Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Abstract

Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

3D printing of bone and cartilage with polymer materials

“…The light source in the 3D printer continuously cross-links the photosensitive resin by scanning layer by layer, thereby accumulating a three-dimensional solid product ( Dadras-Toussi et al, 2022b ). As an excellent 3D printing consumable, the photosensitive resin has the advantages of high molding accuracy and short curing time and is suitable for processing precision devices ( Fang et al, 2020 ). Although the application of photosensitive resin in the field of 3D printing has been widespread, the performance of photosensitive resin has certain defects due to the material itself.…”

Three-Dimensional Printing Constructs Based on the Chitosan for Tissue Regeneration: State of the Art, Developing Directions and Prospect Trends

“…It is worth noting that the electro-spinning approach has gained considerable attention from biomedical experts all around the world for manufacturing various polymers, particularly CS-based polymer scaffolds loaded with drugs for biomedical and wound dressing purposes [ 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 ]. Application of CS-based biopolymers through 3D [ 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 ] printing for biomedical purposes especially as bio-ink in an effort to fabricate complex tissues will probably be an upcoming pattern of 3D printing materials progression. However, CS and CS/Col designed bio-printed skin tissue has not already obtained the FDA approval, taking into account the similarity of constructed materials with some other permitted materials.…”

Parameters prediction in additively manufactured Al-Cu alloy using back propagation neural network

“…Due to its high learning capacity, organisation, and error tolerance, ANN is particularly well-suited for addressing problems involving multiple variable impacts and multiple responses. At present, ANN technology has proven to be a versatile and adaptive tool that has been widely applied in real-time monitoring [13][14][15], dynamic simulation [16][17][18], defect recognition [19][20][21][22][23] and process optimisation [24][25][26][27]. Its ability to enhance efficiency and accuracy in diverse applications has made it increasingly valuable in numerous research and development domains.…”