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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 575An Ensemble Approach of Machine Learning in...

An Ensemble Approach of Machine Learning in Evaluation of Mechanical Property of the Rapid Prototyping Fabricated Prototype

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Abstract:

Rapid prototyping (RP) is a promising product development technology due to its unique characteristic of fabricating functional products timely and efficiently. Fused deposition modelling (FDM) process based on RP technology is used in industries for prototype fabrication and its properties testing. The properties of the RP fabricated prototypes such as wear strength, tensile strength, dimensional accuracy, etc. depends on the parameter settings of the RP machines. For selecting the appropriate parameter settings, various mathematical models developed based on physics and data can be formulated. In the present work, we introduced an ensemble method of genetic programming (GP) and artificial neural network for formulating a model for predicting the wear strength of the FDM fabricated prototype. The results indicate that ensemble model have performed better than that of the standardised GP, which may be then used by experts for optimising the performance of the FDM process.

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Materials Engineering and Automatic Control III

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Periodical:

Applied Mechanics and Materials (Volume 575)

Pages:

493-496

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.575.493

Citation:

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Online since:

June 2014

Authors:

Akhil Garg*, K. Tai

Keywords:

Ensemble Model, Fused Deposition Modeling (FDM), Wear Strength Modelling

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* - Corresponding Author

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