A Topology Optimization Method for Stochastic Lattice Structures

AbstractStochastic lattice structures are very powerful solutions for filling three-dimensional spaces using a generative algorithm. They are suitable for 3D printing and are well appropriate to structural optimization and mass distribution, allowing for high-performance and low-weight structures. The paper shows a method, developed in the Rhino-Grasshopper environment, to distribute lattice structures until a goal is achieved, e.g. the reduction of the weight, the harmonization of the stresses or the limitation of the strain. As case study, a cantilever beam made of Titan alloy, by means of SLS technology has been optimized. The results of the work show the potentiality of the methodology, with a very performing structure and low computational efforts

Optical measurements and experimental investigations in repeated low-energy impacts in powerboat sandwich composites

In the world of powerboats competition, the high-performance sandwich-structured composites have completely replaced traditional materials. During the competition, the structure of this kind of ships is subjected to repeated impacts. It is then fundamental to understand the damage evolution in order to select the most appropriate materials and increase safety issues. The present study is aimed at analysing the behaviour of sandwich-structured composites undergoing repeated low-energy impacts. Three different materials have been analysed. Two are sandwich-structured composites used for the cockpit of offshore powerboats and differing only by the core cell thickness. The third material is composed only by the skin of the same sandwich structures, without the core. Impacts were made at three different energy levels: 15, 17.5 and 20 J. In addition to the parameters typically used for the assessment of the impact damage, a new damage assessment has been carried out by means of three-dimensional optical measurements of the imprinted volumes resulting from the impact events. This approach has allowed the definition of a correlation between the imprinted volumes and the number of impacts, until the complete perforation, for each single specimen. Finally, thanks to usual indexes and the imprinted volumes, some considerations are developed about the influence of the core cell thickness in powerboats design

Composite sandwich impact response: experimental and numerical analysis

The use of composite materials allows to have a great flexibility in terms of mechanical and physical characteristics. One of the most used composite structure in naval field, is the sandwich, which is composed by a stacking sequence of different plies. The designer, in preliminary phase, must handle a great quantity of degree of freedom (types of materials, orientation of the fibres, position along the stack, thickness, etc.) in order to reach the best compromise between mechanical behaviour, environmental impacts and production costs. Finite Element analysis represents a useful tool in order to optimize all these parameters and to estimate the outcome of experimental tests at design stage. The main goal of this work is to develop and to validate a FE model for the simulation of a particular family of composites, widely used in naval field and, in particular, in High Speed Crafts and powerboats. The first part of the paper concerns the experimental tests on two different types of sandwich specimens. Two families of tests were conducted: four-point bending tests and impact drop tests. The second part of the paper focuses on the validation of a FE model for both experimental setups

Damage assessment of different FDM-processed materials adopting Infrared Thermography

The use of components obtained through the additive manufacturing (AM) technique has become increasingly widespread in recent years, playing a central role in industrial production, and in particular in some fields such as automotive, biomedical, aerospace and electronics. Among all AM techniques, FDM (Fused Deposition Modelling) represents the most used printing technique to produce polymeric and composite components, thanks to the flexible printing process, the low cost and the diversity of the materials adopted. The aim of the present work concerns the comparison between the mechanical properties of three plastic materials printed with the FDM technique (polylactic acid PLA, polyethylene terephthalate glycol-modified PETG and Acrylonitrile-butadiene-styrene ABS) using an Original Prusa i3 MK3S, by varying the raster angle between 0°, 45° and 90° degrees. Infrared Thermography has been adopted to monitor the temperature evolution during static tensile tests and to assess stress level that can initiate damage within the material. Failure analysis was performed to correlate the mechanical behaviour with the microstructural characteristics of the materials

Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond

Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minimizing a cost function over their local inputs. FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown vulnerable to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups than to others. In this paper, we address the triadic interaction among personalization, privacy guarantees, and fairness attained by models trained within the FL framework. Differential privacy and its variants have been studied and applied as cutting-edge standards for providing formal privacy guarantees. However, clients in FL often hold very diverse datasets representing heterogeneous communities, making it important to protect their sensitive information while still ensuring that the trained model upholds the aspect of fairness for the users. To attain this objective, a method is put forth that introduces group privacy assurances through the utilization of $d$-privacy (aka metric privacy). $d$-privacy represents a localized form of differential privacy that relies on a metric-oriented obfuscation approach to maintain the original data's topological distribution. This method, besides enabling personalized model training in a federated approach and providing formal privacy guarantees, possesses significantly better group fairness measured under a variety of standard metrics than a global model trained within a classical FL template. Theoretical justifications for the applicability are provided, as well as experimental validation on real-world datasets to illustrate the working of the proposed method

A Well-to-Wheel Comparative Life Cycle Assessment Between Full Electric and Traditional Petrol Engines in the European Context

