Machine learning algorithms for predictive maintenance in milling operations

Pranav Sutar, Santosh Kr. Mishra, Gaurav Kolte, Prathmesh Kadam, Ram Krishna Upadhyay,
Machine learning algorithms for predictive maintenance in milling operations,
Next Research,
Volume 2, Issue 4,
2025,
100933,
ISSN 3050-4759,
https://doi.org/10.1016/j.nexres.2025.100933.
(https://www.sciencedirect.com/science/article/pii/S3050475925008000)
Abstract: Predictive maintenance (PdM) has emerged as a crucial strategy in milling machine operations, aiming to predict equipment failures before they occur, thus minimizing downtime and optimizing production schedules. This research conceptualizes the role of modern-day technologies, particularly Machine Learning (ML) and artificial intelligence (AI), and implements ML models for accurate prediction to enhance PdM strategies for milling machines. This research also studies the set of parameters involved in the failure of the milling machine (milling cutter, to be specific). Finally, it rearranges the parameters based on their criticality or role in the deterioration of the milling machines. PdM models can accurately forecast potential equipment failures, enabling proactive maintenance interventions by leveraging insights from prior studies, sensor technology advancements, and real-time monitoring. Collaborative efforts between PdM and milling operations management disciplines can further enhance manufacturing efficiency by integrating PdM data directly into milling machine operations. The research highlights the potential benefits of PdM, including cost savings, enhanced sustainability, and improved operational efficiency, while also addressing challenges such as technology implementation and data privacy concerns. Ultimately, the future of PdM in milling machine operations holds promise for revolutionizing manufacturing practices, optimizing resource utilization, and advancing sustainability initiatives for a more resilient industry.
Keywords: Predictive Maintenance (PdM); Machine Learning (ML); Artificial Intelligence (AI); Design of Parametric Experiments (DoE)