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Mathematical modelling of fused deposition modeling (FDM) 3D printing of poly vinyl alcohol parts through statistical design of experiments approach

Authors :
Universitat Politècnica de Catalunya. Doctorat en Ciència i Enginyeria dels Materials
Universitat Politècnica de Catalunya. CIEFMA-PROCOMAME - Disseny Microestructural i Fabricació Avançada de Materials
Moradi, Mahmoud
Karamimoghadam, Mojtaba
Meiabadi, Saleh
Casalino, Giuseppe
Ghaleeh, Mohammad
Baby, Bobymon
Ganapathi, Harikrishna
Jose, Jomal
Shahzad Abdulla, Muhammed
Tallon, Paul
Shamsborhan, Mahmoud
Rezayat, Mohammad
Paul, Satyam
Khodadad, Davood
Universitat Politècnica de Catalunya. Doctorat en Ciència i Enginyeria dels Materials
Universitat Politècnica de Catalunya. CIEFMA-PROCOMAME - Disseny Microestructural i Fabricació Avançada de Materials
Moradi, Mahmoud
Karamimoghadam, Mojtaba
Meiabadi, Saleh
Casalino, Giuseppe
Ghaleeh, Mohammad
Baby, Bobymon
Ganapathi, Harikrishna
Jose, Jomal
Shahzad Abdulla, Muhammed
Tallon, Paul
Shamsborhan, Mahmoud
Rezayat, Mohammad
Paul, Satyam
Khodadad, Davood
Publication Year :
2023

Abstract

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.<br />Peer Reviewed<br />Postprint (published version)

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1390664968
Document Type :
Electronic Resource