Industry 4.0- Applications of machine learning in the field of industrial engineering: Systematic review of the literature

Subject Areas : Application of soft computing in industrial engineering

MARCO ANTONIO DIAZ MARTINEZ ¹ (TecNM- Higher Technological Institute of Pánuco (ITSP))
REINA VERONICA ROMAN SALINAS ² (TecNM- Higher Technological Institute of Pánuco (ITSP))
SANTOS RUIZ HERNANDEZ ³ (TecNM- Higher Technological Institute of Pánuco (ITSP))
GABRIELA CERVANTES ZUBIRIAS ⁴ (Multidisciplinary Academic Unit, Reynosa-Aztlan-UAT)
MARIO ALBERTO MORALES RODRIGUEZ ⁵ (Multidisciplinary Academic Unit, Reynosa-Aztlan-UAT)

Received: 2024-04-18 Accepted : 2024-08-15 Published : 2024-07-14

Keywords: Machine Learning, Industry 4.0, Supply Chain, maintenance, artificial intelligence, Deep Learning, Additive Manufacturing,

Abstract :

The aim of this research is to determine how the implementation of machine learning has generated advantages in the field of engineering. Through a systematic review of the literature, it seeks to understand the importance of machine learning and its various applications in engineering, such as equipment maintenance, business demand forecasting, production chain optimization, customer service, and quality control. In this article, we conduct a systematic review and bibliometric analysis to explore the current state of research on machine learning and Industry 4.0 applications in the field of industrial engineering. Our goal is to identify established and emerging fields of research to guide future research. To carry out this study, we initially identified 639 scientific journal publications indexed by publishers such as Ebsco essentials, ScienceDirect, IEEEXplore, and MDPI, collected from 1 January of 2015 to May 2023. Subsequently, a group of specialists evaluated these publications, carefully selecting 65 of them that were placed in the literature review section and that were considered relevant to our analysis. In a second stage, we applied a detailed analysis using MAXQDA v.2020 software on our collected data, focusing on citation and keyword evaluation. This approach allowed us to gain a deeper understanding of trends and connections in existing research in this field.

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