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A soft sensor for die melt temperature profile prediction of polymer extrusion

Project Description

Polymeric materials play a major role in production industry and hence advanced process monitoring is invaluable for improving the product quality and process efficiency. Extrusion is a fundamental method of processing polymeric materials. An extruder is a machine which processes materials by conveying it along a screw and forcing it through a die at a certain pressure. The main function of an extruder is to deliver a homogeneous, well mixed polymer melt at a specified uniform temperature and pressure. Currently, there are no industrially well-established techniques for online measurement/prediction of the die melt temperature profile and viscosity of the melt output. Hence, this project aims to first explore the existing melt temperature and viscosity monitoring techniques used in polymer processing and then propose novel, industrially-compatible techniques for online monitoring of melt viscosity and melt temperature profile across the die. Initially, the efficacy of the novel techniques will be explored via simulation and then will be tested on a medium scale industrial extruder with commonly used polymeric materials. The aim is that the newly proposed techniques should facilitate advanced process monitoring and hence to the development of advanced control strategies to optimize the process energy efficiency and product quality.

Funding Notes

Students with a First class/2.1 degree in Engineering, Physics, Mathematics or Materials Science subjects are encouraged to apply. A prior knowledge on polymer processing and an MSc in a related filed would also be desirable (but not essential). Experience in computer programming would also be preferable.

This project is being considered for DTA funding. This would provide a full fee waiver and a EPSRC standard stipend. International applicants are welcome to apply but will require access to self-funding.

How good is research at The University of Manchester in Electrical and Electronic Engineering, Metallurgy and Materials?
Metallurgy and Materials

FTE Category A staff submitted: 44.00

Research output data provided by the Research Excellence Framework (REF)

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