scholarly journals The Effect of the Thermal Annealing Process to the Sensing Performance of Magnetoelastic Ribbon Materials

2021 ◽  
Vol 13 (24) ◽  
pp. 13947
Author(s):  
Georgios Samourgkanidis ◽  
Kostantis Varvatsoulis ◽  
Dimitris Kouzoudis
Keyword(s):  
Thermal Annealing ◽  
Magnetic Hysteresis ◽  
Environmental Parameters ◽  
Bias Field ◽  
Annealing Process ◽  
Signal Enhancement ◽  
Internal Stresses ◽  
Practical Applications ◽  
Magnetoelastic Materials ◽  
Material Energy

The magnetoelastic materials find many practical applications in everyday life like transformer cores, anti-theft tags, and sensors. The sensors should be very sensitive so as to be able to detect minute quantities of miscellaneous environmental parameters, which are very critical for sustainability such as pollution, air quality, corrosion, etc. Concerning the sensing sensitivity, the magnetoelastic material can be improved, even after its production, by either thermal annealing, as this method relaxes the internal stresses caused during manufacturing, or by applying an external DC magnetic bias field during the sensing operation. In the current work, we performed a systematic study on the optimum thermal annealing parameters of magnetoelastic materials and the Metglas alloy 2826 MB3 in particular. The study showed that a 100% signal enhancement can be achieved, without the presence of the bias field, just by annealing between 350 and 450 °C for at least half an hour. A smaller signal enhancement of 15% can be achieved with a bias field but only at much lower temperatures of 450 °C for a shorter time of 20 min. The magnetic hysteresis measurements show that during the annealing process, the material reorganizes itself, changing both its anisotropy energy and magnetostatic energy but in such a way such that the total material energy is approximately conserved.

scholarly journals Tuning the hierarchical pore structure of graphene oxide through dual thermal activation for high-performance supercapacitor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeongpil Kim ◽  
Jeong-Hyun Eum ◽  
Junhyeok Kang ◽  
Ohchan Kwon ◽  
Hansung Kim ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Pore Structure ◽  
Specific Capacitance ◽  
Surface Area ◽  
Thermal Annealing ◽  
High Performance ◽  
Annealing Process ◽  
Supercapacitor Electrode ◽  
Simple Method ◽  
Surface Areas

AbstractHerein, we introduce a simple method to prepare hierarchical graphene with a tunable pore structure by activating graphene oxide (GO) with a two-step thermal annealing process. First, GO was treated at 600 °C by rapid thermal annealing in air, followed by subsequent thermal annealing in N2. The prepared graphene powder comprised abundant slit nanopores and micropores, showing a large specific surface area of 653.2 m2/g with a microporous surface area of 367.2 m2/g under optimized conditions. The pore structure was easily tunable by controlling the oxidation degree of GO and by the second annealing process. When the graphene powder was used as the supercapacitor electrode, a specific capacitance of 372.1 F/g was achieved at 0.5 A/g in 1 M H2SO4 electrolyte, which is a significantly enhanced value compared to that obtained using activated carbon and commercial reduced GO. The performance of the supercapacitor was highly stable, showing 103.8% retention of specific capacitance after 10,000 cycles at 10 A/g. The influence of pore structure on the supercapacitor performance was systematically investigated by varying the ratio of micro- and external surface areas of graphene.

Robot obstacle avoidance system using deep reinforcement learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaojun Zhu ◽  
Yinghao Liang ◽  
Hanxu Sun ◽  
Xueqian Wang ◽  
Bin Ren
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Environmental Parameters ◽  
Working Environment ◽  
Content Type ◽  
Practical Applications ◽  
Human Operators

Purpose Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem. Design/methodology/approach In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment. Findings The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace. Research limitations/implications The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace. Originality/value This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

Non-Stationary Estimation of Joint Design Criteria With a Multivariate Conditional Extremes Approach

2016 ◽  
Author(s):  
Laks Raghupathi ◽  
David Randell ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Structural Reliability ◽  
Structural Response ◽  
Environmental Parameters ◽  
Joint Estimation ◽  
Current Profile ◽  
Practical Applications ◽  
Significant Wave ◽  
Northern North Sea

