 %) expanded the potential applications. These results demonstrated that PLA/PVP nanofibers synergistically combined improved flexibility and processability without compromising biodegradability, offering a versatile platform for medical materials.

This research presents machine learning approaches utilizing decision tree, K-nearest neighbors (KNN), ensemble learning, and adaptive boosting techniques to precisely estimate the density of PEG saturated with CO 2 based on PEG molecular weight, pressure, temperature, and carbon dioxide weighting factor. An experimental dataset is leveraged to train the predictive models. Additionally, leverage approach is applied to validate data reliability, while the relevancy index is employed to evaluate the impact of every input parameter on density. The findings suggest that the lab data are well-suited for constructing data-driven models. The findings confirmed the dataset’s validity for predictive modeling, revealing that pressure has the most substantial influence on density, followed by molecular weight, temperature, and the carbon dioxide weighting factor. Correlation analysis supported this pattern, with relevance factor values of 0.53 for pressure, 0.3 for molecular weight, 0.24 for temperature, and 0.24 for the carbon dioxide weighting factor. Statistical analysis and graphical evaluations demonstrated that the decision tree model delivered the highest accuracy in predicting the density of carbon dioxide-saturated polyethylene glycol, while other models such as KNN showed comparatively lower performance. The results reaffirmed the significance of all input parameters, with SHAP analysis highlighting pressure as the most dominant factor influencing density.

Breast cancer is widely recognized as the primary cause of cancer globally, necessitating innovative therapeutic strategies to improve patient outcomes. Nanotechnology has become a feasible method for treating cancer, offering targeted drug delivery and enhanced therapeutic effectiveness. In the present study, quinine-loaded pH-responsive PEGylated polymeric nanoparticles (Q-PEG-PNPs) have been synthesized using a high-pressure homogenization technique to target breast cancer. Q-PEG-PNPs were subjected to optimization, in-vitro drug release profile, antioxidant assay, anti-inflammatory assay, and brine shrimp lethality assay. The particle size and zeta potential of the optimized Q-PEG-PNPs were determined to be 112 ± 1.1 nm and −23.3 mV, respectively. The in-vitro drug release profile results demonstrated the efficacy of Q-PEG-PNPs in releasing quinine under acidic conditions. Specifically, the drug release rates were 84.35 ± 2.2 % and 32.89 ± 1.6 % at pH 5.5 and pH 7.4 after 48 h, respectively. Anti-angiogenesis effect of Q-PEG-PNPs showed thinning of the blood vessels. Brine shrimp lethality assay revealed the LC 50 concentration of Q-PEG-PNPs (60.86 μg/mL). Therefore, the pH-responsiveness and anti-angiogenic effect demonstrate the potential of Q-PEG-PNPs as a viable approach for breast cancer treatment.

This study presents the development and performance evaluation of an innovative intelligent isolation bearing integrating dual magnetorheological shear stiffening units: a magnetorheological shear stiffening gel (MRSSG) and a magnetorheological shear stiffening elastomer (MRSSE). The system’s dynamic shear behavior was systematically investigated under small (5 mm) and large (15–25 mm) displacement regimes. Initially, isotropic MRSSG and anisotropic MRSSE were synthesized and characterized through rheological measurements, demonstrating that their storage modulus and damping factor exhibit autonomous frequency-dependent responses while being magnetically tunable. The bearing architecture employs a hierarchical design: the upper MRSSG unit operates through shear deformation to deliver stiffness and damping enhancement under small displacements, whereas the lower laminated MRSSE unit activates under large displacements to maintain structural stability. Dynamic testing revealed three critical control parameters governing shear performance: excitation frequency, applied current, and axial load. Notably, under a 15 mm displacement and 12.0 MPa axial load, the bearing achieved a maximum shear force output of 247.7 kN. Quantitative performance characterization was conducted through three key metrics: equivalent stiffness ( K h ), post-yield stiffness ( K d ), and equivalent damping ratio ( h eq ). While K h and K d showed positive correlations with frequency, current, and axial load, h eq demonstrated displacement-dependency, with optimal energy dissipation observed at smaller displacements.

Dielectric elastomers are a class of electroactive polymers widely used in energy storage, flexible electronics, and soft robotics. These materials require a high dielectric constant and low elastic modulus to achieve optimal performance. In this study, we developed all-organic dielectric composite films composed of polyaniline (PANI), expandable microspheres, and polydimethylsiloxane (PDMS) under the assistance of an external electric field. The oriented PANI particles self-assemble into a conductive network within the PDMS matrix, enhancing the dielectric constant, while the expandable microspheres lower dielectric loss and reduce modulus. At a PANI content of 2.5 wt% and 100 Hz, the composite film achieved a dielectric constant of 30, dielectric loss below 1, and excellent insulation properties. Additionally, the modulus was reduced to 1.0 MPa, and the strain reached 0.66 % under 10 kV/mm, demonstrating significant electromechanical deformation. This study highlights a new strategy to enhance dielectric elastomer performance under low electric fields, offering insights into the design of next-generation soft actuators and energy-efficient electronic devices.

For PC/ABS plastic alloy injection-molded products, warping and volumetric shrinkage are two prevalent quality defects attributed to the injection molding process. To mitigate these defects to the greatest extent, this study introduces an optimized injection molding process parameter scheme based on the water cycle algorithm enhanced kernel extreme learning machine (WCA-KELM), the multi-objective seagull optimization algorithm (MOSOA), and grey relational evaluation. Firstly, all experimental data were derived from orthogonal experimental designs conducted via Moldflow software, focusing on seven factors, such as melt temperature, mold temperature, packing pressure, and packing time. Warpage and volume shrinkage results were obtained in each set of experiments. Secondly, the predictive accuracy of the unoptimized kernel extreme learning machine (KELM) model was compared against that of the WCA-KELM model within this optimization framework, demonstrating that the WCA-KELM model predicts defect values with enhanced precision. Finally, the MOSOA was utilized to identify the pareto optimal frontier, from which the optimal solution was pinpointed through grey relational evaluation. According to the results, the warpage and volume shrinkage are 0.9388 mm and 8.538 %, respectively, which are effectively reduced by 41.73 % and 18.37 % compared with before optimization, indicating the efficacy and reliability of the proposed optimization methodology.