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.