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 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

References

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[2] ...