The Effect of Ti Addition on Microstructure and Magnetic Properties of Nanocrystalline FeAl40 Alloy Powders Prepared by Mechanical Alloying

Powder Metallurgy and Metal Ceramics - Recent research on nanocrystalline FeAl alloys has shown that these alloys are of high importance due to their promising structural and mechanical properties,...     

Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric

Journal of Intelligent Manufacturing - In order to manufacture products at low cost, machine learning (ML) is increasingly used in production, especially in high wage countries. Therefore, we...     

2.6 μm GaSB based photonic crystal coupled cavity lasers

Long wavelength GaInAsSb/AlGaAsSb quantum wells lasers have been grown by molecular beam epitaxy and processed into ridge cavities coupled by an intracavity photonic crystal mirror, to enhance the laser spectral properties. The devices operate in the continuous-wave regime at room temperature with a single frequency emission at 2.6 m.     

MRI-based dynamic tracking of an untethered ferromagnetic microcapsule navigating in liquid

The propulsion of ferromagnetic objects by means of MRI gradients is a promising approach to enable new forms of therapy. In this work, necessary techniques are presented to make this approach work. This includes path planning algorithms working on MRI data, ferromagnetic artifact imaging and a tracking algorithm which delivers position feedback for the ferromagnetic objects, and a propulsion sequence to enable interleaved magnetic propulsion and imaging. Using a dedicated software environment, integrating path-planning methods and real-time tracking, a clinical MRI system is adapted to provide this new functionality for controlled interventional targeted therapeutic applications. Through MRI–based sensing analysis, this article aims to propose a framework to plan a robust pathway to enhance the navigation ability to reach deep locations in the human body. The proposed approaches are validated with different experiments.     

Plasma impedance monitoring for real time endpoint detection of bulk materials etched in ICP tool

Plasma impedance monitoring (PIM) based on electrical measurements is successfully used as an alternative to determine real time detection endpoint during plasma etching of structured bulk materials. In this paper we present the results with this technique for the endpoint detection during the etching of various materials.The endpoint conditions are tested in the sixth harmonic components of the electrical plasma parameters with an RF sensor. The endpoint is determined when an electrical parameter transition is observed. This transition corresponds to the change of the total reactor impedance, and allows the etching of the doped layer to stop on the bulk substrate.Using a Smith chart we determine the best harmonics/electrical monitoring couple parameters for processes on various materials. Resistivity measurements are used before and after etching in order to confirm the usefulness of the PIM method.In this paper, we also demonstrate how to monitor a real time control of non-uniformity during the reactive ion etching (RIE) process in the case of gallium arsenide etching.     

Correlation Between the Pitting Potential Evolution and $$\varvec\sigma$$ Phase Precipitation Kinetics in the 2205 Duplex Stainless Steel

The aim of this work is to correlate the pitting potential (E_pit) evolution with the kinetics of σ phase precipitation in the 2205 duplex stainless steel aged at 850 °C after solution treatment at 1150 °C. The potentiodynamic polarization curves indicate a reduction of the pitting corrosion resistance with the aging time, which is revealed by a decrease in the E_pit values from 0.65 to 0.40 V_SCE. Thus, E_pit values are used to determine the kinetics parameters of the σ phase precipitation. The experimental transformed fraction agrees well with the one calculated by using the modified Kolmogorov–Johnson–Mehl–Avrami equation with an impingement parameter c?=?0.6.     

Effective thermoelastic properties of composites with temperature-dependent constituents

Effective thermoelastic properties—elasticities, thermal expansions, heat capacities—of composites with temperature-dependent constituents are derived all at the same time using a unified thermodynamic treatment within Kovalenko’s thermoelasticity theory, which assumes the strain to be small but places no restrictions on temperature variations. Known results on elasticities and thermal expansions are rederived in a new way and new results on thermal expansions and heat capacities are obtained. In addition, the temperature-independent framework is revisited and expressions for the effective properties are obtained that are free of the restrictions placed on temperature variations within linear thermoelasticity.     

A Unified Artificial Neural Network Architecture for Active Power Filters

In this paper, an efficient and reliable neural active power filter (APF) to estimate and compensate for harmonic distortions from an AC line is proposed. The proposed filter is completely based on Adaline neural networks which are organized in different independent blocks. We introduce a neural method based on Adalines for the online extraction of the voltage components to recover a balanced and equilibrated voltage system, and three different methods for harmonic filtering. These three methods efficiently separate the fundamental harmonic from the distortion harmonics of the measured currents. According to either the Instantaneous Power Theory or to the Fourier series analysis of the currents, each of these methods are based on a specific decomposition. The original decomposition of the currents or of the powers then allows defining the architecture and the inputs of Adaline neural networks. Different learning schemes are then used to control the inverter to inject elaborated reference currents in the power system. Results obtained by simulation and their real-time validation in experiments are presented to compare the compensation methods. By their learning capabilities, artificial neural networks are able to take into account time-varying parameters, and thus appreciably improve the performance of traditional compensating methods. The effectiveness of the algorithms is demonstrated in their application to harmonics compensation in power systems