  Currently, the growth of material data from experiments and simulations is
expanding beyond processable amounts. This makes the development of new
data-driven methods for the discovery of patterns among multiple lengthscales
and time-scales and structure-property relationships essential. These
data-driven approaches show enormous promise within materials science. The
following review covers machine learning applications for metallic material
characterization. Many parameters associated with the processing and the
structure of materials affect the properties and the performance of
manufactured components. Thus, this study is an attempt to investigate the
usefulness of machine learning methods for material property prediction.
Material characteristics such as strength, toughness, hardness, brittleness or
ductility are relevant to categorize a material or component according to their
quality. In industry, material tests like tensile tests, compression tests or
creep tests are often time consuming and expensive to perform. Therefore, the
application of machine learning approaches is considered helpful for an easier
generation of material property information. This study also gives an
application of machine learning methods on small punch test data for the
determination of the property ultimate tensile strength for various materials.
A strong correlation between small punch test data and tensile test data was
found which ultimately allows to replace more costly tests by simple and fast
tests in combination with machine learning.
