Search results

The purpose of this paper is to introduce a numerical calculation method to study the uniformity of the magnetic field in a cold crucible used for directional solidification (DS) and provide information for designing a cold crucible that can induce a uniform magnetic field.

To obtain the characteristics of the magnetic field in a cold crucible and its influence on the directional solidification processing, based on experimental verification, 3‐D finite element (FE) models with different crucible configuration‐elements and power parameters were established to study the uniformity of the magnetic field in a cold crucible. In addition, different TiAl ingots were directionally solidified with different cold crucibles, and the solid/liquid (S/L) interfaced were examined to investigate the effect of the magnetic field on the macrostructure of those ingots.

The uniformity of the magnetic field in a given domain can be quantitatively analyzed by statistical methods. Numerical calculation results showed that the uniformity of the magnetic field can be improved by optimizing the crucible configuration and adopting lower frequency. Better uniformity of the magnetic field in a cold crucible is beneficial to directional solidification.

The calculation of the uniformity of the magnetic field is proposed as a method for quantitative study of the distribution characteristics of the magnetic field in a cold crucible. The relationship between the S/L interfaces of TiAl ingots and the uniformity of the magnetic field is initially characterised; additionally, techniques for improving the uniformity of the magnetic field in a cold crucible are suggested.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.