Effect of Dilution on Microstructure and Wear Resistance of a Fe-Based Hardfacing Alloy with a High Amount of Carbide-Forming Elements

Hardfacing is a widely diffused technique adopted to increase service life of parts for heavy-duty applications. Even though hardfacing alloys feature optimized chemistry and microstructure for specific service conditions, dilution with substrate modifies the resulting properties along a significant fraction of the deposit thickness. In particular, C and B diffusion to the substrate alters hypereutectic alloys reducing the carbide-forming ability andmodifying the solidification sequence. In the present paper, the effect of dilution on a hypereutectic Fe-C-B based alloy containing Cr and Mo was investigated. The effect of dilution on the reference alloy was studied by producing laboratory castings with an increased amount of Fe, up to 50 mass %. The obtained results were compared with the dilution of the hardfacing alloy cast on steel substrates. The microstructural evolution was analyzed by XRD (X-ray diffraction), differential scanning calorimetry (DSC), optical microscopy (OM), and scanning electron microscopy (SEM), whereas mechanical behaviour was evaluated by hardness measurements and wear resistance by pin-on-disc tests

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample

Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices

Subspace analysis is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal processing tasks. However, traditional subspace analysis often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing subspace analysis to be run on low-power devices such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices

Low-power fixed-point compressed sensing decoder with support oracle

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches

Event-based Classification with Recurrent Spiking Neural Networks on Low-end Micro-Controller Units

Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as 4.1μJ and a worst-case computational time of 150.4μs per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks

Effetti della diluizione sulla microstruttura e comportamento ad usura di una lega Fe-C-B-Cr-Mo

Generalmente tra le leghe hardfacing a base ferro quelle ipereutettiche, composte da carburi primari in unastruttura eutettica, offrono le migliori prestazioni ad usura. L’ottimizzazione della composizione chimica di talileghe, però, deve tener conto del fenomeno della diluizione. Durante la deposizione, la diffusione di elementi dilega e la fusione del substrato possono modificare la sequenza di solidificazione della lega. Ciò porta ad unadiminuzione della frazione dei carburi primari e alla variazione delle proprietà del rivestimento.Lo scopo della ricerca è stato quello di analizzare gli effetti della diluizione. In un primo approccio la diluizioneviene simulata tramite la fusione di una lega ipereutettica Fe-C-B–Cr-Mo con aggiunte crescenti di ferro puro.Successivamente è stata analizzata la fusione della lega direttamente in crogioli di acciaio. I risultati derivanti dallasimulazione sono infine messi a confronto con quelli ottenuti dalla deposizione della lega tramite un processoindustriale di spin casting. L’evoluzione microstrutturale dopo diluizione è stata studiata tramite microscopiaottica, elettronica, diffrazione dei raggi X, misure DSC e di microdurezza, mentre la resistenza ad usura è stataanalizzata attraverso prove pin-on-disc

Replication casting of open-cell ALSi7Mg0.3 foams

A typical aluminum alloy for casting (AlSi7Mg0.3) was used to produce open-cell foams by replication of a salt precursor. The process was set to minimize complexity and costs of the casting operations: the preform sintering was avoided and mold temperature lower than the eutectic temperature of the alloy was used. Open-cell foams with a relative density about 35% and high compressive strength resulted. Material analyses showed that, in replication casting, the material response to the process is optimal and a homogeneous and fine grain size distribution is visible in the foams. (C) 2011 Elsevier B.V. All rights reserved

Effects of Er and Zr Additions on the As-Cast Microstructure and on the Solution-Heat-Treatment Response of Innovative Al-Si-Mg-Based Alloys

The microstructure of Al-Si-Mg alloys strongly depends on their chemical composition and the heat treatment they undergo during production. The influence of solution heat treatment (SHT) and the addition of Er and Zr on the microstructure of gravity-cast A356 (Al-7Si-0.4Mg) were examined. The reference as-cast microstructure is characterized by the grain size and morphology of eutectic Si, as well as the morphology, area fraction, and chemical composition of the intermetallic compounds. The morphology of eutectic Si is unstable during SHT; the evolution mechanisms can be described using thermodynamic and kinetic models and have been validated using optical and scanning electron microscope (SEM) micrographs. The effect of high-temperature exposure during SHT, on the other hand, plays a minor role on the quantity and morphology of the intermetallic compounds, as demonstrated by optical and SEM micrographs

Microstructural modification of laser-bent open-cell aluminum foams

Laser forming tests have been performed on open-cell aluminum alloy foams with different pore size. Laser power was fixed at 150 W, a total of 150 laser scans led to a bending angle up to 60°, depending on the laser scan rate. At the end of the laser bending, the foams were left to cool and samples were extracted for analysis by means of an optic microscope. The alloy microstructure was investigated in different points of the samples and correlated with the processing conditions. Image analysis was also carried out to extract the percentage of melted area due to laser heating