Call for papers/Topics

All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:

Part 1: Independent Core Topics

These topics represent the foundational pillars of each distinct discipline before they intersect.

1. Artificial Intelligence (Foundational AI)

The core computational methods, algorithms, and mathematical frameworks that enable machines to mimic cognitive functions.

  • Machine Learning: Supervised learning, unsupervised learning, reinforcement learning, and ensemble methods.

  • Deep Learning: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).

  • Natural Language Processing: Large Language Models (LLMs), sentiment analysis, machine translation, and text-to-speech syntax.

  • Computer Vision: Object detection, image segmentation, facial recognition, and optical character recognition.

  • Knowledge Representation and Reasoning: Expert systems, semantic webs, and automated theorem proving.

2. Energy Engineering

The study of energy efficiency, energy services, facility management, plant engineering, and environmental compliance.

  • Renewable Energy Systems: Solar photovoltaics, wind turbine aerodynamics, hydroelectric power, and geothermal systems.

  • Conventional Energy Systems: Nuclear fission reactors, thermal power plants, and internal combustion engines.

  • Energy Storage & Grid Infrastructure: High-capacity battery chemistry (e.g., solid-state batteries), thermal energy storage, pumped-hydro storage, and AC/DC power transmission.

  • Thermodynamics and Fluid Mechanics: Heat transfer, fluid dynamics, and thermodynamic cycles (Rankine, Brayton, Carnot).

3. Manufacturing Engineering

The discipline of planning, designing, and optimizing the physical production of goods.

  • Traditional Machining Processes: Milling, turning, drilling, and grinding.

  • Advanced Material Shaping: Injection molding, casting, forging, and metal stamping.

  • Additive Manufacturing: 3D printing methods including Stereolithography (SLA), Fused Deposition Modeling (FDM), and Direct Metal Laser Sintering (DMLS).

  • Materials Science in Manufacturing: Metallurgy, polymers, advanced composites, and material stress-strain testing.

Part 2: Interrelated & Applied Topics

These fields represent the convergence of AI, Energy, and Manufacturing Engineering, where technologies fuse to create modern, optimized industrial systems.

1. Smart Grid & AI in Energy Systems

The intersection of Artificial Intelligence and Energy Engineering to create self-healing, efficient power networks.

  • Predictive Energy Demand Forecasting: Using machine learning to forecast peak load demands based on weather and historical data.

  • Renewable Integration Optimization: Algorithms that balance fluctuating wind and solar inputs with grid storage capabilities.

  • Smart Grid Anomalies and Cyber-Physical Security: AI-driven detection of physical faults, power theft, or cyberattacks on electrical infrastructure.

  • Microgrid Management Systems: Decentralized AI agents managing localized energy generation, storage, and consumption.

2. Industry 4.0 & Smart Manufacturing

The integration of Artificial Intelligence and Manufacturing Engineering, driving automation to autonomous levels.

  • Predictive Maintenance: Machine learning models analyzing vibration, temperature, and acoustic data from factory machines to predict failures before they occur.

  • Computer Vision for Quality Control: High-speed cameras running deep learning models to identify micro-defects on assembly lines in real-time.

  • Generative Design: AI algorithms utilizing topology optimization to design lightweight, high-strength parts engineered specifically for 3D printing.

  • Autonomous Robotics and AGVs: Automated Guided Vehicles (AGVs) and collaborative robots (cobots) navigating factory floors using reinforcement learning.

3. Sustainable Manufacturing & Energy-Aware Production

The multi-way intersection where Manufacturing processes are optimized using AI to minimize Energy footprint.

  • Industrial Energy Management Systems (IEMS): AI models that schedule heavy manufacturing operations during off-peak energy hours to reduce costs and grid strain.

  • Digital Twins for Factory Optimization: Creating virtual replicas of entire manufacturing plants to run AI simulations for maximum thermal and mechanical energy efficiency.

  • Lifecycle Assessment (LCA) Automation: Machine learning tools analyzing raw material sourcing, production energy, and recycling potential to calculate carbon footprints.

  • Waste Heat Recovery Optimization: Thermofluids engineered alongside AI algorithms to capture, store, and redistribute excess heat from manufacturing processes.