AN OVERVIEW ON MACHINE LEARNING FOR CHARACTERIZING MATERIALS WITH A VIEW OF PREDICTING THEIR MECHANICAL ASPECTS

Amar C

AN OVERVIEW ON MACHINE LEARNING FOR CHARACTERIZING MATERIALS WITH A VIEW OF PREDICTING THEIR MECHANICAL ASPECTS

Authors: Amar C

Unique Paper ID: 175253
Volume: 6
Issue: 1
PageNo: 1058-1065

Keywords: Machine learning Material characterization Tensile properties Ultimate tensile strength.

Abstract:
The current expansion of material data generated from experiments and simulations is surpassing manageable quantities. The advancement of innovative data-driven techniques for uncovering patterns across various length scales and time-scales, as well as structure-property relationships, is crucial. The application of these data-driven methodologies holds significant potential in the field of materials science. This review examines the applications of machine learning in the characterization of metallic materials. A multitude of parameters related to the processing and structure of materials significantly influence the properties and performance of manufactured components. This study aims to explore the effectiveness of machine learning techniques in predicting material properties. Characteristics of materials, including strength, toughness, hardness, brittleness, and ductility, play a crucial role in classifying a material or component based on its quality. In the industrial sector, conducting material tests such as tensile tests, compression tests, or creep tests frequently requires significant time and financial resources. Consequently, the utilization of machine learning approaches is regarded as beneficial for facilitating the generation of material property information. This investigation presents the application of machine learning techniques to small punch test data for the assessment of ultimate tensile strength across different materials. A significant relationship was identified between SPT data and tensile test data, which ultimately enables the substitution of more expensive tests with simpler and faster tests in conjunction with machine learning.

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ISSN: 2349-6002
Volume: 6
Issue: 1
PageNo: 1058-1065

AN OVERVIEW ON MACHINE LEARNING FOR CHARACTERIZING MATERIALS WITH A VIEW OF PREDICTING THEIR MECHANICAL ASPECTS

Available:https://ijirt.org/Article?manuscript=175253

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