Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 21
DOI: https://doi.org/10.59646/819/10
Author: Ms. I. Reshma Monica
Abstract
Artificial Intelligence (AI) is transforming the manufacturing sector by enabling predictive maintenance, intelligent process optimization, and data-driven decision-making. This study examines the application of AI technologies, including machine learning, deep learning, and predictive analytics, to improve equipment reliability and manufacturing efficiency. AI-based predictive maintenance analyzes real-time sensor and operational data to identify potential equipment failures before they occur, thereby reducing unplanned downtime and maintenance costs. In addition, AI-driven process optimization helps manufacturers improve production scheduling, resource utilization, product quality, and energy efficiency. The study highlights how intelligent systems can detect operational patterns, identify process deviations, and recommend appropriate corrective actions. The findings indicate that integrating AI into manufacturing processes can enhance productivity, minimize waste, improve equipment utilization, and support more sustainable production. However, challenges such as data quality, system integration, cybersecurity, implementation costs, and the need for skilled personnel must be addressed for successful adoption. Overall, AI provides significant opportunities for developing smarter, more efficient, reliable, and adaptive manufacturing environments.
Keywords: Artificial Intelligence, Manufacturing, Predictive Maintenance, Process Optimization, Machine Learning, Deep Learning, Predictive Analytics, Smart Manufacturing, Industrial Automation, Industry 4.0