Author: Nirajkumar Patel
Patel, Nirajkumar, 2024 Examining material properties of the metal with probable values calculated from the grayscale image., Flinders University, College of Science and Engineering
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This research explores the possibility of accurately determining the material properties of metals from grayscale images. The primary objective is to identify suitable material for this investigation. To achieve this goal, tensile testing and hardness testing are performed with specimens prepared from ASTM A 516 Grade 70 steel using a standard design and waterjet cutting technology. Some specimens were subjected to sandblasting, introducing controlled corrosion. Grayscale images were collected both before and after heat treatment processes, and their results were correlated with experimental data. X-ray imaging was conducted at 120 kV with 8 mA and 90 kV with 4 mA power, with a flash distance of 100cm. The heat treatment processes, including annealing at 700°C, normalising at 900°C, and quenching in water at 900°C, were applied to the tensile and hardness specimens. Subsequent X-ray imaging was performed to detect changes in material properties following the heat treatment. Round samples were tested for Vickers hardness with a 3-gram force, and a 50kN-capacity Instron machine was used for tensile testing to find out the Young's modulus. Microscopic examinations revealed the heat treatment results, and SEM (scanning electron microscope) analysis was employed to study grain structures. Ultimately, this research determines that, while MATLAB code can extract magnitude, density, and pixel values from X-ray images, it is not possible to accurately predict the material properties of metal solely based on these variables. The study confirms the success of the heat treatment processes, providing valuable insights into the limitations of image-based material property assessments.
Keywords: Grayscale, Heat treatment, Material properties, X-ray, Micrsosopic examination, Hardness Testing.
Subject: Engineering thesis
Thesis type: Masters
Completed: 2024
School: College of Science and Engineering
Supervisor: Dr Stuart Widly