Applications of lightweight, wear-resistant polymer composites in automobile and engineering fields require an effective method for predicting and optimizing their wear performance, which will also curtail the time for designing new materials. Therefore, the present work employs L25 orthogonal array, machine learning (ML) techniques and wear mapping to study the dry sliding wear behavior of short carbon fiber–reinforced polypropylene (CF/PP) composites. Wear loss of the CF content (5–20 wt. %), applied load, sliding velocity and sliding distance were systematically investigated. The governing wear mechanisms were explained by using microstructural and worn surface analyses. The results show that the use of CF reinforcement changes the prevailing wear-mode from severe adhesive wear and plastic deformation in neat PP to stable micro-abrasive wear due to heat softening and crack initiation and propagation being prevented. Composites with 10–15 wt. % CF had the best wear resistance because the interfacial bond, matrix continuity, and reinforcing efficiency complemented the best. Of the other factors examined, Analysis of Variance revealed that wear loss was strongly impacted by all four parameters ). The most significant contribution was from load (32.88%), followed by sliding distance (15.20%), sliding velocity (21.89%), and fiber content (26.17%). Transition between mild and severe wear regimes, under different operating conditions, was identified with the use of wear maps. The developed and compared four ML algorithms are: K-Nearest Neighbors, Random Forest, Decision Tree, and Support Vector Machine (SVM). The SVM model showed the best accuracy of prediction (R² = 0.9362), which can be used to predict the wear and is used as a strong tool for the design and optimization of wear resistant CF/PP composite.
The optimization of mechanical properties in superduplex stainless steel forgings requires precise control of phase balance and precipitation kinetics. This study investigates the effect of different heat treatment routes, including single- and double-step solution annealing and varying cooling rates, on Cr nitride precipitation and secondary austenite formation in S32760 superduplex stainless steel. Mechanical and corrosion properties were assessed through Charpy impact, tensile, Vickers hardness, and pitting corrosion tests, complemented by optical and SEM microstructural characterization. Unlike the heat treatment, the radial sampling position within the investigated forged component did not significantly affect the measured properties. While pitting corrosion resistance remained unaffected, impact toughness was highly sensitive to the thermal treatment. A single-step treatment at 1120 °C caused severe embrittlement due to extensive Cr nitride precipitation, whereas a second solution annealing step at 1060 °C was necessary to restore toughness. Its effectiveness, however, depended strongly on the cooling rate applied after the first step. Microstructural analysis revealed that slower cooling after the first holding phase promotes coarser nitrides, which act as nucleation sites for secondary austenite during the second annealing stage, enhancing N absorption and limiting subsequent nitride precipitation. Conversely, rapid cooling reduces both nitride and secondary austenite formation, favoring renewed nitride precipitation during final cooling. These results highlight the initial cooling rate as the key parameter governing the effectiveness and reliability of two-step heat treatment for large-scale superduplex components.
This study proposes and applies a mathematical model to describe the process of recrystallization nucleation on large d-phase particles in the Inconel 718 nickel alloy during annealing (the particle stimulated nucleation (PSN) mechanism). The investigation employs a multilevel approach based on an extended statistical model, in which the subgrain structure is explicitly described by an aggregate of Laguerre polyhedra. The model for the formation of new recrystallized grain nuclei is based on the physical mechanisms of recovery: subgrain coalescence and low-angle boundary migration. Additionally, the model accounts for lattice rotations of subgrains near the non-deformable particle resulting from the incompatibility of prior plastic deformation, as well as the retarding effect of dispersed γ′ and γ″ particles during subgrain boundary migration. Numerical experiments were conducted on a representative volume of subgrains at annealing temperatures range 450–950 °C. It is shown that at 450 - 850°C subgrain growth near the d-phase particle is retarded due to increased misorientation, which hinders coalescence. At temperatures above 950 °C, the dissolution of the strengthening phases γ′ and γ″ leads to a decrease in the Zener pinning force and intense boundary migration near the d-phase particle results in the active formation of recrystallization nuclei. It is established that a needle-shaped d-particle promotes more intense nucleation compared to a spherical one due to its larger interfacial area. The simulation results are in qualitative agreement with known experimental data.
The quasi-brittle nature of Geopolymer Concrete (GPC) severely limits its broader structural viability. This study investigates the optimization of a Ground Granulated Blast-furnace Slag (GGBS) geopolymer concrete through sequential Styrene-Butadiene Rubber (SBR) latex modification and alkali-resistant (AR) glass fiber reinforcement. Polymer dosages from 0% to 25% were evaluated, validating a 15% SBR concentration as the microstructural baseline. Scanning Electron Microscopy and Energy Dispersive X-Ray Analysis (SEM/EDAX) confirmed that this dosage facilitates a monolithic interpenetrating polymer network and enhances Calcium Aluminosilicate Hydrate (C-A-S-H) gel precipitation. Subsequently, AR glass fibers were incorporated (0% to 2.0%) into the optimized SBR-GPC matrix. Empirical testing isolated 1.5% as the absolute optimal fiber dosage, yielding peak 28-day compressive (31.70 MPa), flexural (5.39 MPa), and bearing (62.26 MPa) capacities. Furthermore, single-point loading fracture mechanics testing revealed that the 1.5% integration maximized both fracture energy and the critical stress intensity factor , while also achieving a 50.00% enhancement in ultimate impact energy absorption and reducing chloride permeability to 1350.20 Coulombs (Low). Dosages exceeding 1.5% induced severe agglomeration, systematically degrading structural integrity. Finally, mathematical regression models were compared against advanced Machine Learning architectures like Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR). While simple empirical equations reliably predicted tensile and flexural capacities, the GPR framework demonstrated exceptional superiority in mapping the non-linear compressive strength behavior, achieving a Leave-One-Out Cross-Validation of 0.950.
Fatigue cracking at girth welds threatens offshore-pipeline integrity and requires uncertainty in flaw size, crack-growth behaviour, loading and weld geometry to be considered. A probabilistic fatigue-reliability framework is developed for a semi-elliptical surface crack in an API 5L X65 pipeline under cyclic global bending. Geometry-consistent stress-intensity-factor histories from a three-dimensional finite-element model provide 60 training and six case-wise held-out simulations. The Paris-law and loading distributions are identified as DNV code-calibrated benchmark inputs, not site-specific X65 measurements. For the fixed Paris exponent, normalisation by CeffΔσeq3 reduces the primary surrogate to a one-dimensional Gaussian process in initial crack depth. The model achieves R2 = 0.997 and MAPE = 4.04% on the test cases, and R2 = 0.996 with MAPE = 3.70% in leave-one-out validation. Using conditionally truncated inputs, the posterior-mean life distribution has a median of 6.84 × 108 cycles and a 95%-reliability design life of 2.12 × 108 cycles. Correlated latent-GP posterior-function propagation gives Pf = 4.147% at Nd = 2 × 108 cycles, with a 95% GP-posterior interval of 3.858–4.444%. Sensitivity analysis identifies the stress-range scale as dominant, showing that site-specific loading characterisation is essential for field application.
