Articles in Press
Articles in Press have passed peer review and been accepted for publication. They are being copyedited and typeset, and will appear in a forthcoming issue, where the full text, page numbers and final citation become available. Their details may still change slightly before publication.
5 accepted article(s) awaiting publication
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In Press Open Access Research Articles
Influence of Reinforcement Characteristics and Ultrasonic-Assisted Stir-Casting Conditions on AA6061/Al₂O₃/SiC Tensile Strength: Machine Learning Modeling
International Journal of Materials Innovation and Engineering · Accepted 2026-10-22 · Scheduled for Vol. 1 No. 1 (2026)Abstract
The integration of machine learning (ML) with aluminium matrix composite manufacturing offers an effective approach for understanding the relationships between reinforcement characteristics, processing conditions, and mechanical performance. This study investigates the prediction of ultimate tensile strength (UTS) in AA6061 aluminium matrix nanocomposites containing Al₂O₃ and SiC nanoparticles using Gaussian Process Regression (GPR) and Least-Squares Boosting (LSBoost). A dataset comprising 240 observations was analysed using fourteen variables describing alloy composition, reinforcement concentration, particle dimensions, and ultrasonic-assisted stir-casting conditions. Eight reinforcement and processing variables were selected for predictive modelling, with 80% of the observations allocated to training and 20% to testing. Spearman correlation analysis identified SiC concentration (rₛ = 0.66) and Al₂O₃ concentration (rₛ = 0.47) as the variables most strongly associated with UTS. The predictive accuracy of both algorithms was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). GPR demonstrated superior predictive performance, achieving a testing R² of 0.9657, RMSE of approximately 2.85 MPa, and MAE of 2.28 MPa, compared with 0.9391, 3.80 MPa, and 3.12 MPa, respectively, for LSBoost. Residual analysis further indicated improved prediction consistency for GPR. LSBoost predictor-importance analysis highlighted the predominant influence of SiC and Al₂O₃ concentrations on the fitted tensile-strength response. These findings demonstrate the potential of GPR for modelling nonlinear composition–processing–property relationships and provide a computational foundation for further experimental investigation of ceramic-reinforced AA6061 composites.
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In Press Open Access Research Articles
Integrated Response Surface Methodology and Gaussian Process Regression for Multi-Objective Particle Swarm Optimization of Tribological Performance in AZ91/ZrO₂ Composites
International Journal of Materials Innovation and Engineering · Accepted 2026-10-22 · Scheduled for Vol. 1 No. 1 (2026)Abstract
The tribological performance of magnesium-based metal matrix composites under elevated-temperature sliding conditions is governed by complex interactions among operating parameters and ceramic reinforcement. This investigation examines the dry sliding wear and friction characteristics of AZ91 magnesium alloy reinforced with ZrO₂ particles using Box–Behnken design (BBD), response surface methodology (RSM), Gaussian process regression (GPR), and multi-objective particle swarm optimization (MOPSO). A 29-run BBD framework incorporated four independent variables: temperature (60–200 °C), sliding speed (0.75–1.95 m/s), normal load (15–55 N), and ZrO₂ concentration (1.5–7.5 wt.%). Specific wear rate (SWR) and coefficient of friction (COF) were selected as performance responses. Quadratic RSM models achieved determination coefficients of 0.9985 and 0.9984 for SWR and COF, respectively, while five-fold cross-validation yielded R² values of 0.9913 and 0.9882. Corresponding GPR models attained cross-validated R² values of 0.9863 and 0.9868, demonstrating close predictive agreement. Multi-objective optimization identified a compromise condition comprising 60°C, 1.9499 m/s sliding speed, 15.00 N normal load, and 4.6901 wt.% ZrO₂, with predicted SWR and COF values of 0.21003 × 10⁻³ mm³/(N·m) and 0.29220, respectively. Cross-model verification revealed prediction differences below 1% for both responses at the selected operating conditions. The findings demonstrate the applicability of integrated statistical modelling, machine learning, and Pareto-based optimization for examining wear–friction relationships and identifying favourable parameter combinations within the investigated design domain.
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In Press Open Access Research Articles
Influence of Friction Stir Welding Parameters and TiB₂ Content on Tensile Properties and Weld Nugget Hardness of AA7075 Composites: Taguchi L27 Optimization
International Journal of Materials Innovation and Engineering · Accepted 2026-10-22 · Scheduled for Vol. 1 No. 1 (2026)Abstract
This study investigates the addition of friction stir welding (FSW) process parameters on mechanical characteristics of AA7075 aluminium matrix composites reinforced by titanium diboride (TiB₂) particles. AA7075/TiB₂ composites with 3, 6, and 9 wt.% reinforcement was fabricated by stir-casting technique and subsequently joined through FSW. A Taguchi L27 orthogonal array was employed to evaluate four independent factors: welding speed (30–70 mm/min), tool rotational speed (900–1300 rpm), TiB₂ reinforcement content (3–9 wt.%) and axial load (3–7 kN). Percentage elongation (PE), Ultimate tensile strength (UTS) and weld nugget hardness (WNH) were considered as the mechanical responses. Taguchi signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) were performed to determine parameter significance and identify the preferred welding conditions. ANOVA showed tool rotational speed were dominant factor affecting UTS (60.87%) and PE (43.27%), whereas TiB₂ content contributed most significantly to WNH (73.83%). The fitted models achieved coefficients of determination (R²) of 98.26%, 96.61%, and 97.97% for UTS, PE, and WNH, respectively. The preferred conditions for maximizing UTS were 1100 rpm, 50 mm/min, 5 kN, and 6 wt.% TiB₂, yielding a predicted strength of 497.38 MPa. Maximum predicted PE (8.21%) was obtained at 1100 rpm, 50 mm/min, 5 kN, and 3 wt.% TiB₂, while the highest predicted WNH (193.03 HV) corresponded to 1100 rpm, 50 mm/min, 7 kN, and 9 wt.% TiB₂. The findings demonstrate that the preferred axial load and reinforcement concentration depend on the mechanical response, highlighting the effectiveness of Taguchi-based optimization for selecting FSW conditions in AA7075/TiB₂ composites.
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In Press Open Access Research Articles
Experimental Evaluation and RBFNN Prediction of Mechanical Properties in Madar Fiber–Groundnut Shell Powder Reinforced Epoxy Composites
International Journal of Materials Innovation and Engineering · Accepted 2026-10-22 · Scheduled for Vol. 1 No. 1 (2026)Abstract
This study investigates the mechanical response of hybrid composites fabricated from Madar fiber, groundnut shell powder, and epoxy resin. The reinforcement and particulate filler were incorporated into the epoxy matrix to develop composites with improved load-bearing characteristics. Mechanical performance was examined through tensile, flexural, impact, and interlaminar shear strength tests, while scanning electron microscopy (SEM) were used to assess fracture morphology and interfacial characteristics. A radial basis function neural network (RBFNN) were additionally developed to estimate tensile and flexural strengths from the material composition. The network employed radial basis neurons to capture nonlinear relationships between the input variables and mechanical responses. Among the investigated compositions, MGE-2 containing 20 wt.% Madar fiber, 15 wt.% groundnut shell powder, and 65 wt.% epoxy resin exhibited the most favorable performance. MGE-2 attained tensile strength of 61.78 MPa and tensile modulus of 3.26 GPa, together with a flexural strength of 94.36 MPa and flexural modulus of 4.78 GPa. Corresponding impact strength and interlaminar shear strength reached 20.68 kJ/m² and 21.37 MPa, respectively. The RBFNN provided reliable predictions, yielding test R² values of 0.9204 and 0.9447 for tensile and flexural strengths, respectively.
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In Press Open Access Research Articles
Machine Learning- and SHAP Assisted Experimental Investigation of Annona reticulata Bark Fibre–Prosopis juliflora Wood–Jamun Seed Powder Hybrid Epoxy Biocomposites
International Journal of Materials Innovation and Engineering · Accepted 2026-10-22 · Scheduled for Vol. 1 No. 1 (2026)Abstract
An integrated experimental and computational framework was developed to investigate epoxy hybrid biocomposites reinforced with Annona reticulata bark fibre, Prosopis juliflora wood powder and Jamun seed powder. Six formulations, APJ1–APJ6 were fabricated by hand lay-up and evaluated for flexural, tensile, Shore-D hardness, impact and moisture-absorption behaviour. Thermal stability, viscoelastic response, functional groups, crystalline characteristics and fracture morphology were examined using TGA, DMA, FTIR, XRD and SEM, respectively. Among the investigated formulations, APJ5 exhibited the highest tensile and flexural strengths of 267.4 and 341.2 MPa, respectively, whereas APJ6 achieved the highest Shore-D hardness of 85.2, demonstrating property-dependent optimum composition. Machine-learning prediction was performed using ANN, RF, GBR and KRR under five-fold cross-validation. ANN exhibited the best overall predictive performance, achieving R² values of 0.99735, 0.99767, 0.99806 and 0.99573 for tensile strength, flexural strength, impact strength and Shore-D hardness, respectively. SHAP analysis was subsequently employed to interpret the selected ANN model. Jamun seed powder and Prosopis juliflora wood powder exhibited equal normalized predictive contributions of 50% each, reflecting their complementary compositional variation rather than independent causal effects. The combined experimental, ML and SHAP framework provides a systematic approach for evaluating composition-dependent performance and identifying promising hybrid epoxy biocomposites for lightweight engineering applications.