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

Cassidy Silbernagel, Adedeji Aremu, Ian Ashcroft

    Research output: Contribution to journalArticlepeer-review

    42 Citations (Scopus)
    359 Downloads (Pure)


    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: (Figure presented.).

    Original languageEnglish
    Pages (from-to)625-637
    Number of pages13
    JournalRapid Prototyping Journal
    Issue number4
    Early online date2 Dec 2019
    Publication statusPublished - 2 Dec 2019


    • Artificial intelligence
    • Additive manufacturing
    • 3D printing
    • Machine learning
    • Laser powder bed fusion
    • Parameter optimization

    ASJC Scopus subject areas

    • Mechanical Engineering
    • Industrial and Manufacturing Engineering


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