Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Effect of Element Addition, Microstructure Characteristics, Mechanical Properties, Machining and Welding Processes of the Hadfield Austenitic Manganese Steel

Zellagui Rihab, Hemmouche Larbi, Ait-Sadi Hassiba, Chelli Amel

Archives of Metallurgy and Materials

2022

Vol. 67, iss. 3

863--868

High manganese steel, also called Hadfield steel, is an alloy essentially made up of iron, carbon, and manganese. This type of steel occupies an important place in the industry. It possesses high impact toughness and high resistance against abrasive wear and hardens considerably during work hardening. The problem with this kind of steel is the generation of carbides at the grain boundaries after the casting. However, heat treatment at the high-temperature range between 950°C and 1150°C followed by rapid quenching in water is proposed as a solution to remove carbides and obtain a fully austenitic structure. Under the work hardening effects, the hardness of Hadfield steel increases greatly due to the transformation of the austenite γ to martensite ε or α and mechanical twinning, which acts as an obstacle for sliding dislocations. Hot machining is the only solution to machine Hadfield steel adequately without damage of tools or changing the mechanical characteristics of the steel. The choice of welding parameters is important to prevent the formation of carbides and obtain welded steel with great characteristics. This paper aims to give an overview about Hadfield steel, element addition effect, microstructure, heat treatments, work hardening, machinability and welding processes.

Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features

Atmani Youcef, Rechak Said, Mesloub Ammar, Hemmouche Larbi

Archives of Acoustics

2020

Vol. 45, No. 2

283--295

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.