Search results for "Machine"
showing 10 items of 2592 documents
Influence of Rotor Suspension Anisotropy on Oil Film Instability
2018
A crucial problem of turbomachinery is the oil film instability on increasing the angular speed, which is correlated with the asymmetry of the bearing stiffness matrix and resembles the hysteretic instability somehow. As a beneficial effect is exerted on the latter by the anisotropy of the support stiffness, some favorable effects have been recently found by the author also for the former, whence a systematic analysis has been undertaken. The instability thresholds may be detected by the usual conventional methods, but a detailed analysis may be carried out by closed-form procedures in the hypothesis of symmetry of the rotor-shaft-support system, which condition approaches the real working …
Virtual machine concept applied to uncertainties estimation in instrumented indentation testing
2019
The basis of the virtual machine concept, which is commonly used in coordinate measuring machines, was implemented to determine more realistic uncertainties on the estimation of the elastic modulus obtained from nanoindentation tests. The methodology is based on a mathematical model applied to simulate the testing process and to evaluate the uncertainties through Monte Carlo simulations whose application depends on the studied system (instrument, material, scale, etc.). The methodology was applied to the study of fused silica (FQ) and steel samples tested in a nanoindentation system. The results revealed that the most relevant sources of uncertainty are related to the calibration procedure,…
Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials.
2020
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is de…
Feedback Biasing Based Adjustable Gain Ultrasound Preamplifier for CMUTs in 45nm CMOS
2018
As CMOS technology is scaled down, supply voltages are decreasing and intrinsic gain of the nanoscale CMOS transistors is dropping while the threshold voltages of transistors are remaining relatively constant. In such scaled down nanoscale CMOS technologies, conventional vertical stacking architectures (for example. cascode architectures) for high-gain becomes no more attractive. In this paper we present the analysis and design of a feedback biasing based adjustable gain ultrasound preamplifier which is capable of amplifying signals from 15 MHz to 45 MHz from Capacitive Micromachined Ultrasound Transducers (CMUTs) in 45nm CMOS technology for medical ultrasound imaging applications. From the…
Influence of Post-machining Thermal Treatment on the Corrosion Behaviour of Copper
2014
Abstract In this paper, the influence of a post-machining thermal treatment (PMTT) on the corrosion behaviour of copper was investigated in a salt fog atmosphere. The corrosion behaviour was affected by the presence of a high density of grain boundaries generated during machining or dislocations formed during PMTT under certain conditions. The obtained results showed that it is possible to find PMTT conditions to cancel changes induced by machining and that the critical factor leading to a sharp increase of the percentage of oxidized surface seems to be the density of dislocations near the machined surface.
Modelling of the fracture toughness anisotropy in fiber reinforced concrete
2015
Steel fiber reinforced concrete is potentially very promising material with unique properties, which currently is widely used in some applications, such as floors and concrete pavements. However, lack of robust and reliable models of fiber reinforced concrete fracture limits its application as structural material. In this work a numerical model is proposed for predicting the crack growth in fiber reinforced concrete. The mixing of the steel fibers with the concrete usually creates nonuniform fibers distribution with more fibers oriented in horizontal direction, than in vertical. Simple numerical models of fiber reinforced concrete require a priori knowledge of the crack growth direction in …
Search of a topological pattern to evaluate toxicity of heterogeneous compounds.
2001
Abstract Molecular connectivity has been applied to the search of mathematical models able to predict the carcinogenic and teratogenic activity of a wide group of structurally heterogeneous compounds. Through the linear discriminant analysis and the diagrams of distribution of pharmacological activity, the classification criteria that minimizes the percentage of error are established. The easiness and speed of the calculation of the descriptors used in this work make the models developed useful in data bases containing a huge number of compounds.
Least-squares temporal difference learning based on an extreme learning machine
2014
Abstract Reinforcement learning (RL) is a general class of algorithms for solving decision-making problems, which are usually modeled using the Markov decision process (MDP) framework. RL can find exact solutions only when the MDP state space is discrete and small enough. Due to the fact that many real-world problems are described by continuous variables, approximation is essential in practical applications of RL. This paper is focused on learning the value function of a fixed policy in continuous MPDs. This is an important subproblem of several RL algorithms. We propose a least-squares temporal difference (LSTD) algorithm based on the extreme learning machine. LSTD is typically combined wi…
Statistical criteria for early-stopping of support vector machines
2007
This paper proposes the use of statistical criteria for early-stopping support vector machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to fourth order) become stationary, rather than according to a tolerance threshold of primal convergence itself. This simple strategy induces lower computational efforts and no significant differences are observed in terms of performance and sparsity.
Implementing some Evolutionary Computing Methods for Determining the Optimal Parameters in the Turning Process
2015
In this paper, we comparatively present two heuristics search methods – Simulated Annealing and Weighted Sum Genetic Algorithm, in order to find optimal cutting parameters in turning operation. We consider five different constraints aiming to achieve minimum total cost of machining. We developed a customizable software application in Microsoft Visual Studio with C# source code, flexible and extensible that implements the optimization methods. The experiments are based on real data gathered from S.C. “Compa” S.A Sibiu, a company that manufactures automotive components and targets improving of product quality and reducing cost and production time. The obtained results show that, although the …