Overview | Testing | Extreme Conditions | Modelling & ML | Pilot Testing

Modelling and Machine Learning

The AMRICC Centre is a cutting-edge facility that is spearheading the adoption of Industry 4.0 for the ceramics sector and beyond. By harnessing the power of data science and modelling, the centre can uncover patterns and insights that inform process optimisation. The centre also leverages material informatics as a powerful tool that can cut development costs and time.

By performing analysis with computational modelling, cost and time demands can be dramatically reduced, and project savings can be made without a single gram of material being consumed. This also enables the simulation of various aspects of physical systems and the prediction of their interactions, which can accelerate the development and optimisation of products and processes during the early phases of technology scale-up.

The AMRICC Centre is not only advancing the state of the art in the ceramics sector, but also transforming it with Industry 4.0 by aiming to support the process optimisation of manufacturing sites across all segments of the ceramics industry. For example, optimising the 3D printing of advanced ceramics or developing real-time monitoring of ceramics shrinkage during sintering. The centre employs time-series data machine learning on the real-time data collected using a range of standard and novel sensors.

The centre also conducts computational materials development and finite element modelling of key manufacturing processes, including digital twinning. These activities enable the centre to create innovative solutions and products that can help our collaborators secure a global position in the advanced ceramics market.

Digital Capabilities:

  • Design of Experiments (DoE) / ML-Enhanced DoE: Experimental designs to uncover pivotal insights, accelerate materials discovery and process optimisation. Elevate traditional DoE with ML algorithms, unveiling complex patterns and predictive models for superior decision-making
  • Process Optimisation: Utilising data-driven strategies to fine-tune processes, reduce waste, and enhance product quality and consistency
  • Healthcare Applications: Bridging materials science and healthcare to innovate/optimise in drug production
  • Material Property Prediction: Employing ML to predict material properties, aiding in rapid prototyping and material selection
  • Automated Quality Control: Enhancing product quality and consistency by automating quality checks using Computer Vision, reducing errors and ensuring standards are met efficiently

Advanced Computational Modelling:

  • Finite Element Analysis (FEA): Precision analysis for structural, thermal, and fluid flow phenomena, enabling accurate simulations and robust design evaluations
  • Multiphysics simulation: Leveraging the power of software packages for multi-domain simulations, aiding in the exploration of complex physical interactions and real-world scenarios
  • Parametrised Modelling: Offering adaptable parametrised models for flexible design exploration and optimisation, facilitating informed decision-making and rapid prototyping
  • Diverse Modelling Techniques & Integration: Proficiency in static, dynamic, linear, non-linear, and stochastic modelling, providing to a wide spectrum of project needs and challenges