Exploring Data Science in Manufacturing Processes: Current Trends, Challenges, and Future Directions
TL;DR
This review presents a structured analysis of data modeling techniques for manufacturing applications, with emphasis on data generation, sampling, preprocessing, and modeling approaches across diverse operational regimes, including steady, transient, and generative processes.
Abstract
Data science methodologies are playing an increasingly important role in advancing manufacturing systems, enabling improvements in efficiency, energy usage, cost reduction, product quality, and predictive maintenance capabilities. This raises a fundamental question: to what extent can data models reshape manufacturing processes, and what limitations prevent their full-scale adoption? Recent developments show a growing integration of data models within digital twin and digital shadow architectures, facilitating real-time monitoring and decision-making. Nonetheless, the complexity of industrial processes and the scarcity of high-quality, well-structured datasets pose significant challenges, particularly in terms of model robustness, interpretability, and scalability. Importantly, the effectiveness of such models depends more on data quality and representativeness than on data quantity alone. This review presents a structured analysis of data modeling techniques for manufacturing applications, with emphasis on data generation, sampling, preprocessing, and modeling approaches across diverse operational regimes, including steady, transient, and generative processes.