Predicting the porosity in selective laser melting parts using hybrid regression convolutional neural network

Alamri, Nawaf Mohammad H. 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 2022. Predicting the porosity in selective laser melting parts using hybrid regression convolutional neural network. Applied Sciences 12 (24) , 12571. 10.3390/app122412571

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Abstract

Assessing the porosity in Selective Laser Melting (SLM) parts is a challenging issue, and the drawback of using the existing gray value analysis method to assess the porosity is the difficulty and subjectivity in selecting a uniform grayscale threshold to convert a single slice to binary image to highlight the porosity. This paper proposes a new approach based on the use of a Regression Convolutional Neural Network (RCNN) algorithm to predict the percent of porosity in CT scans of finished SLM parts, without the need for subjective difficult thresholding determination to convert a single slice to a binary image. In order to test the algorithm, as the training of the RCNN would require a large amount of experimental data, this paper proposed a new efficient approach of creating artificial porosity images mimicking the real CT scan slices of the finished SLM part with a similarity index of 0.9976. Applying RCNN improved porosity prediction accuracy from 68.60% for image binarization method to 75.50% using the RCNN. The algorithm was then further developed by optimizing its parameters using Bees Algorithm (BA), which is known to mimic the behavior of honeybees, and the hybrid Bees Regression Convolutional Neural Network (BA-RCNN) produced better prediction accuracy with a value of 85.33%.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2076-3417
Date of First Compliant Deposit:	14 December 2022
Date of Acceptance:	5 December 2022
Last Modified:	29 Aug 2024 12:54
URI:	https://orca.cardiff.ac.uk/id/eprint/154858

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