Call for papers/Topics

Topics of interest for submission include any topics related to:

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.