AIEME-2 2025 - 18th PARIS International Conference on “Artificial Intelligence, Energy & Manufacturing Engineering” (AIEME-25) scheduled on Dec. 3-5, 2025 Paris (France)

Website https://eaceee.erpub.org/conference/161 | Edit Freely

Category ENGINEERING, TECHNOLOGY, AI, Automobile, Bioinformatics, Biomedical, Chemical, Computer, Computing, Electrical, Energy, Image Processing, IT, Manufacturing, Mechanical, Metallurgical, Military, Mining, Nanotechnology, Aviation

Deadline: November 22, 2025 | Date: December 03, 2025-December 05, 2025

Venue/Country: Paris, France

Updated: 2025-07-25 15:53:02 (GMT+9)

Call For Papers - CFP

I. Artificial Intelligence (AI) - Foundational and Applied:

Machine Learning (ML) & Deep Learning (DL):

Supervised, unsupervised, reinforcement learning algorithms.

Neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers.

Generative AI (GenAI) for design, optimization, and content creation in engineering.

Physics-informed AI/Machine Learning (PIML) for integrating physical laws into AI models.

Explainable AI (XAI) and AI ethics in critical applications.

Data Science & Analytics:

Big data management, processing, and analysis for large-scale engineering data.

Predictive analytics, forecasting, anomaly detection.

Data visualization and interpretation for engineers.

Data fusion from diverse sensors and sources.

AI for Control & Automation:

Intelligent control systems, adaptive control, optimal control.

Robotics and autonomous systems (industrial robots, drones, autonomous vehicles in industrial settings).

Human-robot interaction and collaboration (cobots).

AI Architectures & Platforms:

Edge AI, fog computing, and cloud computing for industrial applications.

AI hardware accelerators (GPUs, TPUs, neuromorphic chips).

AI software frameworks and tools.

II. AI in Energy Systems:

Smart Grids & Energy Management:

AI for optimizing energy generation, transmission, and distribution.

Predictive maintenance of grid infrastructure (transformers, power lines).

Demand forecasting and response management.

Load balancing and peak shaving using AI.

Microgrid optimization and control.

Renewable Energy Integration & Optimization:

Forecasting wind power, solar irradiance, and hydropower generation.

AI for optimal siting of renewable energy facilities.

Hybrid renewable energy systems management.

Energy storage optimization (batteries, hydrogen, pumped hydro).

Energy Efficiency & Sustainability:

AI for optimizing energy consumption in industrial facilities, buildings, and transportation.

Carbon emissions monitoring, prediction, and reduction strategies using AI.

AI for resource management and waste reduction in energy production.

Energy Infrastructure & Security:

Predictive maintenance for power plants, turbines, and other energy assets.

Cybersecurity for critical energy infrastructure (SCADA systems, smart meters).

AI for fault detection and diagnosis in complex energy systems.

New Energy Technologies:

AI in nuclear fusion research.

AI for advanced materials in energy (e.g., better batteries, solar cells).

Optimization of hydrogen production, storage, and distribution.

III. AI in Manufacturing Engineering:

Smart Manufacturing & Industry 4.0/5.0:

Digital twins and cyber-physical systems for real-time monitoring and control.

AI for predictive maintenance of manufacturing machinery (e.g., CNC machines, assembly lines).

Autonomous manufacturing systems and intelligent automation.

Human-AI collaboration in production environments (Operator 5.0).

Production Optimization & Control:

AI-driven production scheduling and resource allocation.

Real-time process optimization and adaptive control.

Quality control and defect detection using computer vision and sensor data.

Root cause analysis of production issues with AI.

Advanced Manufacturing Processes:

AI for additive manufacturing (3D printing) – design, process control, quality assurance.

Robotics and automation in assembly, welding, material handling, and finishing.

AI for generative design of components and products.

Simulation and modeling of manufacturing processes using AI.

Supply Chain & Logistics:

AI for optimizing supply chain planning, logistics, and inventory management.

Demand forecasting and risk management in supply chains.

Blockchain for secure and transparent supply chain operations.

Workforce & Human Factors:

AI for skill development and training in manufacturing.

Ergonomics and safety in AI-driven manufacturing environments.

AI-assisted decision-making for human operators.

Sustainable Manufacturing:

AI for reducing waste and optimizing material usage.

Circular economy principles in manufacturing driven by AI.

Energy efficiency in factories and production lines.

IV. Cross-Cutting & Interdisciplinary Themes:

Integration of AI, Energy, and Manufacturing: Case studies and real-world applications demonstrating synergistic benefits.

Cybersecurity for AI-enabled Systems: Protecting industrial and energy infrastructure from AI-driven threats.

Data Governance & Management: Strategies for collecting, storing, and utilizing vast amounts of data from energy and manufacturing systems.

Ethics, Regulation, and Policy: Discussing the societal implications, regulatory frameworks, and ethical considerations of AI in these critical sectors.

Digital Transformation Strategies: Roadmaps and challenges for implementing AI across organizations.

Economic Impact & ROI: Analyzing the financial benefits and investment returns of AI adoption.

Education & Workforce Development: Addressing the skills gap and preparing the next generation of engineers.