Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer

The application of Additive Manufacturing (AM), such as 3D printing for complex rapid prototyping and manufacturing moving fast beyond imagination and one of the most prospective AM techniques is Fused Filament Fabrication (FFF) process. The fabricated polymer components require diverse properties f...

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Autores principales:	Md Imran Hossain, Mohammad Asaduzzaman Chowdhury, Md Shovon Zahid, Chowdhury Sakib-Uz-Zaman, Mohammad Lutfar Rahaman, Md Arefin Kowser
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	3D printing Machine learning Nanoparticles PLA HDPE Recycled plastics Polymers and polymer manufacture TP1080-1185
Acceso en línea:	https://doaj.org/article/ed0c6d7125c24d5094cb36ce914aecd2
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id	oai:doaj.org-article:ed0c6d7125c24d5094cb36ce914aecd2
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spelling	oai:doaj.org-article:ed0c6d7125c24d5094cb36ce914aecd22021-12-02T04:58:54ZDevelopment and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer0142-941810.1016/j.polymertesting.2021.107429https://doaj.org/article/ed0c6d7125c24d5094cb36ce914aecd22022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S014294182100372Xhttps://doaj.org/toc/0142-9418The application of Additive Manufacturing (AM), such as 3D printing for complex rapid prototyping and manufacturing moving fast beyond imagination and one of the most prospective AM techniques is Fused Filament Fabrication (FFF) process. The fabricated polymer components require diverse properties for different applications and some of these properties can be obtained by using polymer composite filaments consisting of various materials in different proportions. Therefore, it is imperative to understand how different properties are affected by various compositions of materials in FFF fabricated polymer components. This study evaluates and compares various properties such as microstructure and surface texture, mechanical behavior, thermal properties, and other general characteristics of different polymer composites fabricated by 3-D printing technology. For this purpose, six polymer composite specimens are prepared where Polylactic acid (PLA) and High-density polyethylene (HDPE) are the primary materials in all of them. Recycled plastic and (or) TiO2 nanoparticles are mixed in four specimens, and graphene coating is done in two samples. Extruded filaments are consumed in the (FFF process, where the process parameters are determined under an optimization model from machine learning. FESEM, EDX, and Particle analysis confirm that the nozzle temperature, derived from machine learning, perfectly aligns with the polymers' surface texture, layer, and microstructure. Graphene-coated samples show good profiles in roughness testing. Regarding mechanical behavior analysis, tensile strength, elongation, and hardness tests are conducted. Here, the sample infused with 1% nanoparticle but void of recycled plastic presents sufficient mechanical strength, and the graphene-coated samples show the improved property in terms of elongation. Thermogravimetric analysis (TGA) and Differential scanning calorimeter (DSC) analysis authenticated the thermal properties of the samples. FTIR analysis identified the general characteristics in all specimens except the one with graphene-coating and recycled plastic.Md Imran HossainMohammad Asaduzzaman ChowdhuryMd Shovon ZahidChowdhury Sakib-Uz-ZamanMohammad Lutfar RahamanMd Arefin KowserElsevierarticle3D printingMachine learningNanoparticlesPLAHDPERecycled plasticsPolymers and polymer manufactureTP1080-1185ENPolymer Testing, Vol 106, Iss , Pp 107429- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	3D printing Machine learning Nanoparticles PLA HDPE Recycled plastics Polymers and polymer manufacture TP1080-1185
spellingShingle	3D printing Machine learning Nanoparticles PLA HDPE Recycled plastics Polymers and polymer manufacture TP1080-1185 Md Imran Hossain Mohammad Asaduzzaman Chowdhury Md Shovon Zahid Chowdhury Sakib-Uz-Zaman Mohammad Lutfar Rahaman Md Arefin Kowser Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
description	The application of Additive Manufacturing (AM), such as 3D printing for complex rapid prototyping and manufacturing moving fast beyond imagination and one of the most prospective AM techniques is Fused Filament Fabrication (FFF) process. The fabricated polymer components require diverse properties for different applications and some of these properties can be obtained by using polymer composite filaments consisting of various materials in different proportions. Therefore, it is imperative to understand how different properties are affected by various compositions of materials in FFF fabricated polymer components. This study evaluates and compares various properties such as microstructure and surface texture, mechanical behavior, thermal properties, and other general characteristics of different polymer composites fabricated by 3-D printing technology. For this purpose, six polymer composite specimens are prepared where Polylactic acid (PLA) and High-density polyethylene (HDPE) are the primary materials in all of them. Recycled plastic and (or) TiO2 nanoparticles are mixed in four specimens, and graphene coating is done in two samples. Extruded filaments are consumed in the (FFF process, where the process parameters are determined under an optimization model from machine learning. FESEM, EDX, and Particle analysis confirm that the nozzle temperature, derived from machine learning, perfectly aligns with the polymers' surface texture, layer, and microstructure. Graphene-coated samples show good profiles in roughness testing. Regarding mechanical behavior analysis, tensile strength, elongation, and hardness tests are conducted. Here, the sample infused with 1% nanoparticle but void of recycled plastic presents sufficient mechanical strength, and the graphene-coated samples show the improved property in terms of elongation. Thermogravimetric analysis (TGA) and Differential scanning calorimeter (DSC) analysis authenticated the thermal properties of the samples. FTIR analysis identified the general characteristics in all specimens except the one with graphene-coating and recycled plastic.
format	article
author	Md Imran Hossain Mohammad Asaduzzaman Chowdhury Md Shovon Zahid Chowdhury Sakib-Uz-Zaman Mohammad Lutfar Rahaman Md Arefin Kowser
author_facet	Md Imran Hossain Mohammad Asaduzzaman Chowdhury Md Shovon Zahid Chowdhury Sakib-Uz-Zaman Mohammad Lutfar Rahaman Md Arefin Kowser
author_sort	Md Imran Hossain
title	Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
title_short	Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
title_full	Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
title_fullStr	Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
title_full_unstemmed	Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
title_sort	development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3d printer
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/ed0c6d7125c24d5094cb36ce914aecd2
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Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer

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