Xianrui Lyu, Xiaodan Ren^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074388 - 29 January 2026

Abstract Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational… More >

Yewon Choi^1,2, Song Hyeon Ju², Jungsoo Nam^2,*, Min Ku Kim^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071817 - 29 January 2026

Abstract The composite material layering process has attracted considerable attention due to its production advantages, including high scalability and compatibility with a wide range of raw materials. However, changes in process conditions can lead to degradation in layer quality and non-uniformity, highlighting the need for real-time monitoring to improve overall quality and efficiency. In this study, an AI-based monitoring system was developed to evaluate layer width and assess quality in real time. Three deep learning models Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once version 8 (YOLOv8), and Single Shot MultiBox Detector (SSD) were… More >

Francisco Daniel García^1,2, Solange Nicole Aigner^1,2, Natalia Raffaeli³, Antonio José Barotto³, Eleana Spavento³, Mariano Martín Escobar^1,4, Marcela Angela Mansilla^1,4, Alejandro Bacigalupe^1,4,*

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0181 - 23 January 2026

Abstract This study explores the use of black soldier fly larvae protein as a bio-based adhesive to produce particleboards from sugarcane bagasse. A comprehensive evaluation was conducted, including rheological characterization of the adhesive and physical–mechanical testing of the panels according to European standards. The black soldier fly larvae-based adhesive exhibited gel-like viscoelastic behavior, rapid partial structural recovery after shear, and favorable application properties. Particleboards manufactured with this adhesive and sugarcane bagasse achieved promising mechanical performance, with modulus of rupture and modulus of elasticity values of 30.2 and 3500 MPa, respectively. Internal bond strength exceeded 0.4 MPa,… More > Graphic Abstract

Herlina Marta¹, Novita Indrianti^2,*, Allifiyah Josi Nur Aziza³, Enny Sholichah⁴, Titik Budiati³, Achmat Sarifudin⁵, Yana Cahyana¹, Nandi Sukri¹, Aldila Din Pangawikan¹

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0145 - 23 January 2026

Abstract Corn starch (CS) is a renewable, biodegradable polysaccharide valued for its film-forming ability, yet native CS films exhibit low mechanical strength, high water sensitivity, and limited thermal stability. This study improves CS-based films by blending with poly(vinyl alcohol) (PVA) or glycerol (GLY) and using citric acid (CA) as a green, non-toxic cross-linker. Composite films were prepared by casting CS–PVA or CS–GLY with CA at 0%–0.20% (w/w of starch). The influence of CA on physicochemical, mechanical, optical, thermal, and water barrier properties was evaluated. CA crosslinking markedly enhanced the tensile strength, water resistance, and thermal stability More > Graphic Abstract

Raviduth Ramful^*

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0116 - 23 January 2026

Abstract Typically used thermal insulation materials such as foam insulation and fibreglass may pose notable health risks and environmental impacts thereby resulting in respiratory irritation and waste disposal issues, respectively. While these materials are affordable and display good thermal insulation, their unsustainable traits pertaining to an intensive manufacturing process and poor disposability are major concerns. Alternative insulation materials with enhanced sustainable characteristics are therefore being explored, and one type of material which has gained notable attention owing to its low carbon footprint and low thermal conductivity is natural fibre. Among the few review studies conducted on… More > Graphic Abstract

Girish Hariharan¹, Vinyas¹, Gowrishankar Mandya Chennegowda¹, Nitesh Kumar¹, Shiva Kumar¹, Deepak Doreswamy², Subraya Krishna Bhat^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073772 - 12 January 2026

Abstract This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite–epoxy (G–E) composites reinforced with cast iron (CI) filler particles. Hybrid G–E composite with added cast iron (CI) filler particles enhances stiffness, strength, and vibration damping, offering enhanced performance for vibration-sensitive engineering applications. Unlike conventional approaches, this work simultaneously employs Artificial Neural Networks (ANN) for high-accuracy property prediction and Response Surface Methodology (RSM) for in-depth analysis of factor interactions and optimization. A total of 24 experimental test data sets of varying input… More >

Tahir Mahmood^1,*, Muhammad Waseem Ashraf^1,*, Shahzadi Tayyaba², Muhammad Munir³, Babiker M. A. Abdel-Banat³, Hassan Ali Dinar³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072436 - 12 January 2026

Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >

Junwen Huo, Haicheng Liang, Weiye Dong, Xiaoming Du^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.073285 - 09 December 2025

Abstract With the rapid advancement of electromagnetic launch technology, enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railgun systems. Traditional aluminum alloy armatures often suffer from severe ablation, deformation, and uneven current distribution under high pulsed currents, which limit their performance and service life. To address these challenges, this study employs the Johnson–Cook constitutive model and the finite element method to develop armature models of aluminum matrix composites with varying heterogeneous graphene volume fractions. The temperature, stress, and strain of the armatures during operation… More >

Bindi Saurabh Thakkar¹, Pradeep Kumar Karsh^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.072839 - 09 December 2025

Abstract This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies. Hybrid composites, widely used in aerospace, automotive, and structural applications, often face variability in material properties, geometric configurations, and manufacturing processes, leading to uncertainty in their dynamic response. To address this, three surrogate-based machine learning approaches like radial basis function (RBF), multivariate adaptive regression splines (MARS), and polynomial neural networks (PNN) are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates. The research focuses on predicting the first three natural frequencies… More >

Thierry Mugenzi, Cahit Perkgoz^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.070381 - 10 November 2025

Abstract Missing data presents a crucial challenge in data analysis, especially in high-dimensional datasets, where missing data often leads to biased conclusions and degraded model performance. In this study, we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision. The proposed loss combines (i) a guided, masked mean squared error focusing on missing entries; (ii) a noise-aware regularization term to improve resilience against data corruption; and (iii) a variance penalty to encourage expressive yet stable reconstructions. We evaluate the proposed model across four missingness mechanisms, such as Missing… More >