Call for papers/Topics

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

Artificial Intelligence (AI)

Machine Learning (ML): Supervised learning, unsupervised learning, reinforcement learning, and ensemble methods.
Deep Learning: Artificial neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.
Natural Language Processing (NLP): Large language models (LLMs), semantic analysis, sentiment tracking, and machine translation.
Computer Vision: Image recognition, object detection, semantic segmentation, and edge detection.
Probabilistic Reasoning and Knowledge Representation: Bayesian networks, knowledge graphs, and expert systems.

Generative AI: Diffusion models, Generative Adversarial Networks (GANs), and synthetic data generation.
Edge AI: Localized processing, model quantization, and neuromorphic computing.
Explainable AI (XAI): Model interpretability, transparency frameworks, and bias mitigation.

Solar Energy: Photovoltaic (PV) cell efficiency, solar thermal systems, and inverter technology.
Wind Energy: Onshore and offshore turbine aerodynamics, rotor dynamics, and drivetrain mechanics.
Hydroelectric and Marine Energy: Tidal, wave, and run-of-river generation systems.

Thermal Power Plants: Gas turbines, combined-cycle power plants, and carbon capture and storage (CCS).
Nuclear Engineering: Fission reactors, small modular reactors (SMRs), and fusion energy research.

Grid Management: High-voltage direct current (HVDC) transmission, substations, and grid stability.
Energy Storage Systems: Lithium-ion batteries, solid-state batteries, pumped hydro, and thermal storage.
Hydrogen and Alternative Fuels: Electrolysis, hydrogen storage, fuel cells, and biofuels.

Subtractive Manufacturing: CNC machining, milling, turning, and electrical discharge machining (EDM).
Additive Manufacturing (3D Printing): Powder bed fusion, stereolithography, fused deposition modeling, and direct energy deposition.
Forming and Casting: Injection molding, die casting, forging, and sheet metal forming.

Robotics and Kinematics: Industrial robotic arms, kinematics, end-effectors, and programmable logic controllers (PLCs).
Computer-Integrated Manufacturing (CIM): CAD/CAM integration, product lifecycle management (PLM), and automated guided vehicles (AGVs).
Metrology and Quality Control: Coordinate measuring machines (CMM), non-destructive testing (NDT), and Six Sigma methodologies.

Smart Grid Optimization: Demand forecasting, dynamic pricing algorithms, and automated load balancing using ML.
Predictive Maintenance for Energy Assets: Failure prediction for wind turbine gearboxes, solar inverters, and nuclear plant valves using vibration and thermal data.
Renewable Energy Forecasting: Using deep learning and meteorological data to predict solar irradiance and wind patterns.
Virtual Power Plants (VPPs): AI-driven aggregation and dispatch of distributed energy resources (DERs).

Predictive Maintenance in Factories: Anomaly detection in CNC spindles, conveyor belts, and robotic joints using IoT sensor streams.
Defect Detection & Quality Assurance: Computer vision systems scanning assembly lines in real-time to catch surface micro-cracks or assembly errors.
Generative Design: AI algorithms exploring thousands of design permutations based on material and weight constraints for 3D printing.
Robotic Autonomy and Cobots: Reinforcement learning enabling collaborative robots (cobots) to adapt to human movements and varying part orientations.

Energy-Efficient Manufacturing Processes: Optimizing the power consumption of energy-intensive processes like metal smelting or injection molding.
Life Cycle Assessment (LCA): Evaluating the total environmental and energy footprint of manufactured goods from raw material to disposal.
Manufacturing for the Energy Sector: The specialized production of massive wind turbine blades, battery cells, and silicon PV wafers.
Circular Economy Engineering: Remanufacturing, recycling processes for lithium batteries, and scrap material upcycling.

Industrial Microgrids: Manufacturing facilities utilizing AI to manage their own localized solar, storage, and grid interactions to minimize carbon footprints and energy costs.
Digital Twins of Industrial Ecosystems: Creating real-time virtual replicas of entire factories that simulate both production output (manufacturing) and energy draw (energy) optimized continuously by machine learning (AI).
Autonomous Supply Chain Carbon Accounting: AI systems tracking the logistics, manufacturing energy, and material sourcing to optimize for the lowest possible embedded carbon footprint in real-time.