​Project Overview: 

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This project is part of the EPSRC-funded initiative, Doing More With Less, in collaboration with the Sheffield Titanium Alloy Research (STAR) group. The primary objective is to develop a digital threading framework that connects process sensor data to the M2i2 simulator platform. This integration aims to optimise the Field Assisted Sintering Technology (FAST). 

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Objectives:​

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The project will create a digital ecosystem that provides users with remote access to materials models. This setup will allow users to create digital data from  FAST sensor readings, such as temperature and force profiles. By leveraging these data, the project will generate digital actionable information of microstructure and mechanical properties.

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Methodology:​

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The framework uses  various advanced materials modelling methods, including:

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• Avrami-Based Method: This phenomenological approach simulates the solid-state phase transitions.

• Cellular Automata Method: This method is employed for simulating grain growth.

• Mean Field Description: This statistical technique predicts the evolution (sintering) of process-induced voids and it is combined with a microstructure-sensitive constitutive model to predict the mechanical behaviour of the materials.

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By integrating these methodologies, the project aims to enhance the understanding and performance of FAST, ultimately contributing to more efficient materials processing.​​​​