Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing

Abstract

Purpose

Metal-based additive manufacturing is a relatively new technology used to fabricate metal objects within an entirely digital workflow. However, only a small number of different metals are proven for this process. This is partly due to the need to find a new set of parameters which can be used to successfully build an object for every new alloy investigated. There are dozens of variables which contribute to a successful set of parameters and process parameter optimisation is currently a manual process which relies on human judgement.

Design/methodology/approach

Here, the authors demonstrate the application of machine learning as an alternative method to determine this set of process parameters, the subject of this test is the processing of pure copper in a laser powder bed fusion printer. Data in the form of optical images were collected over the course of traditional parameter optimisation. These images were segmented and fed into a convolutional autoencoder and then clustered to find the clusters which best represented a high-quality result. The clusters were manually scored according to their quality and the results applied to the original set of parameters.

Findings

It was found that the machine-learned clustering and subsequent scoring reflected many of the observations which were found in the traditional parameter optimisation process.

Originality/value

This exercise, as well as demonstrating the effectiveness of the ML approach, indicates an opportunity to fully automate the approach to process optimisation by applying labels to the data, hence, an approach that could also potentially be suited for on-the-fly process optimisation.

Graphical abstract

Opens in a new window.

Citation

Silbernagel, C., Aremu, A. and Ashcroft, I. (2020), "Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing", Rapid Prototyping Journal, Vol. 26 No. 4, pp. 625-637. https://doi.org/10.1108/RPJ-08-2019-0213