Andrea Mura

This paper presents the design methodology and analytical sizing of a laboratory test rig for a mechanical power take-off (PTO) system for ocean wave energy conversion. The PTO comprises dual racks, pinions with freewheel clutches, a toothed-belt transmission, a planetary speed multiplier and a flywheel provision. The rig architecture couples prescribed linear excitation with controllable rotary loading and synchronized force, torque, position and speed acquisition. Ideal kinematic relations and a lumped torque-balance model connect the reference input to component-selection requirements. For a displacement amplitude of 0.5 m, period of 4 s, pinion pitch radius of 0.09 m and total speed ratio of 36, the calculated engaged-drive speed envelope is 3000 rpm. A 5 kN input force capacity gives an ideal output-torque envelope of 12.5 N m and a force-speed capacity product of 3.93 kW. A nominal mechanical-efficiency assumption of 0.884 yields an indicative output-capacity estimate of approximately 3.47 kW. The paper defines the mechanical interfaces, load-emulation concept, acquisition architecture, operating constraints and future verification procedure.

Wave energy represents one of the most promising renewable sources due to its high energy density and predictable availability compared to wind and solar power. Despite its potential, technological exploitation remains challenging because of harsh marine environments, high installation and maintenance costs, and the absence of a dominant technology. This work presents a comprehensive review of wave energy conversion technologies and provides a detailed kinematic and dynamic analysis of a novel mechanical system designed to transform oscillatory linear motion into continuous unidirectional rotary motion, suitable for electricity generation. Particular focus is placed on the analytical modeling of the system, the design of a flywheel for energy stabilization, and performance evaluation. Results highlight the feasibility of the proposed configuration and its potential advantages compared to conventional hydraulic systems.

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments.

Among the amazing properties of graphene, superlubricity is one of the most promising properties. This property can be used in industrial field components to reduce friction without using liquid lubricants, and therefore, improve machines’ efficiency and reliability with low environmental impact thanks to the elimination of oil or grease lubricants. In this paper, copper alloy samples for electrical purposes were coated with graphene by four different deposition processes. The investigated synthesis processes are direct grown graphene on bulk Cu, transferred graphene, and self-assembled graphene from graphene flakes. Ball-on-disk tests were performed to evaluate the tribological performance of samples. The aim was to compare the effect on the tribological performance given by different types of coatings, taking also into consideration industrial scalability. Interestingly, not all graphene nano-coatings being compared proved effective in reducing friction and wear in gross sliding conditions. The results show that the cost-effective self-assembled graphene is the longer-lasting nano-coating among those investigated in this work, and can reduce both friction and wear. Tests revealed that graphene coatings can be applied as a solid lubricant, reducing friction up to 78%, and reducing the average wear volume up to 40%.

Thin-rim gears are widely used in industrial fields such as aerospace and electric vehicles due to the advantage of light weight. Yet, the root crack fracture failure of thin-rim gears significantly limits their application and further affects the reliability and safety of high-end equipment. In this work, the root crack propagation behavior of thin-rim gears is experimentally and numerically investigated. The crack initiation position and crack propagation path for different backup ratio gears are simulated using gear finite element (FE) models. The crack initiation position is determined using the maximum gear root stress position. An extended FE method coupled with commercial software ABAQUS is used to simulate the gear root crack propagation. The simulation results are then verified by conducting experimental tests for different backup ratio gears based on a dedicated designed single-tooth bending test device.

In this work graphene coatings have been deposited on aluminum alloy samples to investigate whether they can improve wear strength and promote friction reduction, which means components reliability and efficiency. Tribological performance has been tested by pin-on-disk wear tests. Three procedures to coat the samples have been explored. One is based on transferred graphene method and the other two are based on percolative graphene. Results show that graphene coatings last little time onto the sample surface, though strongly reducing the coefficient of friction of aluminum samples as far as they endure.

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

Introduction Severe psychiatric disorders are typically associated with a significant reduction in life expectancy compared with the general population. Among the different hypotheses formulated to explain this observation, accelerated ageing has been increasingly recognised as the main culprit. At the same time, telomere shortening is becoming widely accepted as a proxy molecular marker of ageing. The present study aims to fill a gap in the literature by better defining the complex interaction/s between inflammation, age-related comorbidities, telomere shortening and gut microbiota in psychiatric disorders. Methods and analysis A cross-sectional study is proposed, recruiting 40 patients for each of three different diagnostic categories (bipolar disorder, schizophrenia and major depressive disorder) treated at the Section of Psychiatry and at the Unit of Clinical Pharmacology of the University Hospital Agency of Cagliari (Italy), compared with 40 age-matched and sex-matched non-psychiatric controls. Each group includes individuals suffering, or not, from age-related comorbidities, to account for the impact of these medical conditions on the biological make-up of recruited patients. The inflammatory state, microbiota composition and telomere length (TL) are assessed. Ethics and dissemination The study protocol was approved by the Ethics Committee of the University Hospital Agency of Cagliari (PG/2018/11693, 5 September 2018). The study is conducted in accordance with the principles of good clinical practice and the Declaration of Helsinki, and in compliance with the relevant Italian national legislation. Written, informed consent is obtained from all participants. Participation in the study is on a voluntary basis only. Patients will be part of the dissemination phase of the study results, during which a local conference will be organised and families of patients will also be involved. Moreover, findings will be published in one or more research papers and presented at national and international conferences, in posters or oral communications.

Friction and wear affect machine reliability, efficiency and safety, increasing the maintenance and operating costs. A reduction of the coefficient of friction (CoF) of sliding parts would increase the machine efficiency, while a reduction of wear damage would allow higher reliability and safety. The aim of this work is to investigate the application of graphene to reduce the CoF and wear damage in mechanical components made of steel. In this work, graphene has been applied on C40 steel samples using two coating procedures based on chemical vapor deposition technique: direct growth graphene (DG) and transferred graphene coating (TGC). Tribological performance has been evaluated, by means of ball on disk tests, in terms of CoF and worn volume. Results obtained with no coated samples with have been compared to those obtained with coated ones. Results show that the proposed graphene coatings enhance tribological performance of C40 steel, in particular results show that DG sample has better wear strength, while TGC sampled are better to reduces CoF.