How to Cite:
Sunil Jacob Enokkaren, Avinash Attipalli, Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Varun Bitkuri, "Machine Learning Methods for Quality Assurance and Predictive Preservation in Manufacturing: A Review" International Journal of Humanities Science Innovations and Management Studies, Vol. 1, No. 1, pp. 31-42, 2024.

Abstract:
Machine Learning (ML) approaches for Predictive Maintenance (PdM) and Quality Control (QC) have become pivotal in transforming industrial operations by shifting from reactive to proactive strategies. Leveraging extensive sensor data and advanced algorithms, these approaches enable early fault detection, optimized maintenance scheduling, and improved product quality. Interpretable Machine Learning (iML) methods enhance transparency and trust, facilitating smoother integration within existing workflows. Applications across diverse sectors such as smart grids, e-commerce, and cryogenic systems highlight significant benefits including reduced downtime, cost savings, and enhanced operational efficiency. Despite these advancements, challenges persist, including data quality issues, high implementation costs, limited expertise, and resistance to organizational change. Future directions emphasize the development of scalable, real-time, and domain-specific models tailored to heterogeneous industrial data. Emphasis on improved data preprocessing, automated feature selection, and simplified model architectures is essential for maintaining performance and usability. Moreover, integrating ML with emerging technologies like Digital Twins (DT) and the Internet of Things (IoT) can enable continuous monitoring and dynamic operational adaptation. Addressing these challenges and opportunities will pave the way for more intelligent, resilient, and efficient manufacturing systems.

Keywords: Predictive Maintenance, Quality Control, Machine Learning, Interpretable Machine Learning, Industrial Iot, Digital Twins, Manufacturing Efficiency.

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