Bridging Physics-Based Modelling with Digital Manufacturing: Our Collaboration with AFRC

Enabling Predictive Manufacturing Through Integrated Modelling

We are excited to announce a new collaboration with the Advanced Forming Research Centre, focused on integrating our physics-based modelling capabilities into their digital shadow environment.

As manufacturing moves toward increasingly digitalised workflows, the ability to combine real-world process data with high-fidelity simulation is becoming critical. This project aims to bridge that gap by embedding advanced materials modelling directly into a digital manufacturing framework.

Linking Process, Microstructure, and Performance

At the core of this work is the integration of process modelling with microstructure simulation. By capturing the evolution of material behaviour during manufacturing, we can provide deeper insight into how processing conditions influence final material properties.

This is particularly important for high-value alloys such as Inconel 718, where performance is highly sensitive to thermal history and microstructural evolution. Our models focus on key metallurgical phenomena, including:

• Precipitation kinetics

• Recrystallisation behaviour

  
  

These capabilities allow manufacturers to better understand and predict outcomes across complex processes.

Towards Predictive Digital Twins

By integrating these models within AFRC’s digital shadow, this collaboration represents a step toward truly predictive digital twins in metals processing.

Rather than relying on costly and time-consuming trial-and-error approaches, manufacturers can use simulation-driven insights to:

• Optimise process parameters

• Reduce material waste

• Accelerate development cycles

• Improve component performance

  
  

This shift enables more informed decision-making and supports the transition to smarter, more efficient manufacturing systems.

Supporting Advanced Manufacturing and Alloy Development

Our contribution centres on delivering robust, physics-based models that can be embedded into digital environments and used in real-time or near-real-time decision workflows.

This has broad applications across:

• Advanced manufacturing processes

• Alloy design and qualification

• Process optimisation for high-performance components