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| Naji, M. |
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| Motta, Antonella |
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| Ertürk, Emre |
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| Kočí, Jan | Prague |
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| Azam, Siraj |
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Fox, Kate
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Publications (3/3 displayed)
- 2023Algorithmic detection and categorization of partially attached particles in AM structures: a non-destructive method for the certification of lattice implantscitations
- 20203D-printed diamond-titanium composite: A hybrid material for implant engineeringcitations
- 2019Rational design of additively manufactured Ti6Al4V implants to control Staphylococcus aureus biofilm formationcitations
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article
Algorithmic detection and categorization of partially attached particles in AM structures: a non-destructive method for the certification of lattice implants
Abstract
<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.</jats:p> </jats:sec>