Difference between revisions of "Projects:2021s1-13012 Deep Learning Approach for Automatic Defect Detection during Wire + Arc Additive Manufacture (3D printing)"
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| − | + | 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. | |
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== Introduction == | == Introduction == | ||
Project description here | Project description here | ||
Revision as of 00:39, 13 April 2021
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.
Contents
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
Topic 1
Method
Results
Conclusion
References
[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.
[2] ...
