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51st LISBON World Conference on Artificial Intelligence, Energy & Manufacturing Engineering (AIEME-26) May 13-15, 2026 Lisbon (Portugal)
1. AI in Manufacturing Engineering (Smart Manufacturing) This field focuses on the application of machine learning and robotics to optimize production lines and product design. Predictive Maintenance: Failure mode analysis using sensor data. Remaining Useful Life (RUL) estimation. Anomaly detection in rotating machinery. Quality Control & Computer Vision: Automated optical inspection (AOI). Real-time defect detection in assembly lines. Hyperspectral imaging for material characterization. Generative Design & Digital Twins: AI-driven topology optimization (reducing weight/material). Real-time synchronization between physical assets and virtual models. Simulation-based training for reinforcement learning agents. Industrial Robotics: Path planning and obstacle avoidance. Collaborative robots (Cobots) and human-robot interaction. Swarm robotics in warehousing and logistics. 2. AI in Energy Systems AI is essential for managing the complexity of modern power grids, especially with the integration of volatile renewable sources. Smart Grid Management: Demand Response (DR) forecasting and optimization. Load balancing and peak shaving algorithms. Self-healing grid architectures using multi-agent systems. Renewable Energy Forecasting: Deep learning for solar irradiance and wind speed prediction. Hydro-power inflow forecasting. Storage optimization for intermittent sources. Energy Efficiency in Buildings (Smart Buildings): Occupancy-based HVAC (Heating, Ventilation, and Air Conditioning) control. Automated lighting and shading systems. Non-intrusive load monitoring (NILM). Oil, Gas, and Nuclear Energy: Seismic data interpretation using CNNs. Drilling optimization and autonomous underwater vehicles (AUVs). Nuclear reactor core monitoring and safety simulations. 3. Sustainable Manufacturing & Circular Economy This intersection explores how engineering and AI can reduce the environmental footprint of industrial processes. Energy-Aware Scheduling: Optimizing production schedules based on time-of-use electricity pricing. Minimizing "idling" energy in CNC machines and industrial furnaces. Additive Manufacturing (3D Printing) Optimization: In-situ monitoring of melt pools. AI-based parameter tuning for metal powder bed fusion. Supply Chain & Logistics: Carbon footprint tracking using blockchain and AI. Route optimization for heavy-duty electric vehicle fleets. Inverse logistics for product recycling and refurbishment. 4. Foundational Technologies & Frameworks These are the cross-cutting tools required to implement AI in energy and manufacturing. Edge Computing & IIoT: Industrial Internet of Things (IIoT) sensor networks. On-device AI (Edge AI) for low-latency decision-making. Cyber-Physical Systems (CPS): Integration of computation, networking, and physical processes. Cybersecurity for critical infrastructure and factory floors. Explainable AI (XAI): Ensuring transparency in AI decisions for high-stakes engineering environments. Physics-Informed Neural Networks (PINNs) that obey thermodynamic laws.
35th HELSINKI World Congress on Mining, Material and Metallurgical Engineering (WCM3E-26) scheduled on April 27-29, 2026 Helsinki (Finland)
