Artificial Intelligence in Mechanical Systems: A Neural Network Perspective

Artificial intelligence (AI) has revolutionized various fields, including mechanical engineering. Integrating AI in mechanical systems has given rise to intelligent machines that can learn, adapt, and make decisions autonomously. This paper provides an in-depth examination of the application of artificial neural networks (ANNs) in mechanical systems, highlighting their potential benefits and challenges. We also discuss the current state of research in this field and provide examples of successful implementations. The paper explores the intersection of AI and mechanical engineering, examining the potential of ANNs to drive innovation and improvement in mechanical systems. With the increasing demand for efficient and productive mechanical systems, ANNs have become a crucial tool for mechanical engineers. This paper aims to provide a comprehensive overview of the application of ANNs in mechanical systems, highlighting their potential benefits and challenges, and exploring the current state of research in this field.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-30
DOI
https://doi.org/10.5281/zenodo.15388164
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence in Mechanical Systems: A Neural Network Perspective

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Artificial Intelligence in Mechanical Systems: A Neural Network Perspective

article en

Abstract

Artificial intelligence (AI) has revolutionized various fields, including mechanical engineering. Integrating AI in mechanical systems has given rise to intelligent machines that can learn, adapt, and make decisions autonomously. This paper provides an in-depth examination of the application of artificial neural networks (ANNs) in mechanical systems, highlighting their potential benefits and challenges. We also discuss the current state of research in this field and provide examples of successful implementations. The paper explores the intersection of AI and mechanical engineering, examining the potential of ANNs to drive innovation and improvement in mechanical systems. With the increasing demand for efficient and productive mechanical systems, ANNs have become a crucial tool for mechanical engineers. This paper aims to provide a comprehensive overview of the application of ANNs in mechanical systems, highlighting their potential benefits and challenges, and exploring the current state of research in this field.

Zenodo (CERN European Organization for Nuclear Research)
Instituto Federal de Educação, Ciência e Tecnologia do Pará (BR)
Industry, innovation and infrastructure
Openalex Percentile: Top 61%
Machine Learning in Materials Science
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Artificial Intelligence in Mechanical Systems: A Neural Network Perspective · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS