Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer. (February 2022)

• Record Type: Journal Article
• Authors: Hossain, Md Imran; Chowdhury, Mohammad Asaduzzaman; Zahid, Md Shovon; Sakib-Uz-Zaman, Chowdhury; Rahaman, Mohammad Lutfar; Kowser, Md Arefin
• Is Part Of: Polymer testing. Volume 106(2022)
• Publication Date: 2022-02
• Subjects: 3D printing -- Machine learning -- Nanoparticles -- PLA -- HDPE -- Recycled plastics; Polymers -- Testing -- Periodicals; Polymères -- Tests -- Périodiques; 620.1920287
• DOI: 10.1016/j.polymertesting.2021.107429
• Languages: English
• ISSNs: 0142-9418

Abstract

Abstract: The application of Additive Manufacturing (AM), such as 3D printing for complex rapid prototyping and manufacturing is 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 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. Highlights: Manufacture of nanoparticle infused new 3D materials from recycled Plastic Product. New filament materials are produced in combination of recycle plastics and nanoparticles. Graphene coating is used in new 3D materials. Machine learning concept is used to design and discover new materials.