View source for Projects:2019s2-20001 Using Machine Learning to Determine Deposit Height and Defects for Wire + Arc Additive Manufacture (3D printing)

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2019s2|24501]]
Abstract here
== Introduction ==
3D  printing  is  an  emerging  technology  that  has  the  potential  to  significantly  reduce  material  usage  through  the production of near net-shape parts.  Many of the systems for 3D printing are based on lasers andpowders; however the  deposition  rate  with  such  systems  is  very  low  making  the  production  of  large-scale  parts  difficult.    AML Technologies  specialises  in  the  use  of  Wire  +  Arc  Additive  Manufacture  (WAAM)  where  deposition  is  based  on  arc welding processes and the deposition rates are an order of magnitude greater.When building 3D printed parts, even a relatively small layer height error of only 0.1 mm can produce large build height errors when multiplied across the many layers of a typical build.  This canmake path planning difficult, so in-process layer height measurement is an essential building block of any production 3D printing system.A variety of techniques can be used for monitoring the layer height including laser scanners, and arc monitoring.  It is the latter technique that will be explored in this project due to its robustness, and low cost of implementation – it only requires the measurement of arc current and voltage.  Furthermore, it can potentially be used to detect defects by identifying waveform irregularities. This will be achieved with the following stages:
•Undertake a literature review on machine learning methods that can be applied to analyse arc waveforms as well  as  through  arc  seam  tracking  which  is  commonly  used  to  control  the  position  of  the  torch  during  welding.  Determine suitable methods that could be applied to WAAM.
•Use  arc  monitoring  equipment  to  measure  voltage  and  current  for  a  variety  of  contact  tip  to  workpiece distances (CTWD –the distance from the torch to the workpiece) and different welding processes.
•Analyse  the  waveforms  and  identify  characteristics  for  input  into  a  machine  learning  algorithm  that determines both CTWD and defects.
•Use  this  algorithm  to  develop  online  signal  processing  software  that  outputs  thecontact  tip  to  workpiece distance as well as indicating whether defects have occurred.
=== Project team ===
==== Project students ====
* Anh Tran
* Nhat Nguyen
==== Supervisors ====
* Dr. Brian Ng
* Dr. Paul Colegrove (AML3D)
=== Objectives ===
Set of objectives

== Background ==

=== Topic 1 ===

== Method ==

== Results ==

== Conclusion ==

== References ==
[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.

[2] ...