A Systematic Review of Available Datasets in Additive Manufacturing

• URL: http://arxiv.org/abs/2401.15448v1
• Date: Sat, 27 Jan 2024 16:13:32 GMT
• Title: A Systematic Review of Available Datasets in Additive Manufacturing
• Authors: Xiao Liu and Alessandra Mileo and Alan F. Smeaton
• Abstract summary: In-situ monitoring incorporating visual and other sensor technologies allows the collection of extensive datasets during the Additive Manufacturing process. These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning. This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria.
• Score: 56.684125592242445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-situ monitoring incorporating data from visual and other sensor technologies, allows the collection of extensive datasets during the Additive Manufacturing (AM) process. These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning during the manufacturing process. Open and annotated datasets derived from AM processes are necessary for the machine learning community to address this opportunity, which creates difficulties in the application of computer vision-related machine learning in AM. This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria. The review identifies existing gaps among the current image-based datasets in the domain of AM, and points to the need for greater availability of open datasets in order to allow quality assessment and defect detection during additive manufacturing, to develop.

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