The long-term durability of reinforced concrete infrastructure remains a critical challenge, as conventional Portland cement and carbon steel systems are inherently vulnerable to corrosion and environmental degradation. Roman concrete demonstrates exceptional longevity due to slow hydration kinetics, pozzolanic reactions, and self-healing mechanisms, but its integration into modern construction is limited by incompatibility with rapid construction workflows. At the same time, additive manufacturing has enabled advanced geometric control, while rarely addressing durability as a primary design objective. This study proposes a durability-driven construction system integrating Roman-type concrete, stainless steel reinforcement, and permanent additively manufactured thermoplastic formworks. Rather than acting as a temporary construction aid, the formwork is redefined as a permanent protective enclosure that sustains early-age loads, accommodates slow curing, and provides long-term environmental shielding. Stainless steel reinforcement is employed to mitigate corrosion, the dominant degradation mechanism. The system is evaluated using a multi-level methodology that combines material compatibility analysis, finite-element modelling of early-age conditions, and architectural-scale demonstration. The critical pre- and post-casting phases are analysed by modelling the fresh concrete as a fluid-like load acting on the permanent formwork, which represents the load-bearing component prior to setting. A segmented dome inspired by the Pantheon is used to demonstrate scalability and system integration. While direct validation over century-scale timeframes is impractical, the results show that the proposed system satisfies necessary conditions for extended service life, providing a scientifically grounded framework for durability-oriented construction using additive manufacturing.
To address the inadequate accuracy in chatter detection during milling operations, this study proposes a novel milling chatter detection methodology based on an optimized iTransformer-BiGRU-Random Forest (iTBU-RF) hybrid model. Initially, sensitivity analysis of time-frequency domain features is conducted employing Pearson correlation coefficients and significance levels to identify the features most sensitive to chatter detection. Subsequently, a chatter detection model integrating iTBU and RF is constructed. The hyperparameters of the ensemble model are optimized through the Ivy optimization algorithm. Following hyperparameter optimization, the model’s accuracy is substantially enhanced, achieving a maximum improvement of 2.40% compared to the pre-optimized configuration. Upon feature optimization, the model maintains superior classification performance while simultaneously reducing training time from 153.83 seconds to 116.74 seconds, thereby improving computational efficiency by approximately 24.11%. In comparison with benchmark methodologies, the proposed approach demonstrates optimal performance across all evaluation metrics, including accuracy. This investigation provides a novel technological framework for enhancing the precision of chatter detection in milling operations.
3D printing provides an effective digital fabrication route for manufacturing structured adsorbents with customized geometries, offering clear advantages in permeability, recoverability, and structural integration for water treatment applications. However, a fundamental challenge remains: high porosity, which is essential for mass transfer and adsorption capacity, often compromises mechanical robustness, thereby limiting structural stability, recyclability, and service life under dynamic operating conditions. Most existing studies address this trade-off through incremental optimization within individual material systems, resulting in limited performance improvement. This review systematically summarizes recent advances in 3D-printed structured adsorbents by taking adsorption mechanisms as the central framework. Strategies for enhancing mass transfer through hierarchical pore architecture are reviewed alongside a critical analysis of chemical durability and mechanically governed structural stability, which are key factors for engineering reliability. Emerging fabrication approaches, including core-shell printing and multi-material co-extrusion, are discussed as promising routes to decouple adsorption functionality from load-bearing structures, enabling the concurrent improvement of adsorption performance and mechanical integrity. In addition, challenges related to performance evaluation, dynamic adsorption testing, and cost-benefit considerations are examined, providing guidance for the transition from material-level printing toward structurally reliable adsorption device design.
The expensive energy prices and sustainability goals are driving the precision manufacturing facilities to stop their periodic energy reporting to full-time, machine-level reporting that can provide insights into where energy is used, anticipate future-demand and observe unusual behavior in the CNC machining and digital fabrication processes. This paper creates a real-time smart power dashboard, which combines power measurement and production-aware processing to facilitate actionable energy governance on the shop floor. This workflow coordinates time-stamped power data (and optional machine context), cleanses and rebuilds windows of features, and uses a multi-model forecasting layer (autoregressive integrated moving average, additive time-series decomposition, gradient-boosted regression, and long short-term memory (LSTM)) to make short-horizon predictions. A dual protocol based on standardized deviation monitoring and isolation-based outlier detectors detect abnormal consumption with energy windows being clustered into repeatable profiles using clustering to facilitate benchmarking across machines and shifts. The prototype testing demonstrates that the forecasting layer has a best mean absolute percentage error (MAPE) of 8.9, the clustering operation has a conspicuous separation with a silhouette score of 0.742 and the anomaly detection has a precision of 95.7 and a false positive of 2.8 at minimal computing power. Such findings show that the dashboard, as suggested, can be used to provide reliable forecasting, interpretable profiling and low noise alerting that can be used in real-time monitoring. The strategy offers deployable analytics structure that converts raw power streams into decision-ready data and facilitates undertakable efficiency steps by means of energy per job, peak-load exposure, and share of non-productive energy indicators.
This work examines how different welding regimes and filler metal types influence the characteristics of hardfaced layers and the associated heat-affected zones (HAZ) in components made of low-alloy steel 30CrMoV9. Bead-on-plate welding tests were carried out on plate specimens, using five filler metals, including four gas-shielded wires with different chemical compositions and one flux-cored wire. For each filler metal, two welding regimes were applied by varying the current, voltage, and travel speed. After welding, the bead geometry and hardness were measured, and bending tests were performed to assess cracking behavior. The results show that both filler metal selection and arc energy have a pronounced effect on bead shape and hardness, as well as on the hardness distribution in the HAZ. It is also observed that, because of the metallurgical characteristics of 30CrMoV9 steel, preheating and/or post-weld heat treatment is required to reduce the risk of cracking. The findings may serve as practical input for process selection and quality control in the fabrication and repair of precision mechanical parts.
