The influence of prestrain on the microstructural evolution and corrosion behaviour of copper-based alloys has been systematically investigated to elucidate the mechanisms by which mechanical preconditioning enhances structural integrity and electrochemical stability. Prestrain, applied prior to subsequent thermomechanical treatments, has been found to significantly alter dislocation density, grain size distribution, phase transformation pathways, and precipitate morphology and distribution. These changes collectively promote grain refinement and the formation of nanocrystalline domains, thereby improving both strength and ductility. Enhanced effects have been observed in Cu–Cr–Zr and Cu–Al–Ni alloys, particularly when prestrain is introduced via cold rolling or friction stir processing (FSP). In these systems, microstructural stability during post-deformation ageing is markedly improved due to the suppression of grain coarsening and the controlled precipitation of strengthening phases. Moreover, prestrain modifies the local chemical and crystallographic environment in a manner that critically impacts electrochemical behavior. Intermediate levels of mechanical stress have been shown to improve corrosion resistance by facilitating the formation of uniform, adherent passive films, while excessive strain introduces microstructural heterogeneities that serve as initiation sites for intergranular and stress corrosion cracking. These phenomena were characterized using X-ray diffraction, scanning and transmission electron microscopy (TEM), and electrochemical techniques including potentiodynamic polarization and electrochemical impedance spectroscopy. The interplay between mechanical preconditioning, microstructural refinement, and corrosion mechanisms has been clarified, offering insights into process–structure–property relationships. The findings hold particular relevance for the design and optimization of copper alloys in high-performance applications such as electronic interconnects, biomedical implants, and aerospace components, where dimensional stability, chemical resilience, and machinability are of paramount importance. The study underscores the critical role of prestrain not only as a structural refinement tool but also as a means of tailoring corrosion resistance through controlled microstructural engineering.
Prompt and proper maintenance management helps extend the operation lifespan of workplace equipment to achieve production targets without interrupting the production process. In this connection, accurate prediction of the reliability-based scheduled maintenance (SM) time intervals of equipment is essential. The current research aimed to develop a reliability-based model to forecast the maintenance time intervals specifically for Load-Haul-Dumper (LHD) underground mining equipment. The series configuration system of the Reliability Block Diagram (RBD) model was adopted to evaluate the overall system reliability for each LHD machine. The reliability percentage of each sub-system was ascertained through a reliability analysis of a complex repairable system. To build the required Artificial Neural Network (ANN) model for analysis, the “Isograph Reliability Workbench 13.0” software was adopted to estimate the input layers of reliability ($R$) and the best-fit distribution parameters, such as the scale parameter ($\eta$), shape parameter ($\beta$), and location parameter ($\gamma$). The ANN model created was trained using the Levenberg-Marquardt (LM) learning algorithm. The predicted SM values were extremely close to the calculated values as indicated by the optimal $R^2$ value of 0.9998. The outcome demonstrated that the ANN model could improve the performance of the equipment with a major impact on the initial weight optimization. Suggestions were made for the industry practitioners to enhance the dependability of the equipment with planned maintenance procedures designed by the proposed ANN, with possible potential to be explored by other equipment users.
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