A movel hybrid Bees Regression Convolutional Neural Network (BA-RCNN) applied to porosity prediction in selective laser melting parts

Alamri, Nawaf ORCID: https://orcid.org/0000-0002-5641-0178, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Bigot, Samuel ORCID: https://orcid.org/0000-0002-0789-4727 2024. A movel hybrid Bees Regression Convolutional Neural Network (BA-RCNN) applied to porosity prediction in selective laser melting parts. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023. Published in: Spezi, Emiliano and Bray, Michaela eds. Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press, pp. 95-99. 10.18573/conf1.w

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Abstract

Convolutional Neural Network (CNN) is a Deep Learning (DL) technique used for image analysis. CNN can be used in manufacturing, for predicting the percentage of porosity in the finished Selective Laser Melting (SLM) parts. This paper presents a new approach based on Regression Convolutional Neural Network (RCNN) for assessing the porosity which was better than the existing image binarization method. The algorithms were applied to artificial porosity images that were similar to the real images with a 0.9976 similarity index. The RCNN yielded a prediction accuracy of 75.50% compared to 68.60% for image binarization. After the RCNN parameters were optimized using the Bees Algorithm (BA), the application of the novel Bees Regression Convolutional Neural Network (BA-RCNN) improved the porosity prediction accuracy further to 85.33%. When three noise levels were used to examine its sensitivity to noise, the novel hybrid BA-RCNN was found to be less sensitive to noise.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QC Physics T Technology > TA Engineering (General). Civil engineering (General)
Additional Information:	Contents are extended abstracts of papers, not full papers
Publisher:	Cardiff University Press
ISBN:	978-1-9116-5349-3
Date of First Compliant Deposit:	10 June 2024
Date of Acceptance:	2024
Last Modified:	29 Jul 2024 15:08
URI:	https://orca.cardiff.ac.uk/id/eprint/169687

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