Advancing Manufacturing Intelligence: A Comprehensive Analysis of AI-Driven Smart Factories and Predictive Maintenance Systems
DOI:
https://doi.org/10.32628/CSEIT25111222Keywords:
Artificial Intelligence in Manufacturing, Smart Factory Systems, Predictive Maintenance, Industry 4.0 Technologies, Manufacturing Process OptimizationAbstract
This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications. The article explores how AI-driven technologies transform traditional manufacturing processes through advanced data analytics, machine learning algorithms, and Internet of Things (IoT) integration. By examining the evolution of smart factory systems, this study investigates the role of predictive maintenance in reducing operational downtime and optimizing equipment performance. The article analysis encompasses real-time monitoring systems, automated quality control processes, and intelligent production scheduling, highlighting their collective impact on manufacturing efficiency. Through detailed case studies and empirical evidence, the article demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments. The findings reveal significant improvements in operational efficiency, maintenance scheduling, and resource utilization across various manufacturing sectors. This article contributes to the growing knowledge of Industry 4.0 technologies and provides valuable insights for manufacturers seeking to implement AI-driven solutions in their operations. The article also addresses implementation challenges and offers strategic recommendations for successful AI integration in manufacturing processes, paving the way for future developments in smart manufacturing systems.
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