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

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 and powders; 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 can make 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.

Project team

Project students

• Anh Tran
• Nhat Nguyen

Supervisors

• Dr. Brian Ng
• Dr. Paul Colegrove (AML3D)

Objectives

The objective of this project is to increase the efficiency of the manufacture process at AML3D. In order to do so, the team will investigate into the possibility of automating and optimising the quality control processes. The two quality control processes that are currently being implemented at AML3D are measuring layer height using laser sensors, and human supervision for detecting defects. These processes add overhead into production time and usage of human resource, which is not desired. To achieve the goal, it is expected that machine learning methods will be used extensively to analyse the electrical signatures of the weld process.

References

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

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