Rapid advancements in smart manufacturing technologies have transformed modern production systems into highly connected data-rich environments. Sensors, cyber-physical systems, the industrial internet of things (IIoT), and intelligent manufacturing equipment continuously generate large amounts of operational data. The extraction of valuable information from these data streams is becoming increasingly important for improving the productivity, quality, reliability, and sustainability of manufacturing systems.
Process and machine monitoring play a crucial role in enabling the real-time observation, diagnosis, prediction, and optimization of manufacturing operations. Recent developments in data analytics, machine learning, artificial intelligence, and hybrid modeling techniques have created new opportunities to extract insights from manufacturing data and enhance decision-making capabilities.
This special issue aims to provide a forum for researchers and practitioners to present the recent advances, innovative methodologies, and industrial applications related to process & machine monitoring and data analytics for smart manufacturing. Original research and review papers addressing theoretical developments, methodologies, and practical implementations are welcome.
The topics of interest include, but are not limited to:
* Process and machine condition monitoring
* Sensor technologies and sensor fusion
* Signal processing and feature extraction
* Data analytics for manufacturing systems
* Machine learning and deep learning methods
* Anomaly detection and fault diagnosis
* Quality prediction and process optimization
* Industrial applications and case studies of smart manufacturing