Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions

Abstract

The emergence of Industry 4.0 (I4.0) has significantly transformed manufacturing landscapes, introducing interconnected, dynamic, and data-rich environments. This article focuses on the application of industrial machine learning (I-ML) within these evolving manufacturing contexts, exploring both the challenges and future prospects of its integration. A systematic literature review, following the preferred reporting items for systematic reviews and meta-analyzes (PRISMA) guidelines, forms the foundation of our analysis, characterizing the role of machine learning (ML) in modern manufacturing, its current challenges, and future trends. This research delves into the implications of I-ML in various manufacturing scenarios, including predictive maintenance, anomaly detection, and quality control, providing a comprehensive overview of practical applications along with an identification of related emerging technologies and trends. We also address the critical need for sustainable, reproducible, and reliable performance in industrial applications and explore strategies for overcoming barriers to ML adoption in the industry. Recommendations for future research directions are provided, aiming to bridge the gap between ML advancements and their practical, scalable implementation in industrial settings, paving the way to future research in the field. Lastly, we aim to contribute to the identification of challenges and future research directions for the ongoing digital transformation of manufacturing industries, offering insights into how ML can be effectively leveraged in the era of I4.0.