Fracture and Structural Integrity: The Podcast Titelbild

Fracture and Structural Integrity: The Podcast

Fracture and Structural Integrity: The Podcast

Von: Gruppo Italiano Frattura (IGF)
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 Stay at the cutting edge of fracture mechanics and structural integrity research with the official podcast of the Fracture and Structural Integrity journal. Join us for insightful interviews with top researchers, in-depth discussions of groundbreaking papers, and explorations of emerging trends in the field.All rights reserved Bildung Wissenschaft
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  • Effect of B4C variation on the mechanical, fractographic and tribological performance of hybrid composites Al7075/Gr/ZrO₂

    Effect of B4C variation on the mechanical, fractographic and tribological performance of hybrid composites Al7075/Gr/ZrO₂
    Jan 10 2026
    The current study focuses on the influence of varying levels of boron-carbide (B4C) particles on mechanical and tribological characteristics of Al7075 hybrid composites that are strengthened by fixed percentages of graphite (Gr) and zirconia (ZrO2). Hybrid composites were made by stir casting the 2 and 4 wt.% of B4C to Al7075-Gr-ZrO2 matrix in two-steps. The reinforcements were evenly spread throughout the matrix was confirmed by analysis through electron microscopy SEM together with elemental mapping through energy dispersive spectroscopy was utilized. Microstructural properties, tensile, hardness, and wear behaviour of the resulting hybrid composites were tested. The findings suggest that the adding of 4 wt.% B4C shall improve the hardness of Al7075 hybrid reinforced composites to 87 BHN, the UTS by 37% (214 MPa to 293 MPa) and vice versa slight decrease in ductility was attributed due to the addition of B4C. The tribological study revealed that the resistance to wear increased with additions of B4C as the hard ceramic particles served as load bearing phases. These results demonstrate the importance of B4C variation in improving mechanical and tribological behaviour of Al7075-Gr-ZrO2 hybrid reinforced composites in potential structural and aerospace application.
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    5 Min.
  • Hybrid feedforward neural network for pressure vessel internal corrosion prediction: integrating chemical models with inspection data for structural integrity assessment

    Hybrid feedforward neural network for pressure vessel internal corrosion prediction: integrating chemical models with inspection data for structural integrity assessment
    Jan 6 2026
    This study presents a hybrid framework integrating a physics-based corrosion model with a feedforward neural network (FNN) to predict corrosion rates and estimate the remaining useful life (RUL) of industrial pressure vessels for condition-based maintenance. Using non-destructive evaluation (NDE) wall thickness measurements from 24 inspection points over multiple years (2002–2008) and physics-based training data, a three-layer FNN with Monte Carlo dropout predicts localized corrosion rates, while exponential and linear degradation models project future wall thickness. The FNN achieved a coefficient of determination (R²) of 0.975 for corrosion rate prediction and a mean absolute error (MAE) of 0.1204 mm/year. For thickness prediction, the exponential model achieved R² = 0.99 with MAE = 0.0389 mm, outperforming the linear model (MAE) = 0.1350 mm. The framework was integrated with Fitness-for-Service (FFS) assessment based on API 579-1/ASME FFS-1 standards, enabling classification of vessel components and identification of sections requiring maintenance. This hybrid approach translates predictive analytics into standards-compliant engineering decisions for structural integrity management.
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    5 Min.
  • Experimental calibration of a virtual raster section for high-accuracy FDM simulation in Abaqus

    Experimental calibration of a virtual raster section for high-accuracy FDM simulation in Abaqus
    Jan 2 2026
    This study presents an experimentally calibrated methodology to enhance the predictive accuracy of finite element simulations for Fused Deposition Modeling (FDM) parts in Abaqus by replacing idealized filament geometry with a physically accurate “corrected virtual raster section.” A Box-Behnken Design of Experiments (DoE) across 27 ABS specimens systematically quantifies how key printing parameters, layer thickness, raster width, extrusion temperature, and print speed, influence the true cross-sectional geometry of deposited filaments, as measured via Scanning Electron Microscopy (SEM). These data inform a predictive mathematical model that transforms the conventional circular filament shape into an experimentally grounded oval-rectangular profile, accurately capturing extrusion-induced flattening and lateral spreading. The calibrated virtual section is integrated into a custom Python-based tool that parses G-code toolpaths and sweeps the corrected geometry along deposition trajectories to generate high-fidelity, mesh-ready Abaqus models. The workflow is validated through tensile testing of ASTM D638 specimens printed at 0°, 45°, and 90° raster orientations (n=3 per orientation). Error analysis against the experimental mean demonstrates that the corrected model reduces simulation errors from catastrophic levels in the non-corrected approach (7–92% relative error, 2.5–19 MPa absolute) to engineering-grade precision (0.03–7% relative error, ≤1.3 MPa absolute). This workflow bridges G-code to physical behavior, enabling reliable simulation of FDM anisotropy.
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    5 Min.
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