Roll bonding (RB) describes solid-state manufacturing processes where cold or hot rolling of plates or sheet metal is carried out for joining similar and dissimilar materials through the principle of severe plastic deformation. This review covers the mechanics of RB processes, identifies the key process parameters, and provides a detailed discussion on their scientific and/or engineering aspects, which influence the microstructure–mechanical behavior relations of processed materials. It further evaluates the available research focused on improving the metallurgical and mechanical behavior of bonded materials such as microstructure modification, strength enhancement, local mechanical properties, and corrosion and electrical resistance evolution. Moreover, current applications and advantages, limitations of the process and developments in dissimilar material hot roll bonding technologies for producing titanium to steel and stainless steel to carbon steel ultra-thick plates are also discussed. The paper concludes by deliberating on the bonding mechanisms, engineering guidelines and process–property–structure relationships, and recommending probable areas for future research.

The effect of temperature on the localized corrosion resistance and passive film characteristics of laser powder-bed fusion (LPBF) 316L (UNS S31603) was studied in a buffered 3.5 wt% NaCl solution at 25, 50, and 75 °C. DC techniques such as cyclic potentiodynamic polarization showed lower passive current densities, high breakdown potentials, and a higher resistance to initial breakdown compared with wrought 316L samples at all temperatures. However, LPBF 316L was more susceptible to metastable pitting at potentials before film breakdown and higher damage accumulation post film breakdown. AC techniques, such as Mott–Schottky analysis and electrochemical impedance spectroscopy, showed that the formed passive film was more robust on the LPBF 316L samples at all temperatures, accounting for the higher initial resistance to pitting. However, with increasing temperatures, the film formed had an increasing concentration of defect density. Passive compositions at the various test temperatures studied using X-ray photoelectron spectroscopy (XPS) showed that the LPBF samples showed higher amounts of Cr and Fe oxides and hydroxides compared with the wrought samples, which made the passive films on the LPBF samples more compact and protective. Investigation of the pits formed on the LPBF showed the preferential regions of attack were the melt-pool boundaries and cell interiors due to their being depleted of Cr and Mo when compared with the boundaries and matrix.

This study investigates the impact of varying steel fiber (SF) content (0%, 0.8%, 1.0%, and 1.2% by volume) on the mechanical and durability properties of 3D-printed Ca(OH)₂-activated geopolymer concrete (GPC). The addition of 1.2% SF improved flexural strength by 69% at 7 days and 16% at 28 days, while tensile strength more than doubled to 3.75 MPa at 28 days. Although compressive strength remained unaffected at 43 MPa, SF enhanced interlayer bond strength by 20%, which is crucial for layer cohesion in 3D-printed structures. Additionally, the elastic modulus increased by 7%, contributing to improved stiffness. Durability assessments, including autogenous shrinkage and self-induced stress, indicated a slight reduction in shrinkage of SF-reinforced samples, with no significant effect on self-induced stress. Microstructural analysis using scanning electron microscopy (SEM) and X-ray micro-computed tomography (µCT) demonstrated the crack-bridging behavior of steel fibers, enhancing ductility and fracture resistance. There was a slight increase in porosity (5.34%) of SF-reinforced samples without negatively affecting their mechanical properties. Notably, SF improved early-age toughness and controlled crack propagation across printed layers, addressing a critical challenge in 3D-printed concrete. The novelty of this work lies in successfully reinforcing 3D-printed Ca(OH)₂-activated GPC with recycled steel fibers, enhancing mechanical properties, interlayer bonding, and durability without compromising printability. This study offers a sustainable reinforcement strategy for 3D printing in construction.

Machine learning-driven automated replication micrographs analysis makes possible rapid and unbiased damage assessment of in-service steel components. Although micrographs captured by scanning electron microscopy (SEM) have been analyzed at depth using machine learning, there is no literature available on the technique being attempted on optical replication micrographs. This paper presents a machine-learning approach to segment and quantify carbide precipitates in thermally exposed HP40-Nb stainless-steel microstructures from batches of low-resolution optical images obtained by replication metallography. A dataset of nine micrographs was used to develop a random forest classification model to segment precipitates within the matrix (intragranular) and at grain boundaries (intergranular). The micrographs were preprocessed using background subtraction, denoising, and sharpening to improve quality. The method achieves high segmentation accuracy (91% intergranular, 97% intragranular) compared to human expert classification. Furthermore, segmented micrographs were quantified to obtain carbide size, shape, and density distribution. The correlations in the quantified data aligned with expected carbide evolution mechanisms. Results from this study are promising but necessitate validation of the method on a larger dataset representative of evolution of thermal degradation in steel, given that characterization of the evolution of microstructure components, such as precipitates, applies to broad applications across diverse alloy systems, particularly in extreme service.

This study reports the synthesis and characterization of Ni-P-AlN nanocomposite coatings with varying concentrations of aluminium nitride (AlN) nanoparticles. The coatings were deposited on carbon steel substrates using an electroless deposition technique. Structural (XRD, SEM) and compositional analyses (EDX) confirmed the successful co-deposition of AlN nanoparticles into the Ni–P matrix. A comparative analysis of the developed coatings indicates that Ni-P-AlN nanocomposite coatings exhibit their best mechanical and corrosion resistance performance with an optimum composition of AlN nanoparticles at 0.75 g/L. The improvement in microhardness of Ni-P-AlN nanocomposite coatings with increasing concentration of AlN nanoparticles can be attributed to the formation of a compact coating structure, grain refinement, and dispersion hardening effect. Similarly, an enhancement in corrosion protection efficiency of Ni-P-AlN nanocomposite by the gradual increase of AlN nanoparticles may be attributed to the reduction in the active area of the Ni-P matrix and reduced porosity due to the filling of pores by AlN nanoparticles. A decline in mechanical and corrosion-resistant properties beyond the optimal concentration (0.75 g/L) is observed, likely due to excessive agglomeration of AlN nanoparticles caused by their high surface area. These findings demonstrate that Ni-P-AlN nanocomposite coatings are suitable for many industries.