Publication:
PREDICTIVE MODELING OF DIMENSIONAL ACCURACIES IN 3D PRINTING USING ARTIFICIAL NEURAL NETWORK

Simple item page
dc.citedby1
dc.contributor.authorSivaraosen_US
dc.contributor.authorKumaran K.en_US
dc.contributor.authorDharsyanth R.en_US
dc.contributor.authorAmran M.en_US
dc.contributor.authorShukor S.M.en_US
dc.contributor.authorPujari S.en_US
dc.contributor.authorRamasamy D.en_US
dc.contributor.authorVatesh U.K.en_US
dc.contributor.authorMahdi Al-Obaidi A.S.H.en_US
dc.contributor.authorRamesh S.en_US
dc.contributor.authorLee K.Y.S.en_US
dc.contributor.authorid58116060300en_US
dc.contributor.authorid12761486500en_US
dc.contributor.authorid58694316000en_US
dc.contributor.authorid57453993100en_US
dc.contributor.authorid58694133100en_US
dc.contributor.authorid57212385887en_US
dc.contributor.authorid26325891500en_US
dc.contributor.authorid56270107300en_US
dc.contributor.authorid55744566600en_US
dc.contributor.authorid41061958200en_US
dc.contributor.authorid57221177925en_US
dc.date.accessioned2024-10-14T03:20:30Z
dc.date.available2024-10-14T03:20:30Z
dc.date.issued2023
dc.description.abstractAdditive manufacturing, particularly Fused Deposition Modeling (FDM) using three-dimensional (3D) printing, has revolutionized the manufacturing industry by offering design flexibility, customization options, affordability, and high printing speed. However, improper selection of process parameters in FDM can lead to suboptimal surface efficiency, defective mechanical properties, increased waste, and higher production costs. In this research, an Artificial Neural Network (ANN) model was developed to optimize dimensional properties in FDM by considering control factors such as layer thickness, orientation, raster angle, raster width, and air gap. Experimental data consisting of 27 sets of control parameters and corresponding dimensional outputs were used to train and validate the ANN model. The ANN model was developed using MATLAB software, employing training functions and learning algorithms to optimize the neural network architecture. The optimized ANN structure comprised 15 neurons and 2 layers, and it demonstrated accurate prediction of dimensional properties with percentage errors ranging from 0.01% to 25.49% for length, less than 10% for weight, and less than 4% for thickness. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to quantify the errors, indicating the effectiveness of the ANN model in predicting dimensional properties. The results highlight the potential of ANN in optimizing FDM process parameters for improved dimensional accuracy. The ANN model provides a reliable tool for manufacturers to predict and optimize the length, weight, and thickness of 3D-printed components, leading to enhanced product quality and reduced production costs. The developed ANN model can be further extended to consider other parameters and optimize various aspects of the additive manufacturing process. � School of Engineering, Taylor�s University.en_US
dc.description.natureFinalen_US
dc.identifier.epage2160
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85176590524
dc.identifier.spage2148
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176590524&partnerID=40&md5=fdd8c21eae32d78195646c53fc81504b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34540
dc.identifier.volume18
dc.pagecount12
dc.publisherTaylor's Universityen_US
dc.sourceScopus
dc.sourcetitleJournal of Engineering Science and Technology
dc.subjectAdditive manufacturing
dc.subjectArtificial neutral network
dc.subjectDimensional accuracy
dc.subjectFused deposition modelling
dc.subjectPredictive modelling
dc.titlePREDICTIVE MODELING OF DIMENSIONAL ACCURACIES IN 3D PRINTING USING ARTIFICIAL NEURAL NETWORKen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS