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Investigating acoustic emission in selective laser melting

Ball, Stephen 2021. Investigating acoustic emission in selective laser melting. PhD Thesis, Cardiff University.
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Abstract

As additive manufacturing continues to develop as a manufacturing technique, companies such as Renishaw plc are increasingly looking for ways to make it a more cost-effective and practical alternative to traditional methods. A major part of this is the use of in-process condition monitoring techniques, such as acoustic emission, to allow the suitability of AM parts for loading to be assessed without expensive post-processing methods. Acoustic emission will allow not only the detection but also the location of defects as they occur throughout the build volume. This project was undertaken with the aim of assessing the use of acoustic emission within the Renishaw AM500.This work was split into three main sections; assessing how AE signals propagate through a build plate assembly, assessing how to accurately locate AE sources and whether it is then possible to classify defects based on collected AE signals. Studies were carried out using a 3D scanning laser vibrometer to assess how signals propagate through various parts of the build plate assembly. Work was also carried out to assess the accuracy of using the Time of Arrival and delta-T methods to locate sources, with studies done to improve the implementation of each. Finally, work was done to assess the viability of using machine learning methods to classify signals in such a complex geometry. The results of these propagation studies led to a design change to the heater plate within the AM500 to simplify propagation paths to give more reliable location results. Studies into using the delta-T location method showed that differences in plate thickness led to negligible differences in delta-T training maps meaning that a single map can be used throughout the lifetime of a build plate. Further results showed that in a homogeneous material such as an AM build plate, data from as few as four training points are needed to produce a reliable delta-T training set. Finally, results of characterisation studies showed that not only will the complex geometry of the system make distinguishing signals difficult, but differences between parts being built will make developing a standard set of training data exceedingly difficult.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Engineering
Uncontrolled Keywords: Acoustics Emission, Additive Manufacturing, 3 D Printing , Prcess Monitoring, Selective laser siintering, Rapid Prototyping
Date of First Compliant Deposit: 14 July 2022
Last Modified: 14 Jul 2022 09:12
URI: https://orca.cardiff.ac.uk/id/eprint/150922

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