Rajadhani Institute of Engineering and Technology

PurposeThis paper aims to optimize the process parameters of fused filament fabrication (FFF) to promote the sustainable manufacturing of the components.Design/methodology/approachIn this paper, six key process parameters, such as extrusion temperature, print speed, infill density, infill pattern, layer thickness and build orientation, are identified as influential factors in the mechanical performance and sustainability matrices of the polyethylene terephthalate glycol (PETG) printed parts. Taguchi's design of experiments was used to assess the impact of each parameter on six critical response variables: tensile strength, flexural strength, surface roughness, time to print, energy and material consumption. Statistical methods such as analysis of means and analysis of variance were performed to systematically evaluate the influence of process parameters of FFF on responses. Furthermore, a new matrix-based method is used to optimize all six responses simultaneously.FindingsThe major findings include that layer thickness and infill density are the two major parameters that influence the responses considered in the study, followed by print speed, infill pattern, build orientation and extrusion temperature. The optimum parameters obtained by the matrix method are validated by experiment, and the results are promising, demonstrating a significant improvement in energy efficiency and material performance, aligning with sustainable manufacturing goals.Originality/valueUsing a matrix-based multi-response optimization method, mechanical characteristics and energy-efficient PETG components can be printed, leading to sustainable manufacturing.

Nickel oxide (NiO) nanostructures were synthesized by thermally oxidizing thin nickel (Ni) films deposited on glass substrates via electron beam evaporation under a controlled vacuum environment. Thermal oxidation was performed at 400 °C, 500 °C, and 600 °C in ambient air. X-ray diffraction (XRD) confirmed mixed phases of Ni and NiO at 400 °C and 500 °C, whereas a single-phase NiO with improved crystallinity was obtained at 600 °C. XRD data were analyzed using Rietveld refinement to confirm phase composition and structural evolution. Field emission scanning electron microscopy (FESEM) revealed that higher oxidation temperatures promote grain growth and induce a porous morphology. Energy-dispersive X-ray spectroscopy (EDX) confirmed the chemical purity of the oxidized films, showing only nickel and oxygen. Optical absorption analysis demonstrated a redshift in the bandgap with increasing temperature, consistent with defect-mediated electronic transitions. X-ray photoelectron spectroscopy (XPS) of the Ni 2p core level exhibited characteristic Ni 2p₃/₂ and Ni 2p₁/₂ peaks along with satellite features, evidencing the coexistence of Ni2+ and Ni3+ oxidation states. Nonlinear optical behavior was probed using the Z-scan technique at 532 nm, yielding third order nonlinear susceptibility (χ3) values in the range of 3.64 × 10–4–1.67 × 10–4 esu. The films displayed strong nonlinear absorption and thermal lensing effects, with an optical limiting threshold as low as 0.26 kJ/cm2. These findings highlight the potential of NiO thin films as efficient candidates for nonlinear optical limiting applications, where structural tuning via oxidation temperature plays a critical role in enhancing performance.

The precise forecasting of thermal conductivity in nanofluids is essential for enhancement of thermal management systems within industrial contexts. This research establishes a robust framework that combines machine learning (ML), deep learning (DL), advanced augmentation, and optimization strategies to predict two types of thermal conductivity: experimental thermal conductivity (Exp-TC) and the effective thermal conductivity based on the Koo-Kleinstreuer-Li model (KKL-TC). The study curates a comprehensive dataset consisting of 278 samples, which includes a variety of nanoparticle materials (such as Cu, CuO, Al, and Al2O3) and base fluids (including water, ethylene glycol, and transformer oil). Critical parameters, including particle size, temperature, volume fraction, thermal conductivity of particle and liquid medium, were enhanced through advanced data augmentation techniques-specifically, polynomial and fourier expansion inspired augmentation and conditional variational autoencoders to improve data diversity and the robustness of the models. The approach utilized fourteen ML and DL models in two configurations: standalone and ensemble stacking with linear regression. Optimization techniques, such as grey wolf optimization and particle swarm optimization, were employed to refine hyperparameters and enhance predictive accuracy. The results reveal that both augmentation and optimization significantly boost predictive accuracy. Specifically, CatBoost delivers the best Exp-TC (R2 = 0.99964, RMSE = 0.00464) and KKL-TC performance (R2 = 0.99782, RMSE = 0.00391). These insights establish a highly scalable and adaptable framework that extends beyond thermal system design to a broad range of engineering and industrial applications. This framework drives innovation in energy management, facilitating the development of more efficient, sustainable, and intelligent thermal systems.

In modern pharmaceutical information systems, ensuring secure access to sensitive drug-related data and digital healthcare platforms has become increasingly important due to the growing threat of automated bot attacks and unauthorized access. Traditional CAPTCHA mechanisms provide a basic level of protection; however, they often compromise usability and accessibility, particularly for users accessing pharmaceutical databases and healthcare portals. This study proposes an intelligent adaptive CAPTCHA mechanism that integrates Optical Character Recognition (OCR) and Convolutional Neural Networks (CNNs) to enhance authentication and security in pharmaceutical information systems. The proposed system employs machine learning techniques and real-time behavioral analysis to dynamically adjust CAPTCHA difficulty based on user interaction patterns and response times. By continuously monitoring user behavior, the system intelligently generates personalized challenges that improve usability for legitimate users while effectively identifying automated bots. The framework incorporates multimodal CAPTCHA formats, including text and image-based challenges, improving accessibility and adaptability across diverse user groups. Additionally, the adaptive design increases resistance to automated bot training and improves overall system robustness. Experimental results demonstrate that the proposed approach achieves higher detection accuracy, reduced false-positive rates, and improved user experience compared to traditional CAPTCHA systems. The intelligent adaptive CAPTCHA mechanism provides a secure and user-friendly solution for protecting pharmaceutical data platforms, drug information systems, and digital healthcare applications from malicious automated access

This comprehensive study focuses on Gd3+ substituted Mn-Cd nanoferrites, represented by the chemical formula Mn0.3Cd0.7GdxFe2-xO4 (MCGF). The dopant ion used was Gd3+, with x values ranging from 0.000 to 0.025. We prepared the samples for this research and conducted an extensive analysis using XRD, FTIR, TEM, SEM, and SAED techniques. The XRD patterns confirmed the presence of a spinel structure. Notably, the average crystallite size determined from the XRD analysis was between 32 and 36 nm, which aligns with the results from the TEM analysis. Additionally, the lattice parameters increased with the substitution of the metal ion (Gd3+), clearly demonstrating the effect of doping on the structure of the material. The examination of the dielectric properties provided crucial insights: both the dielectric constant (ε’) and dielectric loss (ε") increased with temperature. This is linked to the enhanced sample polarization and suggests that higher temperatures promote dipole relaxation, resulting in an increased dielectric loss. Samples exhibiting significant dielectric losses are particularly valuable for electromagnetic interference shielding applications. We also explored the AC conductivity of the samples and analyzed the relationship between the conductivity and frequency for various doping levels. This investigation offers insights into the behavior of materials at different doping levels and temperatures. The discussion on how the AC conductivity increases with temperature and frequency is particularly intriguing, given the material behavior. Furthermore, we examined the frequency dependency of the real part Z’ and imaginary part Z’’ of the impedance and provided detailed plots. The Nyquist plots and observed relaxation phenomena are crucial for understanding the electrical characteristics of materials. Gas detectors, storage devices, and other devices that can be developed from these synthesized materials are important for modern technologies.