Mass optimisation of 3D-printed specimens using multivariable regression analysis

Doicin, Cristian-Vasile and Ulmeanu, Mihaela-Elena and Rennie, Allan and Lupeanu, Elena (2021) Mass optimisation of 3D-printed specimens using multivariable regression analysis. International Journal of Rapid Manufacturing, 10 (1). pp. 1-22. ISSN 1757-8817

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Abstract

Fused deposition modelling popularity is attributed to equipment affordability, materials availability and open-source software. Given the variety of optimisation combinations, process parameters can be elaborate. This paper provides methods for optimisation of mass calculation using multivariable regression analysis. Layer thickness, extrusion temperature and speed were considered independent variables for a two-level factorial experiment. DOE was used for 12 sets of programs and analysis (two stages) undertaken using Design-Expert® V11 Software. In stage-1, four models were found to be significant. Stage-2 involved redesigning the remaining eight models, iteratively increasing the number of replicates and blocks. Adequacy of models was analysed, demonstrating that: model is significant, F-value is large, p < 0.05; lack of fit is insignificant; adequate precision >4.00; residuals are well behaved; R^2 is as close as possible to 1.00 or for models with multiple replicates, the adjusted R^2 and predicted R^2 differential <0.2. All models were validated through measured, calculated responses.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Rapid Manufacturing
Subjects:
ID Code:
169197
Deposited By:
Deposited On:
28 Apr 2022 13:05
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Apr 2022 13:05