Caratterizzazione microstrutturale e prove di resilienza su giunti Friction Stir Welding e Linear Friction Welding di compositi a matrice metallica

In questo studio sono stati caratterizzati giunti Friction Stir Welding e Linear Friction Welding su compositi a matrice in lega di alluminio e rinforzo particellare ceramico. Il processo FSW è stato applicato a due compositi ottenuti con processo fusorio, quindi estrusi e trattati termicamente T6: AA6061/20%vol.Al2O3p e AA7005/10%vol.Al2O3p. I giunti LFW sono stati invece realizzati su un composito con matrice in lega di alluminio e rinforzo particellare in carburo di silicio, ottenuto mediante metallurgia delle polveri, quindi forgiato e trattato termicamente T4: AA2124/25%vol.SiCp. Sono stati esaminati gli effetti della saldatura sullecaratteristiche microstrutturali dei giunti, avvalendosi di tecniche di microscopia ottica con analisi di immagine e di microscopia elettronica in scansione (SEM) con microsonda a dispersione di energia (EDS). Sono state quindi condotte prove di resilienza con pendolo strumentato Charpy. Lo studio dei meccanismi di danneggiamento è stato effettuato mediante analisi al SEM delle superfici di frattura. Entrambi i processi di saldatura hanno portato a giunti sostanzialmente esenti da difetti. La microstruttura dei cordoni è risultata dipendente sia dalle caratteristiche microstrutturali iniziali dei compositi considerati, sia dalla tipologia di processo di saldatura. Nel caso dei compositi AA6061/20%Al2O3p e AA7005/10%Al2O3p saldati FSW si è osservato un sostanziale incremento di resilienza, rispetto al materiale base, in conseguenza dell’affinamento dei grani della matrice, della riduzione della dimensione media delle particelle di rinforzo e della loro spigolosità, indotte dal processo di saldatura. Il composito AA2124/25%SiCp saldato LFW ha presentato valori di resilienza confrontabili con quelli del materiale base, in conseguenza, soprattutto, dei limitati effetti della saldatura su dimensione e distribuzione delle particelle di rinforzo

A review of friction stir welding of aluminium matrix composites

As a solid state joining process, friction stir welding (FSW) has proven to be a promising approach for joining aluminium matrix composites (AMCs). However, challenges still remain in using FSW to join AMCs even with considerable progress having been made in recent years. This review paper provides an overview of the state of-the-art of FSW of AMC materials. Specific attention and critical assessment have been given to: (a) the macrostructure and microstructure of AMC joints, (b) the evaluation of mechanical properties of joints, and (c) the wear of FSW tools due to the presence of reinforcement materials in aluminium matrices. This review concludes with recommendations for future research directions

Monte-Carlo simulations of the recombination dynamics in porous silicon

A simple lattice model describing the recombination dynamics in visible light emitting porous Silicon is presented. In the model, each occupied lattice site represents a Si crystal of nanometer size. The disordered structure of porous Silicon is modeled by modified random percolation networks in two and three dimensions. Both correlated (excitons) and uncorrelated electron-hole pairs have been studied. Radiative and non-radiative processes as well as hopping between nearest neighbor occupied sites are taken into account. By means of extensive Monte-Carlo simulations, we show that the recombination dynamics in porous Silicon is due to a dispersive diffusion of excitons in a disordered arrangement of interconnected Si quantum dots. The simulated luminescence decay for the excitons shows a stretched exponential lineshape while for uncorrelated electron-hole pairs a power law decay is suggested. Our results successfully account for the recombination dynamics recently observed in the experiments. The present model is a prototype for a larger class of models describing diffusion of particles in a complex disordered system.Comment: 33 pages, RevTeX, 19 figures available on request to [email protected]

A variational approach to quantum gated recurrent units

Quantum Recurrent Neural Networks are receiving an increased attention thanks to their enhanced generalization capabilities in time series analysis. However, their performances were bottlenecked by long training times and unscalable architectures. In this paper, we propose a novel Quantum Recurrent Neural Network model based on Quantum Gated Recurrent Units. It uses a learnable Variational Quantum Layer to process temporal data, interspersed with two classical layers to properly match the dimensionality of the input and output vectors. Such an architecture has fewer quantum parameters than existing Quantum Long Short-Term Memory models. Both the quantum networks were evaluated on periodic and real-world time series datasets, together with the classical counterparts. The quantum models exhibited superior performances compared to the classical ones in all the test cases. The Quantum Gated Recurrent Units outperformed the Quantum Long Short-Term Memory network despite having a simpler internal configuration. Moreover, the Quantum Gated Recurrent Units network demonstrated to be about 25% faster during the training and inference procedure over the Quantum Long Short-Term Memory. This improvement in speed comes with one less quantum circuit to be executed, suggesting that our model may offer a more efficient alternative for implementing Quantum Recurrent Neural Networks on both simulated and real quantum hardware

EVALUATION OF HIGH-TEMPERATURE TENSILE PROPERTIES OF HEAT-TREATED ALSI10MG ALLOY PRODUCED BY LASER-BASED POWDER BED FUSION

The AlSi10Mg alloy is widely used to produce complex-shaped components by Laser-based Powder Bed Fusion (L-PBF); these parts, characterized by light structures and high specific strength, are currently employed in high-performance room temperature applications in the automotive and aerospace industries. However, it is important to increase the data concerning the high-temperature mechanical properties of the L-PBF AlSi10Mg alloy to spread its use. This study aims to fulfill the lack of knowledge by investigating the mechanical behavior at 200 °C, a representative condition of the average temperature of engine heads, of the L-PBF AlSi10Mg alloy subjected to a T5 heat treatment (artificial aging at 160 °C for 4 h) and an innovative T6 heat treatment (solubilization at 510 °C for 10 min and artificial aging at 160 °C for 6 h). The influence of high temperatures on the mechanical behavior of the L-PBF AlSi10Mg alloy was assessed by tensile tests, while microstructural and fractographic analyses were carried out to correlate the mechanical behavior of the alloy to its microstructure, and consequently explain the failure mechanisms. The ultrafine cellular microstructure, characterizing the T5 alloy, led to higher tensile strength than the homogeneous composite-like microstructure of the T6 alloy, which makes it very interesting for future application in the automotive and aerospace industries

A General Approach to Dropout in Quantum Neural Networks

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks