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

التفاصيل البيبلوغرافية
العنوان: Development and analysis of nanoparticle infused plastic products manufactured by machine learning guided 3D printer
المؤلفون: Imran Hossain, Arefin Kowser, Shovon Zahid, Mohammad Lutfar Rahaman, Chowdhury Sakib-Uz-Zaman, Mohammad Asaduzzaman Chowdhury
المصدر: Polymer Testing, Vol 106, Iss, Pp 107429-(2022)
بيانات النشر: Elsevier, 2022.
سنة النشر: 2022
مصطلحات موضوعية: Thermogravimetric analysis, Materials science, Polymers and Plastics, Recycled plastics, Fused filament fabrication, HDPE, Machine learning, computer.software_genre, Indentation hardness, Ultimate tensile strength, Polymers and polymer manufacture, chemistry.chemical_classification, business.industry, Organic Chemistry, Polymer, 3D printing, Microstructure, TP1080-1185, chemistry, Void (composites), Nanoparticles, PLA, High-density polyethylene, Artificial intelligence, business, computer
الوصف: 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.
اللغة: English
تدمد: 0142-9418
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd52086a47e86badf1e7121864e9db78
http://www.sciencedirect.com/science/article/pii/S014294182100372X
Rights: OPEN
رقم الانضمام: edsair.doi.dedup.....cd52086a47e86badf1e7121864e9db78
قاعدة البيانات: OpenAIRE