Mechanical response of additively manufactured foam: A machine learning approachShow others and affiliations
2022 (English)In: Results in Engineering (RINENG), ISSN 2590-1230, Vol. 16, article id 100801Article in journal (Refereed) Published
Abstract [en]
This paper uses ensemble and automated machine learning algorithms to predict the mechanical properties (tensile and flexural strength) of a three-dimensionally printed (3DP) foamed structure. The closed cell foams were made from the most commonly used thermoplastic, High-Density Polyethylene (HDPE). The hollow glass microspheres are infused in HDPE at varying volume %. The available data on these foams' mechanical properties are used by the chosen machine learning (ML) algorithms to propose the best suited algorithm for such a three-phased microstructure as these closed cell foams exhibit. Finally, the strength predictions from the models were validated using experimental data. The models were trained with nozzle temperature, bed temperature, and force values as input parameters. The output parameters predicted were the tensile and flexural strength. LightGBM outperforms all other models in terms of performance among ensemble-based models, while H2OAutoML outperforms all other models. All the ML algorithms produced models with greater than 95% accuracy. Finally, memory and time consumption for each model are presented. © 2022 The Authors
Place, publisher, year, edition, pages
2022. Vol. 16, article id 100801
Keywords [en]
Bending strength, Foams, High density polyethylenes, Learning algorithms, Learning systems, Machine learning, Tensile strength, 3-D printing, 3D-printing, Automated machines, Closed cell foams, GMB, High-density polyethylenes, Hollow glass microspheres, Machine learning algorithms, Machine learning approaches, Mechanical response, 3D printers, 3D printing, Composite, HDPE
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:du-45359DOI: 10.1016/j.rineng.2022.100801ISI: 000908361500007Scopus ID: 2-s2.0-85146232165OAI: oai:DiVA.org:du-45359DiVA, id: diva2:1734252
2023-02-062023-02-062023-02-06Bibliographically approved