Cargando…

Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm

Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively emp...

Descripción completa

Detalles Bibliográficos
Autores principales: Chohan, Jasgurpreet Singh, Mittal, Nitin, Kumar, Raman, Singh, Sandeep, Sharma, Shubham, Singh, Jujhar, Rao, Kalagadda Venkateswara, Mia, Mozammel, Pimenov, Danil Yurievich, Dwivedi, Shashi Prakash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599457/
https://www.ncbi.nlm.nih.gov/pubmed/33007848
http://dx.doi.org/10.3390/polym12102250
_version_ 1783602879026692096
author Chohan, Jasgurpreet Singh
Mittal, Nitin
Kumar, Raman
Singh, Sandeep
Sharma, Shubham
Singh, Jujhar
Rao, Kalagadda Venkateswara
Mia, Mozammel
Pimenov, Danil Yurievich
Dwivedi, Shashi Prakash
author_facet Chohan, Jasgurpreet Singh
Mittal, Nitin
Kumar, Raman
Singh, Sandeep
Sharma, Shubham
Singh, Jujhar
Rao, Kalagadda Venkateswara
Mia, Mozammel
Pimenov, Danil Yurievich
Dwivedi, Shashi Prakash
author_sort Chohan, Jasgurpreet Singh
collection PubMed
description Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings.
format Online
Article
Text
id pubmed-7599457
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75994572020-11-01 Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm Chohan, Jasgurpreet Singh Mittal, Nitin Kumar, Raman Singh, Sandeep Sharma, Shubham Singh, Jujhar Rao, Kalagadda Venkateswara Mia, Mozammel Pimenov, Danil Yurievich Dwivedi, Shashi Prakash Polymers (Basel) Article Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings. MDPI 2020-09-30 /pmc/articles/PMC7599457/ /pubmed/33007848 http://dx.doi.org/10.3390/polym12102250 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chohan, Jasgurpreet Singh
Mittal, Nitin
Kumar, Raman
Singh, Sandeep
Sharma, Shubham
Singh, Jujhar
Rao, Kalagadda Venkateswara
Mia, Mozammel
Pimenov, Danil Yurievich
Dwivedi, Shashi Prakash
Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
title Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
title_full Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
title_fullStr Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
title_full_unstemmed Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
title_short Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
title_sort mechanical strength enhancement of 3d printed acrylonitrile butadiene styrene polymer components using neural network optimization algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599457/
https://www.ncbi.nlm.nih.gov/pubmed/33007848
http://dx.doi.org/10.3390/polym12102250
work_keys_str_mv AT chohanjasgurpreetsingh mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT mittalnitin mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT kumarraman mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT singhsandeep mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT sharmashubham mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT singhjujhar mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT raokalagaddavenkateswara mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT miamozammel mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT pimenovdanilyurievich mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm
AT dwivedishashiprakash mechanicalstrengthenhancementof3dprintedacrylonitrilebutadienestyrenepolymercomponentsusingneuralnetworkoptimizationalgorithm