Smart manufacturing is currently at the forefront of the industrial revolution, driven by the rapid adoption of Industry 4.0 principles and emerging technological advancements. The integration of artificial intelligence (AI) and Internet of Things (IoT) solutions has transformed traditional manufacturing practices into interconnected, intelligent systems capable of improved decision-making, enhanced reliability, and increased operational efficiency. While technologies such as big data analytics, machine learning, advanced robotics, digital twins, and cyber-physical systems are already making significant contributions to manufacturing operations, there remain substantial challenges and gaps to address. Key debates and unresolved questions focus on the precise mechanisms of integrating AI and IoT technologies to optimize manufacturing productivity, effectively predict system failures, and enhance cybersecurity across interconnected manufacturing environments.

This Research Topic aims to consolidate the latest scholarly efforts to explore, understand, and advance the implementation of AI and IoT technologies into smart manufacturing processes. The primary objectives include investigating the effectiveness of AI-driven predictive analytics in identifying faults and downtime, overcoming implementation barriers associated with IoT technologies, and developing comprehensive frameworks for digital transformation in manufacturing scenarios. Through this exploration, researchers are encouraged to provide insights, innovative solutions, and critical evaluations that facilitate deeper understanding and wider adoption of AI-enabled IoT practices in industrial processes.

To achieve comprehensive insights, the scope of this Research Topic focuses specifically on theoretical advancements, practical applications, and case study evaluations related to AI and IoT integration into smart manufacturing. Submissions are encouraged in, but not limited to, the following areas:

• Big Data, Machine learning, AI, and IoT in manufacturing processes and industrial activities.
• Machine learning and deep learning for materials processing.
• Machine learning and deep learning approaches for fault detection and diagnosis
• Cyber-physical systems integration and applications
• Real-time condition monitoring techniques based on AI and IoT
• Advanced industrial IoT platforms for data-driven manufacturing operations Digital twin concepts
• Optimized planning and scheduling methods in smart manufacturing environments.

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Applications of Artificial Intelligence and IoT Technologies in Smart Manufacturing Vol. 2