Development of a Machine Learning Tool to Aid Wire DED AM Process Parameter and Toolpath Selection via Bead Geometry Prediction

In this section

Back to Core Research Programme AMorph – 4D Printing for Field Morphing Structures Automation of Composite and Hybrid Joining Process Coaxial Wire Additive Manufacturing Processes and In-Line Monitoring Tools Development of Coded Excitation Technique for Non-Metallic Pipes Inspection Development of Digital Twin technology as a demonstrator for real-time integrity assessment of crack growth in tensile specimens Development of Friction Stir Welding (FSW) for Hydrogen Fuel Storage Tanks Development of Leak-Before-Break Hydrogen Storage Composite Pressure Vessels With Polymeric Liner Establishing Assessment Methods for Determining Safe Service Limits of High-Temperature Materials in Extreme Environments Fracture toughness in aggressive environments: effect of crack monitoring techniques on test results In-process detection and non-destructive testing of electron beam weld imperfections Internal Bore Coatings for Applications in Corrosion and Hydrogen Environment Joining of Additively Manufactured Materials Long term behaviour of polymeric liner materials/coatings in hydrogen service Techniques for High Temperature Ultrasonic Inspection of Arc Welding Ultimate Leverage Fund

Project Code: PROP311381

Start date and planned duration: April 2024, 24 months

Objective

Develop predictive models using machine learning (ML) algorithms to predict directed energy deposition) DED bead geometry from input parameters.
Validate and enhance predictive models through in-depth testing and optimisation techniques.
Implement a user-friendly software tool integrating the predictive model for pre-trial DED bead geometry predictions

Project Outline

This project seeks to revolutionise the field of wire DED additive manufacture (AM) by harnessing the power of ML to predict DED bead geometry accurately based on given process parameter sets and toolpaths. The central idea is to develop a robust predictive model that can predict bead geometry outcomes from a limited set of practical trials. By creating a data-driven prediction tool, we aim to significantly reduce the number of developmental pre-trials required, thus streamlining the parameter development process.

The integration of ML techniques will enable us to uncover complex patterns and relationships in the vast amount of process data, leading to more informed decision-making and precise predictions. This predictive model will not only optimise DED manufacturing but also lay the foundation for future advancements in microstructure and material property predictions, further enhancing the capabilities of this cutting-edge technology.

Industry Sectors

- Aerospace

- Automative

- Construction and engineering

- Oil and Gas

- Power

Benefits to Industry

The outcome of this project holds the potential to enable Industry Members to harness the power of machine learning for the purpose of optimising and enhancing the productivity of the AM process. This could lead to a substantial reduction in both costs and lead times.