A Review into Data Science and Its Approaches in Mechanical Engineering

TL;DR

This paper reviews the role of data science in mechanical engineering, highlighting its methods, applications, challenges, and future development to enhance decision-making and system optimization.

Contribution

It provides a comprehensive overview of data science methodologies and their specific applications, challenges, and development prospects in mechanical engineering.

Findings

Data science improves prediction accuracy in mechanical systems.

Integration of data science enhances decision-making processes.

Challenges include data quality and methodological adaptation.

Abstract

Nowadays it is inevitable to use intelligent systems to improve the performance and optimization of different components of devices or factories. Furthermore, it's so essential to have appropriate predictions to make better decisions in businesses, medical studies, and engineering studies, etc. One of the newest and most widely used of these methods is a field called Data Science that all of the scientists, engineers, and factories need to learn and use in their careers. This article briefly introduced data science and reviewed its methods, especially it's usages in mechanical engineering and challenges and ways of developing data science in mechanical engineering. In the introduction, different definitions of data science and its background in technology reviewed. In the following, data science methodology which is the process that a data scientist needs to do in its works been discussed. Further, some researches in the mechanical engineering area that used data science methods in their studies, are reviewed. Eventually, it has been discussed according to the subjects that have been reviewed in the article, why it is necessary to use data science in mechanical engineering researches and projects.