About this Research Topic
In the rapidly evolving world of artificial intelligence (AI), various industrial sectors are increasingly adopting AI-driven technologies to automate complex tasks and enhance production efficiencies. Their transformation strongly relies on data generated from production lines via sensor networks, which serve as a valuable resource for improving several aspects of production processes. This optimization spans such tasks as detecting anomalies, ensuring adherence to high-quality standards, monitoring the transformation of products, and minimizing material wastage. Furthermore, AI supports predictive maintenance, forecasting potential disruptions to ensure continuous operation, heralding a significant shift toward automated processes.
This Research Topic focuses on exploring the intersection of deep learning and industrial applications, with a commitment to advancing both near-term efficiency and long-term sustainability. Central to this exploration are cutting-edge deep learning methodologies, which empower autonomous decision-making through optimized machine-to-machine communication, facilitated by IoT and Edge computing. These frameworks promise enhanced energy efficiency and reduced latency but bring challenges like fitting advanced models into the compact, real-time environments of smart factories. This paradox necessitates research into real-world application viability and the improvement of trust and safety in automation technologies.
Contributions are sought that not only develop but robustly test and validate new deep learning approaches within the industrial context. Submissions should focus on:
Smart Manufacturing
- Real-time sensing
- Intelligent control
- Anomaly detection
- Quality inspection
- Fault detection and predictive maintenance
- Energy and resource consumption forecasting
- Planning and scheduling
Explainable AI (XAI)
- Feature-based and example-based methods
- Model-specific and model-agnostic frameworks
- Local and global approaches
- Trust and transparency metrics
Optimization Techniques
- Model compression
- Knowledge distillation
- Pruning techniques
- Neural architecture search (NAS)
Smart Industries (Sector-Specific Applications)
- Food and beverage
- Automotive
- Transportation
- Aerospace
- Energy
- Electronics
- Wood
- Oil and gas
Through this focus, the Research Topic aims to shed light on novel AI strategies and frameworks for more adaptive, reliable, and sustainable industrial environments.
This Research Topic focuses on exploring the intersection of deep learning and industrial applications, with a commitment to advancing both near-term efficiency and long-term sustainability. Central to this exploration are cutting-edge deep learning methodologies, which empower autonomous decision-making through optimized machine-to-machine communication, facilitated by IoT and Edge computing. These frameworks promise enhanced energy efficiency and reduced latency but bring challenges like fitting advanced models into the compact, real-time environments of smart factories. This paradox necessitates research into real-world application viability and the improvement of trust and safety in automation technologies.
Contributions are sought that not only develop but robustly test and validate new deep learning approaches within the industrial context. Submissions should focus on:
Smart Manufacturing
- Real-time sensing
- Intelligent control
- Anomaly detection
- Quality inspection
- Fault detection and predictive maintenance
- Energy and resource consumption forecasting
- Planning and scheduling
Explainable AI (XAI)
- Feature-based and example-based methods
- Model-specific and model-agnostic frameworks
- Local and global approaches
- Trust and transparency metrics
Optimization Techniques
- Model compression
- Knowledge distillation
- Pruning techniques
- Neural architecture search (NAS)
Smart Industries (Sector-Specific Applications)
- Food and beverage
- Automotive
- Transportation
- Aerospace
- Energy
- Electronics
- Wood
- Oil and gas
Through this focus, the Research Topic aims to shed light on novel AI strategies and frameworks for more adaptive, reliable, and sustainable industrial environments.
Keywords: artificial intelligence (AI), sustainable production, trustworthiness, automation, Internet of Things (IoT)
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