AbstractAutomotive sector is crucial for the economic and social system. Conversely, it also plays an important role in the global emissions balance with strong consequences on the environment. Currently the Research world is engaged in the reduction of the emissions, especially in order to contrast the Climate Change and reduce toxicity on humans and the ecosystem. This study presents a comparative Life Cycle Assessment, Well-to-Wheel, between the most common technology used in the automotive sector, i.e. the traditional petrol Internal Combustion Engine and the full Battery Electric Vehicle. The different configurations have been analysed within 17 different impact categories in terms of climate change, human health, resourced depletion and ecosystems. The Well-to-Wheel approach allows to focus the attention on the use stage of the vehicle, considering the local effects due to the direct emissions in high density urban zones and it mitigates the dependence of usage hypotheses, different scenarios and intrinsic differences between the various models of cars in circulation

Optimum VM Placement for NFV Infrastructures

This paper shows how to use a Linux-based operating system as a real-time processing platform for low-latency and predictable packet processing in cloudified radio-access network (cRAN) scenarios. This use-case exhibits challenging end-to-end processing latencies, in the order of milliseconds for the most time-critical layers of the stack. A significant portion of the variability and instability in the observed end-to-end performance in this domain is due to the power saving capabilities of modern CPUs, often in contrast with the low-latency and high-performance requirements of this type of applications. We discuss how to properly configure the system for this scenario, and evaluate the proposed configuration on a synthetic application designed to mimic the behavior and computational requirements of typical software components implementing baseband processing in production environments

Thermal Emission analysis to predict damage in specimens of High Strength Concrete

In this paper thermal analysis was applied to determine the “Critical Stress” of concrete, different from its ultimate strength, able to produce the first damage in the structures under compressive loads. The Critical Stress can be thought as the stress able to produce the beginning of fatigue rupture within the material. Several specimens of high strength concrete were tested in order to define the incipient crack phenomena, also in internal part of the specimen not accessible by direct inspections, with the aid of infrared thermography. A finite element analysis completes the study and compares, for the same static loading conditions, the stress state with the experimental thermographic images. The final results show as the coupling of normal compressive test and the acquisition of the thermal images can be a useful aid to estimate a security stress value, indeed the Critical Stress, before the Ultimate Serviceability Limit (SLU) of the structure, defined as the maximum load condition before its failure

Forecasting Operation Metrics for Virtualized Network Functions

Network Function Virtualization (NFV) is the key technology that allows modern network operators to provide flexible and efficient services, by leveraging on general-purpose private cloud infrastructures. In this work, we investigate the performance of a number of metric forecasting techniques based on machine learning and artificial intelligence, and provide insights on how they can support the decisions of NFV operation teams. Our analysis focuses on both infrastructure-level and service-level metrics. The former can be fetched directly from the monitoring system of an NFV infrastructure, whereas the latter are typically provided by the monitoring components of the individual virtualized network functions. Our selected forecasting techniques are experimentally evaluated using real-life data, exported from a production environment deployed within some Vodafone NFV data centers. The results show what the compared techniques can achieve in terms of the forecasting accuracy and computational cost required to train them on production data

Gastro-intestinal emergency surgery: Evaluation of morbidity and mortality. Protocol of a prospective, multicenter study in Italy for evaluating the burden of abdominal emergency surgery in different age groups. (The GESEMM study)

Gastrointestinal emergencies (GE) are frequently encountered in emergency department (ED), and patients can present with wide-ranging symptoms. more than 3 million patients admitted to US hospitals each year for EGS diagnoses, more than the sum of all new cancer diagnoses. In addition to the complexity of the urgent surgical patient (often suffering from multiple co-morbidities), there is the unpredictability and the severity of the event. In the light of this, these patients need a rapid decision-making process that allows a correct diagnosis and an adequate and timely treatment. The primary endpoint of this Italian nationwide study is to analyze the clinicopathological findings, management strategies and short-term outcomes of gastrointestinal emergency procedures performed in patients over 18. Secondary endpoints will be to evaluate to analyze the prognostic role of existing risk-scores to define the most suitable scoring system for gastro-intestinal surgical emergency. The primary outcomes are 30-day overall postoperative morbidity and mortality rates. Secondary outcomes are 30-day postoperative morbidity and mortality rates, stratified for each procedure or cause of intervention, length of hospital stay, admission and length of stay in ICU, and place of discharge (home or rehabilitation or care facility). In conclusion, to improve the level of care that should be reserved for these patients, we aim to analyze the clinicopathological findings, management strategies and short-term outcomes of gastrointestinal emergency procedures performed in patients over 18, to analyze the prognostic role of existing risk-scores and to define new tools suitable for EGS. This process could ameliorate outcomes and avoid futile treatments. These results may potentially influence the survival of many high-risk EGS procedure