Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27782
Title: A new computational intelligence approach in formulation of functional relationship of open porosity of the additive manufacturing process
Authors: Garg, A
Lam, JSL
Savalani, MM 
Keywords: Additive manufacturing process
Open porosity prediction
Rapid prototyping modelling
Selective laser melting
Issue Date: 2015
Publisher: Springer
Source: International journal of advanced manufacturing technology, 2015, v. 80, no. 1-4, p. 555-565 How to cite?
Journal: International journal of advanced manufacturing technology 
Abstract: An additive manufacturing process of selective laser sintering (SLS) builds components of complex 3D shapes directly from metal powder. Past studies reveal that the properties of an SLS-fabricated prototype such as porosity, surface roughness, waviness, compressive strength, tensile strength, wear strength, and dimensional accuracy depend on the parameter settings of the SLS setup and can be improved by appropriate adjustment. In this context, the computational intelligence (CI) approach of multi-gene genetic programming (MGGP) can be used to formulate the model for understanding the process behavior. MGGP develops the model structure and its coefficients automatically. Despite being widely applied, MGGP generates models that may not give satisfactory performance on test data. The underlying reason is the inappropriate formulation procedure of the multi-gene model and the difficulty in model selection. Therefore, the present work proposes a new CI approach (ensemble-based MGGP (EN-MGGP)) that makes use of statistical and classification strategies for improving its generalization. The EN-MGGP approach is applied to the open porosity data obtained from the experiments conducted on an SLS machine, and its performance is compared to that of the standardized MGGP. The proposed EN-MGGP model outperforms the standardized model and is proven to capture the dynamics of the SLS process by unveiling dominant input process parameters and the hidden non-linear relationships.
URI: http://hdl.handle.net/10397/27782
ISSN: 0268-3768
EISSN: 1433-3015
DOI: 10.1007/s00170-015-6989-2
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