Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26927
Title: State-of-the-art in empirical modelling of rapid prototyping processes
Authors: Garg, A
Tai, K
Savalani, MM 
Keywords: Artificial neural network
Empirical modelling
Fused deposition modeling
Rapid prototyping
Selective laser sintering
Stereolithograhpy
Issue Date: 2014
Publisher: Emerald Group Publishing Limited
Source: Rapid prototyping journal, 2014, v. 20, no. 2, 17106328, p. 164-178 How to cite?
Journal: Rapid Prototyping Journal 
Abstract: Purpose: The empirical modelling of major rapid prototyping (RP) processes such as fused deposition modelling (FDM), selective laser sintering (SLS) and stereolithography (SL) has attracted the attention of researchers in view of their contribution to the overall cost of the product. Empirical modelling techniques such as artificial neural network (ANN) and regression analysis have been paid considerable attention. In this paper, a powerful modelling technique using genetic programming (GP) for modelling the FDM process is introduced and the issues related to the empirical modelling of RP processes are discussed. The present work aims to investigate the performance of various potential empirical modelling techniques so that the choice of an appropriate modelling technique for a given RP process can be made. The paper aims to discuss these issues. Design/methodology/approach: Apart from the study of applications of empirical modelling techniques on RP processes, a multigene GP is applied to predict the compressive strength of a FDM part based on five given input process parameters. The parameter setting for GP is determined using trial and experimental runs. The performance of the GP model is compared to those of neural networks and regression analysis. Findings: The GP approach provides a model in the form of a mathematical equation reflecting the relationship between the compressive strength and five given input parameters. The performance of ANN is found to be better than those of GP and regression, showing the effectiveness of ANN in predicting the performance characteristics of the FDM part. The GP is able to identify the significant input parameters that comply with those of an earlier study. The distinct advantages of GP as compared to ANN and regression are highlighted. Several vital issues related to the empirical modelling of RP processes are also highlighted in the end. Originality/value: For the first time, a review of the application of empirical modelling techniques on RP processes is undertaken and a new GP method for modelling the FDM process is introduced. The performance of potential empirical modelling techniques for modelling RP processes is evaluated. This is an important step in modernising the era of empirical modelling of RP processes.
URI: http://hdl.handle.net/10397/26927
ISSN: 1355-2546
DOI: 10.1108/RPJ-08-2012-0072
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