Abstract:
Research Problem: Traditional manufacturing systems often struggle with inefficiencies, equipment downtime, and a lack of real-time adaptability. These issues stem from rigid automation that lacks intelligent decision-making capabilities.
Objectives:
• To explore how AI enhances smart manufacturing systems through predictive maintenance, quality control, and real-time optimization.
• To evaluate AI-enabled manufacturing performance metrics.
• To assess the potential impact of these technologies on operational efficiency and industry transformation.
Methods: This paper uses a combination of simulation-based experiments and case study analysis in automotive and electronics sectors. It incorporates deep learning models for fault detection and predictive maintenance.
Key Findings: The AI-integrated manufacturing system demonstrated a 30% reduction in unplanned downtime, 20% improvement in product quality, and a 25% increase in throughput compared to traditional systems.
Conclusion: The results underscore AI’s transformative potential in creating intelligent, selfoptimizing factories aligned with Industry 4.0 and paving the way for Industry 5.0.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131024