This paper investigates energy harvesting performances of porous piezoelectric polymer films to collect electrical energy from vibrations and power various sensors. The influence of void content on the elastic matrix, dielectric, electrical, and mechanical properties of porous piezoelectric polymer films produced from available commercial poly(ethylene-co-vinyl acetate) using an industrially applicable melt-state extrusion method (EVA) were examined and discussed. Electrical and mechanical characterization showed an increase in the harvested current and a decrease in Young’s modulus with the increasing ratio of voids. Thermal analysis revealed a decrease in piezoelectric constant of the porous materials. The authors present a mathematical model that is able to predict harvested current as a function of matrix characteristics, mechanical excitation and porosity percentage. The output current is directly proportional to the porosity percentage. The harvested power significantly increases with increasing strain or porosity, achieving a power value up to 0.23, 1.55, and 3.87 mW/m³ for three EVA compositions: EVA 0%, EVA 37% and EVA 65%, respectively. In conclusion, porous piezoelectric EVA films has great potential from an energy density viewpoint and could represent interesting candidates for energy harvesting applications. Our work contributes to the development of smart materials, with potential uses as innovative harvester systems of energy generated by different vibration sources such as roads, machines and oceans.

The growing demand for sustainable and self-powered technologies in automotive applications has led to increased interest in energy harvesting from vehicle suspensions. Recovering mechanical energy from road-induced vibrations offers a viable solution for powering wireless sensors and autonomous electronic systems, reducing dependence on external power sources. This study presents the design, modeling, and experimental validation of a hybrid energy-harvesting system that integrates piezoelectric and magnetoelectric effects to efficiently convert mechanical vibrations into electrical energy. A model-based systems engineering (MBSE) approach was used to optimize the system architecture, ensuring high energy conversion efficiency, durability, and seamless integration into suspension systems. The theoretical modeling of both piezoelectric and magnetoelectric energy harvesting mechanisms was developed, providing analytical expressions for the harvested power as a function of system parameters. The designed system was then fabricated and tested under controlled mechanical excitations to validate the theoretical models. Experimental results demonstrate that the hybrid system achieves a maximum power output of 16 µW/cm² from the piezoelectric effect and 3.5 µW/cm² from the magnetoelectric effect. The strong correlation between theoretical predictions and experimental measurements confirms the feasibility of this hybrid approach for self-powered automotive applications.

This study investigates advanced diagnostic techniques for predicting bearing faults in industrial turbogenerators using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR). Real-world data were collected from a 58 MW steam-driven turbogenerator over a two-year period, including vibration and rotational speed measurements, to evaluate the reliability of predictive maintenance strategies. A threshold-based monitoring approach was first applied to classify machine health using predefined alarm and trip limits for speed (3003–3005 rpm) and vibration (27–29 mm/s). Machine learning techniques were then implemented, including SVR models for regression analysis and ANN models trained using Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) algorithms. The results indicated that threshold-based monitoring successfully identified abnormal operating conditions, with multiple instances of excessive vibration and speed exceeding critical limits. However, linear SVR models demonstrated poor predictive performance, with negative R² values, highlighting the nonlinear nature of the system. In contrast, ANN models achieved high prediction accuracy, with Bayesian Regularization providing the most stable and robust performance across datasets. These findings confirm the effectiveness of hybrid diagnostic frameworks that integrate physical threshold monitoring with AI-based models for reliable predictive maintenance in industrial turbogenerator systems.

This paper presents the design and development of a laboratory-scale 3D printing platform intended for research on sustainable construction materials. The growing interest in low-carbon and locally available materials, including clay, geopolymers, recycled aggregates, and bio-based composites, has increased the need for flexible experimental printing systems. However, most existing construction 3D printers are designed for industrial applications and remain costly, bulky, or limited to specific material categories. The proposed platform was developed using a Model-Based Systems Engineering approach in order to structure the design process and establish links between user needs, system requirements, functions, and physical components. The platform is based on modular Cartesian architecture and includes interchangeable extrusion systems. A syringe extruder is used for relatively fluid materials such as clay slurries, ceramic pastes, gypsum-based mixtures, and fluid geopolymers, while a screw extruder is designed for more viscous materials such as mortars, cement-based mixtures, and dense geopolymer pastes. The system also integrates motion-control components, material feeding devices, monitoring elements, and safety functions to ensure stable and repeatable printing conditions. The platform is intended to support the evaluation of printability, material flow, layer deposition, dimensional stability, and interlayer bonding. By combining a flexible hardware configuration with an MBSE-based design methodology, the proposed system provides a practical research tool for the development and validation of sustainable construction materials. It also creates opportunities for future work on multi-material printing, automated process control, and digital manufacturing applications.