Metallographic Methods for Revealing the Multiphase Microstructure of TRIP-Assisted Steels - some applications for steels

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

https://www.mdpi.com/1424-8220/19/23/5170/pdf

A Survey on Machine-Learning Techniques for UAV-Based Communications

Decision Forests For Computer Vision And Medical Image Analysis

Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms

Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless and real-time transfer of data, are imperative infrastructural advancements to realize the emerging smart world. The sensor-generated data are, however, vulnerable to anomalies caused due to faults, errors, and/or cyberattacks, which may cause accidents resulting in fatal casualties. To help in avoiding such situations by timely detecting anomalies, this study proposes an anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN. The data streams, in the proposed method, are converted into vectors and then processed for anomaly detection. We also designed a method, namely, weight-adjusted fine-tuned ensemble: WAVED, which works on the principle of average predicted probability of multiple classifiers to detect anomalies in CAVs and benchmark the performance of the MSALSTM-CNN method. The MSALSTM-CNN method effectively enhances the anomaly detection rate in both low and high magnitude cases of anomalous instances in the dataset with the gain of up to 2.54% in F-score for detecting different single anomaly types. The method achieves the gain of up to 3.24% in F-score in the case of detecting mixed anomaly types. The experiment results show that the MSALSTM-CNN method achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.

Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network

We propose an improved random forest classifier that performs classification with minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark datasets. We further use the proposed classifier to detect mitotic nuclei in the histopathological datasets of breast tissues. We also apply our method on the industrial dataset of dual phase steel microstructures to classify different phases. Results of our method on different datasets show significant reduction in average classification error compared to a number of competing methods.

Improved Random Forest for Classification.

https://www.repository.cam.ac.uk/bitstream/1810/291882/1/wear_paper_June2019.pdf

Role of fracture toughness in impact-abrasion wear.

Many abrasion resistant steels rely on a martensitic microstructure to ensure hardness, which in general correlates with better wear performance. However, in practice the steel may be subjected to ...

https://tandfonline.com/doi/pdf/10.1080/02670836.2019.1615669

Metallurgical aspects of steels designed to resist abrasion, and impact-abrasion wear

The current work demonstrates the microstructural evolution and mechanical property evaluation of a newly designed steel composition after hot rolling in laboratory-scale rolling mill, followed by continuous cooling. The steel thus developed has typically about 80% carbide-free bainite; about 20% retained austenite and can deliver ∼1400 MPa ultimate tensile strength along with more than 20% total elongation. The presence of ultra-fine bainite plates (∼100–130 nm thick) with high dislocation density was thought to be responsible for ultra-high strength. Excellent ductility at such strength level could be due to the presence of sufficient amount of retained austenite (∼20%) thermally stable at room temperature but starts transforming to martensite during deformation exhibiting transformation-induced plasticity effect.

Hot-rolled and continuously cooled bainitic steel with good strength-elongation combination

Proportions of different phases (phase fraction) in the microstructures determine the quality of dual phase (DP) steel. So, calculation of phase fraction in the microstructures of steel samples is important for quality assurance. Manual calculation of phase fraction involves Le Pera
 etching of steel which is time consuming and dependent on operator efficiency. Calculation of phase fraction from Le Pera
 etched samples requires cumbersome manual observations. Nital
 etching is a faster alternative to Le Pera
 etching. However, due to lack of visually discriminative information, different phases cannot be identified manually from nital
 images. We propose a novel method for automatic calculation of phase fractions in steel microstructures from nital
 images using machine learning techniques. We show that regional contour patterns and local entropy (which cannot be evaluated manually) of regions of nital
 images are related to the formation process of the phases. We design a method that automatically evaluates regional contour patterns and local entropy from nital
 images of DP steel. Subsequently, we construct a random forest classifier that uses regional contour patterns and local entropy as features for classification of different phases. Our method is ~150 times faster than manual classification. Experiments show close to 90% accuracy in classification.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.1154

Appa Rao Chintha

Papers

Improved Random Forest for Classification.

Role of fracture toughness in impact-abrasion wear.

Metallurgical aspects of steels designed to resist abrasion, and impact-abrasion wear

Hot-rolled and continuously cooled bainitic steel with good strength-elongation combination

Calculation of phase fraction in steel microstructure images using random forest classifier