Weighted principal component approach for improving surface finish of ABS plastic parts built through fused deposition modelling process. (17th June 2011)

• Record Type: Journal Article
• Authors: Sood, Anoop Kumar; Mahapatra, S.S.; Ohdar, R.K.
• Is Part Of: International journal of rapid manufacturing. Volume 2:Number 1/2(2011)
• Page Start: 4
• Page End: 27
• Publication Date: 2011-06-17
• Subjects: rapid prototyping -- surface roughness -- PCA -- principal component analysis -- multi-objective optimisation -- BFOA -- bacteria foraging optimisation algorithm -- polymers -- extrusion -- quality control -- surface finish -- ABS plastic parts -- fused deposition modelling -- FDM -- surface quality -- process parameters -- layer thickness -- orientation -- raster angle -- raster width -- air gap; Manufacturing processes -- Periodicals; 670.4205
• Languages: English
• ISSNs: 1757-8817

Abstract

Fused deposition modelling (FDM) is a fast growing rapid prototyping technology due to its ability to build functional parts having complex geometrical shape in reasonable time period. For general engineering application, the surface finish of FDM processed part is inevitably excessively rough especially on the inclined surfaces of the parts. To minimise the surface roughness, a process improvement strategy through effective control of process parameters needs to be evolved. In this study, effect of five important process parameters, such as layer thickness, orientation, raster angle, raster width and air gap on surface roughness along three surfaces of test specimen, has been studied. Experiments have been conducted based on central composite design so as to reduce experimental runs. Empirical models relating response and process parameters are developed. The validity of the models is tested using analysis of variance and residuals. Experimental results indicate that process parameters and their interactions are different for minimisation of roughness of different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi-response performance index (MPI) using principal component analysis. Bacterial foraging optimisation algorithm, a latest evolutionary approach, has been adopted to find out best process parameter setting which maximises MPI.