Modelling and Optimisation of Mechanical Behaviour in Fused Deposition Modelling (FDM) Additive Manufacturing

About the Project

Summary of the proposed research:

Additive Manufacturing (AM) also known as three (3D) dimensional printing, particularly Fused Deposition Modelling (FDM) has emerged as a transformative technology in the production of functional components and prototypes across different industries. While significant advances have been made in the development of materials and printing techniques, the performance of FDM-manufactured parts remains highly sensitive to a range of process parameters. Variability in mechanical performance—such as tensile/compressive strength, impact resistance, and dimensional accuracy—can often be traced back to fluctuations in adjustable process parameters such as layer thickness, print speed, infill density, infill pattern, printing temperature and build orientation. These parameters not only influence the mechanical behaviour and accuracy of the parts, but at the same time, also influence the amount of time and material required for completing the part. The amount of time and material required directly controls the environmental impact and sustainability of these processes.

This PhD project aims to systematically investigate the influence of key process parameters and material selection on the mechanical performance of FDM-printed components, with a view to developing predictive models and optimisation strategies that can guide process planning for enhanced reliability and repeatability.

Project Objectives:

• To identify and quantify the effects of input parameters and material type on the mechanical behaviour of parts produced via FDM.
• To develop empirical and machine learning-based models that can predict mechanical outcomes from process settings.
• To optimise process parameter combinations for application-specific requirements using advanced optimisation algorithms.
• To contribute to the establishment of standardised frameworks for part consistency and performance benchmarking.

The project will involve a multi-disciplinary methodology, incorporating:

• Experimental fabrication and mechanical testing of standardised test specimens under a controlled matrix of parameters.
• Design of Experiments (DoE) approaches to minimise experimental runs while capturing meaningful insights.
• Material characterisation using techniques such as scanning electron microscopy (SEM), tensile/compression testing, and surface roughness evaluation.
• Numerical simulation and finite element modelling (FEM) to understand internal stress distribution and failure modes.
• Data-driven modelling using machine learning techniques (e.g., regression models, neural networks) to predict mechanical responses based on input settings.

The project offers the opportunity to contribute to the development of more consistent, cost-effective, and performance-driven additive manufacturing practices, particularly in applications where reliability is paramount—such as biomedical devices, aerospace tooling, and customised consumer products.

This research is suited to applicants with a background in mechanical engineering, materials science, manufacturing engineering, or applied physics, and with an interest in experimental methods, modelling, and process optimisation. Prior experience with additive manufacturing technologies, CAD software, or programming for data analysis (e.g., MATLAB, Python, R) would be advantageous.

How to apply:

For further information please contact:

Dr Muhammad Fahad at muhammad.fahad@staffs.ac.uk

The applications should consist of a cover letter or personal statement of interest, and a CV.

Dr Muhammad Fahad

Senior Lecturer

School of Digital, Technology, Innovation & Business

University of Staffordshire

College Road

Stoke-on-Trent

ST4 2DE

The expected start dates are January and April 2026.

Entry Requirements:

Applicants should have a First- or Upper Second-Class UK honours degree, or equivalent, in Mechanical Engineering or a relevant discipline. An MSc with Distinction or Merit in a relevant subject is highly desirable. Knowledge or experience of additive manufacturing technologies, CAD software, or programming for data analysis (e.g., MATLAB, Python, R) is advantageous.

The standard minimum IELTS Academic requirement is 6.5 overall with no less than 6.0 in each band. Some International students are also required to meet UKVI requirements for the appropriate study visa. A valid ATAS certificate (where required) must be secured as a prerequisite to enrolment.