Understanding the interaction of ocean environments with fixed and floating structures is critical to the design of offshore and coastal facilities. Structural response to environmental loading is typically the combined effect of multiple environmental parameters over a period of time. Knowledge of the tails of marginal and joint distributions of these parameters (e.g. storm peak significant wave height and associated current) as a function of covariates (e.g. dominant wave and current directions) is central to the estimation of extreme structural response, and hence of structural reliability and safety. In this paper, we present a framework for the joint estimation of multivariate extremal dependencies with multi-dimensional covariates. We demonstrate proof of principle with a synthetic bi-variate example with two covariates quantified by rigorous uncertainty analysis. We further substantiate it using two practical applications (associated current given significant wave height for northern North Sea and joint current profile for offshore Brazil locations). Further applications include the estimation of associated criteria for response-based design (e.g., TP given HS), extreme current profiles with depth for mooring and riser loading, weathervaning systems with non-stationary effects for the design of FLNG/FPSO installations, etc.

scholarly journals The Magnetoelectric Effect of a Ni0.3Zn0.62Cu0.08Fe2O4 - PbFe0.5Nb0.5O3 Multilayer Composite

2014 ◽  
Vol 59 (3) ◽  
pp. 1011-1015
Author(s):  
P. Guzdek ◽  
M. Sikora ◽  
Ł. Góra ◽  
Cz. Kapusta
Keyword(s):  
Magnetic Field ◽  
Magnetoelectric Effect ◽  
Magnetic Hysteresis ◽  
Tape Casting ◽  
Casting Process ◽  
Layered Composites ◽  
Practical Applications ◽  
Magnetoelectric Composite ◽  
Sinusoidal Magnetic Field ◽  
Magnetoelectric Coefficient

Abstract The magnetoelectric effect in multiferroic materials has been widely studied for its fundamental interest and practical applications. The magnetoelectric effect observed for single phase materials like Cr2O3, BiFeO3, and Pb(Fe0.5Nb0.5)O3 is usually small. A much larger effect can be obtained in composites consisting of magnetostrictive and piezoelectric phases. This paper investigates the magnetoelectric effect of a multilayer (laminated) structure consisting of 6 nickel ferrite and 7 PFN relaxor layers. It describes the synthesis and tape casting process for Ni0.3Zn0.62Cu0.08Fe2O4 ferrite and relaxor PbFe0.5Nb0.5O3 (PFN). Magnetic hysteresis, ZFC - FC curves and dependencies of magnetization versus temperature for PFN relaxor and magnetoelectric composite were measured with a vibrating sample magnetometer (VSM) in an applied magnetic field up to 85 kOe at a temperature range of 10 – 400 K. Magnetoelectric effect at room temperature was investigated as a function of a static magnetic field (0.3 - 6.5 kOe) and the frequency of sinusoidal magnetic field (0.01 - 6.5 kHz). At lower magnetic field, the magnetoelectric coefficient increases slightly before reaching a maximum and then decreases. The magnetoelectric coefficient aME increases continuously as the frequency is raised, although this increase is less pronounced in the 1-6.5 kHz range. Maximum values of the magnetoelectric coefficient attained for the layered composites exceed about 50 mV/(Oe cm).

Exchange Bias Properties in Bulk Ni45Co5Mn38Sn12 Heusler Alloy

2014 ◽  
Vol 875-877 ◽  
pp. 272-276 ◽  
Author(s):  
Chao Jing ◽  
Ye Jun Yang ◽  
Dong Hua Yu ◽  
Zhe Li ◽  
Xiao Long Wang ◽  
...  
Keyword(s):  
Hysteresis Loop ◽  
Exchange Bias ◽  
Heusler Alloy ◽  
Magnetic Hysteresis ◽  
Hysteresis Loops ◽  
Bias Field ◽  
Magnetic Hysteresis Loop ◽  
Zero Field ◽  
Co Substitution ◽  
Exchange Bias Field

We report the exchange bias properties in the bulk Ni45Co5Mn38Sn12quaternary Heusler alloy. The ferromagnetic (FM) –antiferromagnetic (AFM) interactions get reinforced after the Co substitution for Ni in the Ni-Mn-Sn alloy, which increase the exchange bias field (HE). A maximum shift in hysteresis loops of 306 Oe was observed in the 10 kOe field cooled sample. The origin of this large exchange bias field has been discussed. Magnetic hysteresis loop obtained in the zero field cooled (ZFC) mode shows double-shifted loop, and the reason of this phenomenon has been explained in detail.

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