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Machine learning tools are critical facilitators for allowing material scientists and engineers to develop innovative materials, processes, and procedures more quickly. One goal of applying such methodologies in materials research is to obtain high-throughput identification and quantification of critical aspects throughout the process-structure-property-performance chain. Machine learning and statistical learning techniques are evaluated in this article in terms of their effective application to specific challenges in the field of continuous materials mechanics. They are classified according to their task type, which is either descriptive, predictive, or prescriptive, with the goal of eventually achieving identification, prediction, or even optimization of vital qualities. In the same context this paper focusing the various approaches of Machine learning in field of Mechanical engineering.
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