View source for Projects:2021s1-13012 Deep Learning Approach for Automatic Defect Detection during Wire + Arc Additive Manufacture (3D printing)

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|130012]]
3D metal printing through AML3D's ARCEMY AND WAAM (Wire Arc Additive Manufacturing) Process uses programmed robots and wire-arc welding to generate 3D metal objects and designs. These objects are free form parts utilising wire as feedstock to the wire-arc process. There are economic, environmental, and time benefits from AML3D's ARCEMY and WAAM process. Throughout this process there is potential for equipment and deposition problems which can cause defects. Examples of these types of defects are porosity, lack of fusion, humping, and holes. With this in mind, the project will focus on layer by layer Automatic defect detection during the WAAM process. The primary objective for this project is to use Deep-Learning approaches for Automatic Defect detection during the WAAM process. There are serval ways for this to be achieved such as image processing and recognition, computer vision, machine learning, deep learning, or any combination of these. The detection process for layer-by-layer detection will be (1) A Welding/3D metal printing layer is produced. (2) A photo is taken of the layer and inputted into an algorithm. (3) If there is a defect, then signal a warning and wait until it has been resolved. Otherwise, if there are no defects, then repeat the cycle after the next layer has been completed. To assist a deep learning approach, a large sample of defects will allow the algorithm to learn and grow. This will result with increasing success for identifying defects. An extensive literature review will need to be initially conducted not only in 3D metal printing defect detection, but in all other aspects like image processing, computer visions, and machine learning. Once this is complete, then some experimental analysis techniques can be implemented and tested. 

== Introduction ==
Project description here

=== Project team ===
==== Project students ====
* Yuhan Zhou
* Liam Dorward

==== Supervisors ====
* Dr Brian Ng
* Dr Paul Colegrove (AML3D)
==== Advisors ====
*
*

=== Objectives ===
Set of objectives

== Background ==
=== Welding Defects ===
Through WAAM, the weld is constantly subjected to thermal expansion and shrinkage throughout the building process. This can cause longitudinal and transverse shrinkage, bending, angular, and rotational deformations to occur. Residual stress is tested when all external loading forces have been removed. This residual stress will determine its mechanical properties and fatigue performance. If the stress is high these parts can crack. Cracks can also arise from high strain in the melt pool.

Low mechanical strength and low fatigue properties are potential outcomes with porosity defects. Porosity occurs from surface contamination which may contain hydrocarbons, grease, and moisture. It contaminates the molten pool being deposited. Additionally, a lack of fusion can occur if the metals are not properly clean. This can create gaps within each layer. 

Delamination is an example of the layers appearing as separate and not representing one whole component. This type of defect is formed from incomplete melting or insufficient remelting of the underlying solid between the layers. This defect is much more visible and is unlikely to be fixed later.

== Method ==

== Results ==

== Conclusion ==

== References ==
[1] 

[2]