CONFERENCE TOPICS

CONFERENCE TOPICS

Explore Cutting-Edge Discussions in AI and Composites

Explore Cutting-Edge Discussions in AI and Composites

AIComp 2026 covers six key areas, each pivotal to advancing composite materials through the power of artificial intelligence. These topics highlight AI’s transformative potential in the composites industry. Attending AIComp 2026 will help you gain a deeper understanding of these innovations and their real-world applications.

Here’s a closer look at these transformative topics:

KEY TOPICS

  • Design and Optimization
  • Process Modelling
  • Manufacturing Control and Optimization
  • Non-destructive Evaluation (NDE)
  • Damage and Failure Prediction
  • Structural Modelling

Design and Optimization

AI-driven design and optimization are reshaping the way composite materials are conceptualized. From automating material selection to optimizing structural layouts, AI algorithms enable designers to explore many possibilities quickly and efficiently. These advancements create stronger, lighter, and more sustainable materials tailored to specific applications.

Process Modelling
Understanding and predicting complex manufacturing processes is a significant challenge in composites. AI excels at creating detailed models that simulate manufacturing workflows, from resin flow in vacuum infusion to curing processes in autoclaves. Manufacturers can reduce errors, optimize process parameters, and ensure consistent product quality by integrating AI models.

Manufacturing (Control and Optimization)
AI technologies are revolutionizing manufacturing control by enabling real-time monitoring and adaptive decision-making. Manufacturers can detect anomalies, predict maintenance needs, and improve production efficiency with AI-enhanced sensors and data analytics. These innovations lead to higher yield rates, cost savings, and enhanced scalability.

Non-destructive Evaluation (NDE)
Ensuring the integrity of composite materials is critical, and AI is playing a key role in advancing non-destructive evaluation techniques. Machine learning algorithms analyse vast datasets from ultrasonic, thermographic, and radiographic inspections to identify defects with unprecedented accuracy. This proactive approach minimises risks and extends the lifecycle of composite structures.

Damage and Failure Prediction
AI’s ability to process complex datasets makes it invaluable for predicting damage and failure in composite materials. By analyzing stress, strain, and environmental factors, AI models can predict potential failure points before they occur. This capability enhances safety, reduces downtime, and supports the development of more robust materials.

Structural Modelling
Structural modelling is at the heart of composite material applications, and AI is pushing its boundaries. By incorporating neural networks and optimisation algorithms, AI tools provide insights into how composite structures behave under various loads and conditions. This knowledge supports the design of safer, more efficient structures in industries ranging from aerospace to construction.