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Title: A hybrid M5 ′ -genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process
Authors: Garg, A.
Tai, K.
Lee, C. H.
Savalani, M. M.
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2013
Source: Garg, A., Tai, K., Lee, C. H., & Savalani, M. M. (2013). A hybrid M5‘ -genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. Journal of intelligent manufacturing, 25(6), 1349-1365.
Series/Report no.: Journal of intelligent manufacturing
Abstract: Recent years have seen various rapid prototyping (RP) processes such as fused deposition modelling (FDM) and three-dimensional printing being used for fabricating prototypes, leading to shorter product development times and less human intervention. The literature reveals that the properties of RP built parts such as surface roughness, strength, dimensional accuracy, build cost, etc are related to and can be improved by the appropriate settings of the input process parameters. Researchers have formulated physics-based models and applied empirical modelling techniques such as regression analysis and artificial neural network for the modelling of RP processes. Physics-based models require in-depth understanding of the processes which is a formidable task due to their complexity. The issue of improving trustworthiness of the prediction ability of empirical models on test (unseen) samples is paid little attention. In the present work, a hybrid M5 ′ -genetic programming (M5 ′ -GP) approach is proposed for empirical modelling of the FDM process with an attempt to resolve this issue of ensuring trustworthiness. This methodology is based on the error compensation achieved using a GP model in parallel with a M5 ′ model. The performance of the proposed hybrid model is compared to those of support vector regression (SVR) and adaptive neuro fuzzy inference system (ANFIS) model and it is found that the M5 ′ -GP model has the goodness of fit better than those of the SVR and ANFIS models.
ISSN: 0956-5515
DOI: 10.1007/s10845-013-0734-1
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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