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Mastering AI in Polymer Composites: Data-Driven Formulation, Process Optimization & Defect Prevention

Mastering AI in Polymer Composites: Data-Driven Formulation, Process Optimization & Defect Prevention
Language: English
Length: 90 min
Dec 03, 2025 03:00 PM
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Course Description

Polymer composites deliver exceptional strength-to-weight performance, but their development is a complex balancing act. Engineers must fine-tune multi-component formulations to meet targets for stiffness, toughness, cost, and sustainability. Traditional trial-and-error methods are slow and wasteful, with high scrap rates and long development cycles, just as demand grows for greener, more efficient solutions.
This course shows how AI and data-driven methods can tackle these challenges. You’ll learn to apply machine learning and digital twins to detect process anomalies and predict defects before they occur. By integrating data from lab tests, pilot runs, and full-scale production (a multifidelity approach), you can optimize formulations and processing holistically.
For example, ML algorithms can uncover new resin formulations and even simulate lifecycles, dramatically reducing waste and accelerating design cycles. Real-world case studies reveal how AI boosts quality, yield, and sustainability in composite manufacturing.
Intermediate
Level
Dr. Srikanth Pilla
Dr. Srikanth Pilla
0 courses

Dr. Srikanth Pilla is a globally recognized leader in sustainable materials and manufacturing, with over 20 years of experience driving innovation at the intersection of composites, lightweighting, and circular economy solutions. He currently serves as Professor and Director of the Center for Composite Materials (UD-CCM) at the University of Delaware, and holds cross-disciplinary appointments in Mechanical Engineering, Materials Science, and Chemical and Biomolecular Engineering.

Dr. Pilla is also the Founding Director of ‘AIM for Composites’, a U.S. Department of Energy–funded Energy Frontier Research Center dedicated to advancing AI-driven sustainable composites manufacturing.

Before joining UD-CCM, he held the prestigious ExxonMobil Employees Chair in Engineering at Clemson University, where he founded the Clemson Composites Center and led major collaborations with automotive OEMs and tier suppliers. His early career includes roles at Stanford University, the University of Wisconsin-Madison, and the University of Wisconsin-Milwaukee.

With a research portfolio backed by NSF, DOE, DOD, NASA, USDA, and leading global companies, Dr. Pilla has authored 150+ peer-reviewed publications, mentored over 50 graduate students and postdocs, and actively contributes to advancing sustainable engineering worldwide.

He currently serves as Editor-in-Chief of the SAE International Journal of Sustainable Transportation, Energy, Environment and Policy, and has received top honors including the U.S. EPA Presidential Green Chemistry Challenge Award (2021) and DOE Vehicle Technologies Office Team Award (2022).

Dr. Pilla’s work bridges academic excellence and industrial relevance, making him a trusted voice in developing the next generation of sustainable materials and technologies.

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Why should you view this course?

The polymer composites sector is rapidly moving toward AI-enabled manufacturing, and professionals who master these tools will be in high demand. This course provides practical skills that immediately improve efficiency, reduce scrap, and accelerate product development. You’ll return equipped with the know-how to deploy AI in real-world settings, whether it’s in thermoset laminates, thermoplastic compounding, or injection molding. In short, this course delivers tangible ROI for both your career and your organization.

  1. Accelerate Innovation: Learn data-driven and inverse-design techniques (e.g: neural networks, surrogate models, and digital twins) that compress R&D time from months to days.

  2. Improve Quality & Yield: Discover how real-time monitoring with sensors, cameras, and ML can spot defects (voids, delamination, shrinkage, dispersion issues) before they escalate.

  3. Drive Sustainability: Gain methods to optimize thermoset and thermoplastic formulations for both performance and eco-efficiency.


Who should join this course?
    • Materials & Composites Experts – Engineers and
      Scientists eager to apply AI to plastics, aerospace, automotive, and wind energy innovations.
    • Manufacturing & Quality Professionals – Process, production, and QA Engineers looking to optimize molding, extrusion, compounding, or pultrusion with AI.
    • Data-Driven Innovators – Analysts, ML specialists, and technical leaders ready to harness AI and Industry 4.0 for smarter materials and manufacturing.

  • Complete the course and (unlock your personalized certificate)– your badge of accomplishment awaits!

  • This course is suitable for intermediate level proficiency
    Intermediate
Questions you will be able to answer after this course:

  1. How can AI and inverse design replace trial and error in composite formulation?

  2. What kinds of data are required, and how can sparse or noisy datasets be effectively managed?

  3. Which AI/ML models and multi-fidelity simulations work best for predicting properties and optimizing processing?

  4. How can AI accelerate injection molding, extrusion, and curing cycles while improving consistency?

  5. What role do sensors, vision systems, and predictive analytics play in preventing defects like voids, warpage, or poor dispersion?

  6. How can organizations scale AI from lab to plant while balancing cost, performance, and sustainability?

Course Outline
The following sections will be covered during this session:

Introduction: Why AI for Composite Manufacturing? 

  • What is a Composite Material?
  • How do we traditionally select the right material composition?
  • How do we traditionally manufacture a composite part?
  • Traditional Approach to Composites Design and Manufacturing
  • Case Example: Traditional Approach to Composite Part Design
  • Evolution of AI in Composite Science
  • The Inverse Design Approach

Data in Composite Manufacturing – Challenges & Solutions 

  • Drawbacks of conventional Data flow in conventional design methods, Advantages of ML Methods.
  • Handling sparse, noisy, and unstructured data
  • Data fusion from lab, pilot, and plant levels

AI Models for Composites: for Process Optimization

  • Fundamentals of Machine Learning Techniques 
  • Architecture of different Neural Networks
  1. Feedforward Neural Networks
  2. Recurrent Neural Networks
  3. Convolution Neural Networks
  4. Multifidelity Approach overview

AI in Defect Detection & Prediction 

  • Common defects: voids, delamination, poor dispersion, shrinkage
  • Real-time monitoring using sensors, vision systems, & Machine Learning
  • Predictive models for defect risk during extrusion, compounding & molding
  • Case studies: Enhancing product quality and process monitoring in Injection Molding

Scaling & Deployment in the Plastics Industry 

  • Translating lab-scale AI models to plant-scale manufacturing
  • Integrating AI with process control systems (Industry 4.0 context)
  • Barriers to adoption: cost, culture, data silos

Case Studies & Industrial Best Practices 

  • Real examples of AI 
  1. Multi-Fidelity warpage optimization in Injection Molding
  2. Generative models to predict fiber distributions
  • Future Use Case: Discover AI-driven hybrid manufacturing
  • Lessons learned from early adopters
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Prerequisites for this course
  • Background in Polymer Composites or Materials Engineering: Familiarity with thermoset and thermoplastic composites, fiber reinforcement, and processing methods (molding, extrusion, etc.) is recommended.
  • Engineering or Science Foundation: Understanding of mechanical properties (strength, stiffness, toughness) and basic statistics.
  • Data Literacy: Comfort with data analysis concepts. Programming is not required, but a willingness to engage with data-driven examples is helpful.
  • Openness to AI Concepts: No prior AI expertise needed, but curiosity about neural networks, regression, and Machine Learning applications in engineering is expected.
30 min Q & As for this course
Interact directly with your tutor and clarify your doubts
  1. 30 mins per session for Questions and Answers
  2. Clarify your doubts.
  3. If your questions are not answered, dont worry you will recieve tutor’s reply via email after sessions.
Mastering AI in Polymer Composites: Data-Driven Formulation, Process Optimization & Defect Prevention
€299
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