Effect of alternative microstructures on the wear mechanism of alloys used in the automotive components production

L’usura è un fenomeno complesso che si instaura tra le superfici dei corpi a contatto ed in moto relativo provocando un progressivo decadimento delle prestazioni dell’accoppiamento e una dissipazione di energia per attrito, oltre a possibili danneggiamenti superficiali con generazione di frammenti o detriti di usura. Diversi componenti automobilistici sono soggetti ad usura durante il loro servizio, ed una loro errata progettazione porta nel tempo a malfunzionamenti e inoltre può essere fonte di emissione di particolato nell’ambiente, come per esempio nel caso dell’accoppiamento tra disco freno e pastiglia. L’obiettivo della presente attività di ricerca è costituito dall’individuazione di possibili soluzioni volte a promuovere un miglioramento della resistenza ad usura di alcuni componenti impiegati nel settore automobilistico, basate su modifiche microstrutturali o di composizione delle leghe sotto indagine, nonché sull’applicazione di rivestimenti superficiali.Wear is a complex phenomenon that occurs between the surfaces of bodies in contact and relative motion, causing a progressive decline in coupling performance and a dissipation of energy by friction, as well as possible surface damage with the generation of wear fragments. Various automotive components undergo wear during their service, and their incorrect design leads to malfunctions over time and can also be a source of particulate emissions in the environment, as in the case of the coupling between the brake disc and pad. The aim of this research activity is the identification of possible solutions aimed at promoting an improvement in the wear resistance of some components used in the automotive sector, based on microstructural or chemical composition modifications of the alloys under investigation, as well as on the application of surface coatings

Variational Sparse Coding

Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications in data visualisation, clustering and artificial data synthesis. We propose a model based on variational auto-encoders (VAEs) in which interpretation is induced through latent space sparsity with a mixture of Spike and Slab distributions as prior. We derive an evidence lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standard VAE approaches when an estimate of the number of true sources of variation is not available and objects display different combinations of attributes. Furthermore, the new model provides unique capabilities, such as recovering feature exploitation, synthesising samples that share attributes with a given input object and controlling both discrete and continuous features upon generation

Early cross-sectional imaging following open and laparoscopic cholecystectomy : a primer for radiologists

Abstract: Performed on either an elective or urgent basis, cholecystectomy currently represents the most common abdominal operation due to the widespread use of laparoscopy and the progressively expanded indications. Compared to traditional open surgery, laparoscopic cholecystectomy minimised the duration of hospitalisation and perioperative mortality. Albeit generally considered safe, cholecystectomy may result in adverse outcomes with non-negligible morbidity. Furthermore, the incidence of worrisome haemorrhages and biliary complications has not been influenced by the technique shift. Due to the growing medico-legal concerns and the vast number of cholecystectomies, radiologists are increasingly requested to investigate recently operated patients. Aiming to increase familiarity with post-cholecystectomy cross-sectional imaging, this paper provides a brief overview of indications and surgical techniques and illustrates the expected early postoperative imaging findings. Afterwards, most iatrogenic complications following open, converted, laparoscopic and laparo-endoscopic rendezvous cholecystectomy are reviewed with examples, including infections, haematoma and active bleeding, residual choledocholithiasis, pancreatitis, biliary obstruction and leakage. Multidetector computed tomography (CT) represents the \u201cworkhorse\u201d modality to rapidly investigate the postoperative abdomen in order to provide a reliable basis for an appropriate choice between conservative, interventional or surgical treatment. Emphasis is placed on the role of early magnetic resonance cholangiopancreatography (MRCP) and additional gadoxetic acid-enhanced MRCP to provide a non-invasive anatomic and functional assessment of the operated biliary tract. Teaching Points: \u2022 Having minimised perioperative mortality and hospital stay, laparoscopy has now become the first-line approach to performing cholecystectomy, even in patients with acute cholecystitis. \u2022 Laparoscopic, laparo-endoscopic rendezvous, converted and open cholecystectomy remain associated with non-negligible morbidity, including surgical site infections, haemorrhage, residual lithiasis, pancreatitis, biliary obstruction and leakage. \u2022 Contrast-enhanced multidetector computed tomography (CT) is increasingly requested early after cholecystectomy and represents the \u201cworkhorse\u201d modality that rapidly provides a comprehensive assessment of the operated biliary tract and abdomen. \u2022 Magnetic resonance cholangiopancreatography (MRCP) is the best modality to provide anatomic visualisation of the operated biliary tract and is indicated when biliary complications are suspected. \u2022 Additional gadoxetic acid (Gd-EOB-DTPA)-enhanced MRCP non-invasively provides functional biliary assessment, in order to confirm and visualise bile leakage

Multidetector CT of expected findings and complications after contemporary inguinal hernia repair surgery

