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CONTENTS
Volume 10, Number 1, January 2025
 

Abstract
Functionally graded porous beams (FGPB) are structural components engineered to enhance mechanical performance by customized material gradation and porosity distribution. The present study examines the buckling analysis of FGPB modelled using Higher-order shear deformation theory. The governing equations are formulated via Hamilton's principle and solved utilizing Karush-Kuhn-Tucker conditions. The analysis utilizes gradient indices (P_x, P_z), porosity distributions (even and uneven) and porosity indices to assess their influence on the dimensionless critical buckling loads under various boundary conditions, including Simply Supported (SS), Clamped-Simply supported (CS), Clamped-Clamped (CC), and Clamped-Free (CF). In line with this, the results show that an increase in P_x led to a decrease in the buckling load from 51.342 when P_x=0 to 8.811 when P_x=5 under the SS boundary conditions. Likewise, with increase in P_z the buckling load was reduced from 51.342 to 13.351. Uneven porosity consistently exhibited higher dimensionless critical buckling as compared to even porosity. Under CC boundary conditions, the dimensionless critical buckling load was 151.970 and 196.587 for even and uneven porosity distribution at P_x=0 and P_z=0. Among the boundary conditions, CC demonstrated the highest stability, with a dimensionless critical buckling load of 151.970, succeeded by CS (101.656), SS (51.342), and CF (13.175). These results prove the ability of the outlined methodology with errors less than 5% compared to literature. This study emphasizes the significance of material gradation and porosity in structural stability and presents a comprehensive method for designing innovative lightweight structures. Future studies may consider Machine Learning based predictive modeling for complex geometries.

Key Words
aspect ratio; dimensionless critical buckling; functionally graded porous beam; gradient index; higher order shear deformation theory; Karush-Kuhn-Tucker conditions; porosity

Address
Geetha Narayanan Kannaiyan: Department of Mathematics, Dayananda Sagar College of Engineering, Bengaluru 560078, India

Vivekanandam Balasubramaniam: Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia

Bridjesh Pappula and Seshibe Makgato: Department of Chemical & Materials Engineering, College of Science, Engineering and Technology, University of South Africa (UNISA), c/o Christiaan de Wet & Pioneer Avenue, Florida Campus 1710, Johannesburg, South Africa

Abstract
This study introduces a new framework that utilizes artificial neural networks (ANN) to analyze data and forecast the tribological properties of Al8090/TiB2/C composites. For training a multi-layered artificial neural network (ANN), a total of 1920 input datasets are used. These datasets are created by combining six input parameters, including the volume of Al8090 matrix, Titanium diboride, and Graphene, as well as the load, sliding speed, and sliding distance. The corresponding output consists of specific wear rate and coefficient of friction. A surrogate model for predicting the tribological properties has been developed by optimizing the hyperparameters to enhance the accuracy of the model's predictions. The results of the ANN-based approach validate that the proposed model has a mean absolute percentage error of 3.42% for the predictions of specific wear rate in dry sliding wear test scenarios, and a MAPE of 0.28% for the predictions of coefficient of friction.

Key Words
aluminium; artificial neural network; hyperparameters; graphene; tribology

Address
Mohamed Zakaulla: Department of Mechanical Engineering, H.K.B.K College of Engineering, Bangalore 560045, India/ Visvesvaraya Technological University, Belagavi, Karnataka, India


Abstract
This study presents a computational analysis of molecular dynamics results within a fuzzy scaled system framework. Molecular dynamics simulations are widely used to investigate the behavior of materials at the atomic level, providing insights into their structural and dynamic properties. However, the inherent uncertainties and complexities in molecular interactions often challenge traditional analytical approaches. By applying fuzzy logic to the analysis of molecular dynamics results, we can effectively capture the variability in atomic interactions and enhance the interpretation of simulation outcomes. This approach enables the development of a more nuanced understanding of material behaviors under various conditions, accounting for factors such as temperature fluctuations and external stresses. The fuzzy scaled system allows for the integration of qualitative and quantitative data, facilitating a comprehensive assessment of molecular dynamics results. Through detailed computational experiments, we demonstrate the efficacy of the fuzzy scaled system in analyzing molecular dynamics data, highlighting its ability to improve prediction accuracy and provide meaningful insights into material properties. The findings underscore the potential of combining fuzzy logic with molecular dynamics simulations to advance the field of computational materials science. This research contributes to the development of more robust analytical tools for interpreting complex molecular behaviors, ultimately paving the way for innovations in material design and engineering applications.

Key Words
Fuzzy grey GM(1,1) model; nanocomposite; nonlocal elasticity; size-dependent properties; stability

Address
C.C. Hung: School of Big Data, Fuzhou University of International Studies and Trade, No. 28, Yuhuan Road, Shouzhan New District, Changle District, Fuzhou City, Fujian Province, PR China

T. Nguyễn: Ha Tinh University, Dai Nai Ward, Ha Tinh City, Vietnam

C.Y. Hsieh: National Pingtung University Education School, No.4-18, Minsheng Rd., Pingtung City, Pingtung County 900391, Taiwan


Abstract
This research paper presents design analysis of a microgripper made up of Thermo-plastic Polyurethane (TPU) material with smart actuation by Ionic Polymer Metal Composite (IPMC) for Micromanipulations. The microgripper has been designed by Pro Release 5.0 software using TPU as the base material which is of very flexible, light weight and low cost. IPMC has been used as the smart actuator which is an Electro active polymer (EAP) operated with low voltage application (1–5 V) for typical sample of dimension 40 mm X 10 mm X 0.2 mm. The gripper is able to provide gripping at its jaw due to deformation of the actuator inserted within it. Displacement analysis has been done using Finite element method (FEM) in ANSYS where the maximum displacement of the gripper jaw has been recorded to obtain the in-put-output characteristics of the structure which is found to be linear in the range of interest. The microgripper system modeling has been done using system identification toolbox in MATLAB where the estimated transfer function model is validated using MATLAB Simulink.

Key Words
displacement analysis; Ionic Polymer Metal Composite (IPMC); microgripper; pro release 5.0; simulink; smart actuation; system identification

Address
Neeta Sahay: Department of Electrical and Electronics Engineering Institute of Engineering & Management, Kolkata, Sector V, Saltlake City, Kolkata-700091, West Bengal, India

Subrata Chattopadhyay: Department of Electrical Engineering National Institute of Technical Teachers' Training and Research, Kolkata, Block FC, Saltlake City, Kolkata-700106, West Bengal, India

Tamonash Jana: Department of Mechanical Engineering Institute of Engineering & Management, Kolkata, Sector V, Saltlake City, Kolkata-700091, West Bengal, India


Abstract
We introduce HybridRecSys, a hybrid recommendation system that integrates collaborative filtering (CF) using enhanced Singular Value Decomposition (SVD) and advanced content-based (CB) filtering techniques enriched by Natural Language Processing (NLP). The proposed system addresses critical challenges such as sparsity and cold start by leveraging a dual approach: explicit ratings for strong user profiling and implicit preferences derived from content and genre analysis. Novel contributions include the application of weighted cosine similarity alongside RBF and cosine similarity, significantly improving similarity metrics. Experimental validation on IMDb and Netflix datasets demonstrates superior performance, with HybridRecSys achieving RMSE and MAE scores of 0.6991 and 0.6987 on IMDb, and 0.2364 and 0.2357 on Netflix, respectively. The system outperforms existing methods by efficiently addressing sparsity and cold start challenges, ensuring highly personalized and accurate recommendations.

Key Words
collaborative filtering; content-based filtering; enhanced SVD; HybridRecSys; temporal dynamics

Address
Kulvinder Singh, Sanjeev Dhawan and Manoj Yadav: Department of Computer Science & Engineering, University Institute of Engineering & Technology (U.I.E.T), Kurukshetra University, Kurukshetra, Haryana, India

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