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Mathematical Modelling of Fused Deposition Modeling (FDM) 3D Printing of Poly Vinyl Alcohol Parts through Statistical Design of Experiments Approach
Faculty of Arts, Science and Technology, University of Northampton, Northampton NN1 5PH, UK.ORCID iD: 0000-0003-2361-4796
Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy.ORCID iD: 0000-0002-4753-8030
Department of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre-Dame West, Montreal, QC H3C 1K3, Canada.ORCID iD: 0000-0002-9171-4812
Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy.ORCID iD: 0000-0003-1774-4631
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2023 (English)In: Mathematics, E-ISSN 2227-7390, Vol. 11, no 13, p. 3022-3022Article in journal (Refereed) Published
Abstract [en]

This paper explores the 3D printing of poly vinyl alcohol (PVA) using the fused deposition modeling (FDM) process by conducting statistical modeling and optimization. This study focuses on varying the infill percentage (10-50%) and patterns (Cubic, Gyroid, tri-hexagon and triangle, Grid) as input parameters for the response surface methodology (DOE) while measuring modulus, elongation at break, and weight as experimental responses. To determine the optimal parameters, a regression equation analysis was conducted to identify the most significant parameters. The results indicate that both input parameters significantly impact the output responses. The Design Expert software was utilized to create surface and residual plots, and the interaction between the two input parameters shows that increasing the infill percentage (IP) leads to printing heavier samples, while the patterns do not affect the weight of the parts due to close printing structures. On the contrary, the discrepancy between the predicted and actual responses for the optimal samples is below 15%. This level of error is deemed acceptable for the DOE experiments.

Place, publisher, year, edition, pages
2023. Vol. 11, no 13, p. 3022-3022
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64198DOI: 10.3390/math11133022ISI: 001028570600001Scopus ID: 2-s2.0-85164962701OAI: oai:DiVA.org:mdh-64198DiVA, id: diva2:1795062
Available from: 2023-09-07 Created: 2023-09-07 Last updated: 2023-09-11Bibliographically approved

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Paul, Satyam

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Moradi, MahmoudKaramimoghadam, MojtabaMeiabadi, SalehCasalino, GiuseppeShamsborhan, MahmoudRezayat, MohammadPaul, SatyamKhodadad, Davood
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