Inguinal hernia repair (IHR) with prosthetic mesh implantation is the most common procedure in general surgery, and may be performed using either an open or laparoscopic approach. This paper provides an overview of contemporary tension-free IHR techniques and materials, and illustrates the expected postoperative imaging findings and iatrogenic injuries. Emphasis is placed on multidetector CT, which represents the ideal modality to comprehensively visualize the operated groin region and deeper intra-abdominal structures. CT consistently depicts seroma, mesh infections, hemorrhages, bowel complications and urinary bladder injuries, and thus generally provides a consistent basis for therapeutic choice. Since radiologists are increasingly requested to investigate suspected iatrogenic complications, this paper aims to provide an increased familiarity with early CT studies after IHR, including complications and normal postoperative appearances such as focal pseudolesions, in order to avoid misinterpretation and inappropriate management

Variational learning for inverse problems

Machine learning methods for solving inverse problems require uncertainty estimation to be reliable in real settings. While deep variational models offer a computationally tractable way of recovering complex uncertainties, they need large supervised data volumes to be trained, which in many practical applications requires prohibitively expensive collections with specific instruments. This thesis introduces two novel frameworks to train variational inference models for inverse problems, in semi-supervised and unsupervised settings respectively. In the former, a realistic scenario is considered, where few experimentally collected supervised data are available, and analytical models from domain expertise and existing unsupervised data sets are leveraged in addition to solve inverse problems in a semi-supervised fashion. This minimises the supervised data collection requirements and allows the training of effective probabilistic recovery models relatively inexpensively. This novel method is first evaluated in quantitative simulated experiments, testing performance in various controlled settings and compared to alternative techniques. The framework is then implemented in several real world applications, spanning imaging, astronomy and human-computer interaction. In each real world setting, the novel technique makes use of all available information for training, whether this is simulations, data or both, depending on the task. In each experimental scenario, state of the art recovery and uncertainty estimation were demonstrated with reasonably limited experimental collection efforts for training. The second framework presented in this thesis approaches instead the challenging unsupervised situation, where no examples of ground-truths are available. This type of inverse problem is commonly encountered in data pre-processing and information retrieval. A variational framework is designed to capture the solution space of inverse problem by using solely an estimate of the observation process and large ensembles of observations examples. The unsupervised framework is tested on data recovery tasks under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. Furthermore, higher classification accuracy after imputation are shown, proving the advantage of propagating uncertainty to downstream tasks with the new model

The role of late photons in diffuse optical imaging

The ability to image through turbid media such as organic tissues, is a highly attractive prospect for biological and medical imaging. This is challenging however, due to the highly scattering properties of tissues which scramble the image information. The earliest photons that arrive at the detector are often associated with ballistic transmission, whilst the later photons are associated with complex paths due to multiple independent scattering events and are therefore typically considered to be detrimental to the final image formation process. In this work we report on the importance of these highly diffuse, "late" photons for computational time-of-flight diffuse optical imaging. In thick scattering materials, >80 transport mean free paths, we provide evidence that including late photons in the inverse retrieval enhances the image reconstruction quality. We also show that the late photons alone have sufficient information to retrieve images of a similar quality to early photon gated data. This result emphasises the importance in the strongly diffusive regime discussed here, of fully time-resolved imaging techniques.Comment: 17 pages, 5 figure

Variational Sparse Coding

Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications in data visualisation, clustering and artificial data synthesis. We propose a model based on variational auto-encoders (VAEs) in which interpretation is induced through latent space sparsity with a mixture of Spike and Slab distributions as prior. We derive an evidence lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standard VAE approaches when an estimate of the number of true sources of variation is not available and objects display different combinations of attributes. Furthermore, the new model provides unique capabilities, such as recovering feature exploitation, synthesising samples that share attributes with a given input object and controlling both discrete and continuous features upon generation

Detection and tracking of moving objects hidden from view

The ability to detect motion and track a moving object hidden around a corner or behind a wall provides a crucial advantage when physically going around the obstacle is impossible or dangerous. Previous methods have demonstrated that it is possible to reconstruct the shape of an object hidden from view. However, these methods do not enable the tracking of movement in real time. We demonstrate a compact non-line-of-sight laser ranging technology that relies on the ability to send light around an obstacle using a scattering floor and then detect the return signal from a hidden object within only a few seconds of acquisition time. By detecting this signal with a single-photon avalanche diode (SPAD) camera, we follow the movement of an object located a metre away from the camera with centimetre precision. We discuss the possibility of applying this technology to a variety of real-life situations in the near future

Wear Behavior of AlSi10Mg Alloy Produced by Laser-Based Powder Bed Fusion and Gravity Casting

Herein, the sliding wear behavior of AlSi10Mg samples realized using laser‐based powder bed fusion (LPBF) is investigated via pin‐on‐disc (PoD) tests, before and after T6 heat treatment. The changes in the microstructure, density, and hardness induced by heat treatment are correlated with the tribological behavior of the alloy. Furthermore, short wear tests are conducted and the resulting wear tracks are investigated through scanning electron microscopy (SEM), equipped with an energy‐dispersive spectroscopy (EDS) microprobe to elucidate how the wear mechanisms evolve with sliding distance. For comparison, gravity cast (GC) AlSi10Mg samples are also characterized and tested. The as‐built additive manufacturing (AM) sample exhibits the lowest wear rate and coefficient of friction because of its high hardness and relative density, whereas the heat‐treated sample shows the worst behavior in comparison with the GC samples. The results suggest a significant influence of porosity on the wear behavior of AM alloys