The fundamental mechanical properties and constrained recovery behavior of two domestically produced Fe-Mn-Si shape memory alloys (SMAs) (Fe-16.86Mn-4.5Si-10.3Cr-5.29Ni-0.08C and Fe-17.6Mn-4.5Si-3.22Cr-2.96Ni-0.28C-1.45V) were investigated with specific reference to their potential application in bridge strengthening. Uniaxial tensile tests, differential scanning calorimetry (DSC), and thermal expansion measurements were conducted to determine the elastic modulus, transformation stress, transformation temperatures, and thermal expansion characteristics. The alloy containing vanadium exhibited a higher elastic modulus and a higher transformation stress than the vanadium-free alloy. In addition, the presence of vanadium significantly reduced the width of the transformation temperature interval, which is advantageous for temperature control during practical activation. Constrained recovery tests showed that the recovery stress increased with increasing activation temperature and reached a maximum at a pre-strain of approximately 6%. The level of pre-applied stress had only a minor effect on the final recovery stress, indicating a stable and controllable recovery behavior under engineering conditions. These results provide both experimental data and a mechanical basis for the use of domestically produced Fe-Mn-Si shape memory alloys in the active strengthening of civil engineering structures.
The design of automatic clamping mechanisms often involves trade-offs between clamping stability, structural compactness, manufacturability, and operational reliability. These trade-offs are difficult to handle in early design stages, where decisions are largely experience-based and design alternatives are not yet fully defined. An integrated design approach combining Extenics and TRIZ is applied to support the innovative development and structural optimization of an automatic clamping mechanism. Functional requirements and structural constraints are first expressed in the form of Extenics element models. Key design conflicts are then identified through functional analysis and addressed using TRIZ contradiction principles and inventive principles, which guide the generation of alternative structural configurations. The candidate designs are evaluated with respect to mechanical performance, manufacturability, and structural feasibility in order to select a configuration that better satisfies practical engineering requirements. The approach is illustrated through the redesign of an automatic clamping mechanism. The results show that the selected configuration improves clamping stability and structural reliability while maintaining reasonable manufacturability. The study suggests that the combined use of Extenics and TRIZ can support systematic innovation in mechanical structure design and provide practical guidance for similar precision engineering applications.
The enduring resilience of Roman infrastructure, exemplified by the Tiberius Bridge in Rimini—completed in the 1st century CE and remaining structurally sound after over two millennia—has long drawn scholarly attention. This study re-evaluates Roman construction methodologies with a particular focus on opus caementicium (Roman concrete) encased within durable permanent facings such as opus quadratum, opus incertum, and opus latericium. Central to this longevity was the use of pozzolanic binders, which underwent prolonged hydration reactions, enabling continued strength development over extended timescales—markedly contrasting with contemporary hydraulic cements engineered for rapid early-age strength gain. A comparative analysis is conducted between ancient Roman materials and modern high-performance cementitious composites, including High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and Engineered Cementitious Composites (ECC). Contemporary practices are frequently guided by design codes such as Eurocode, which, while structurally robust, tend to prioritize short-term performance metrics. To bridge this gap, a hybrid construction strategy is proposed wherein additive manufacturing is employed to produce permanent structural formworks that mimic the load-bearing and protective functions of Roman facings. This approach enables the use of modern slow-maturing binders within digitally fabricated enclosures, thus integrating ancient durability principles into automated, scalable workflows. By reconciling historical construction insights with advances in modern materials science and digital fabrication, a new paradigm is introduced for designing infrastructure with service lives far exceeding the conventional 50–100 year design horizon. The implications of such an approach extend to both sustainability and resilience, offering a technically viable and historically informed route toward ultra-durable infrastructure in the face of evolving environmental and operational challenges.
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.
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.
The widespread adoption of electric vehicles (EVs) has brought about critical challenges in brake rotor performance, primarily attributed to the reduced reliance on conventional friction braking systems. This decreased usage, owing to the predominant application of regenerative braking, has inadvertently increased the susceptibility of brake rotors—particularly those manufactured from grey cast iron (GCI)—to corrosion and non-traditional wear mechanisms due to extended exposure to environmental elements. These challenges are compounded by the global imperative for sustainable transportation solutions, as emphasized in the European Union (EU)’s roadmap for climate-neutral mobility. In this context, the development and implementation of sustainable strategies to improve the wear and corrosion resistance of EV brake rotors have become paramount. This review synthesizes recent advancements in environmentally conscious approaches, including the application of eco-friendly surface treatments, alloying modifications, microstructural engineering, and solid or dry lubrication techniques tailored for GCI rotors. The analysis extends to the evaluation of scalability, cost-efficiency, tribological stability, and environmental compatibility over the rotors' service life. Particular attention is devoted to emergent solutions such as bio-inspired multifunctional coatings, integration of intelligent condition-monitoring technologies, and rotor design optimized through data-driven predictive modelling. The necessity for robust life cycle assessments (LCA) is underscored, aiming to holistically quantify environmental impact from raw material extraction through end-of-life disposal or recycling. Key research gaps are identified, including the limited real-world validation of novel materials under EV-specific load profiles and insufficient understanding of synergistic degradation modes under mixed braking regimes. It is suggested that a multidisciplinary research agenda—merging materials science, tribology, electrochemistry, and intelligent systems—is essential to advance the next generation of high-performance, low-impact braking solutions. In doing so, a comprehensive framework for sustainable brake rotor innovation in EVs can be established, aligning material resilience with broader environmental and regulatory goals.
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