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Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method
AbstractThis paper presents experimental investigations on influence of important process parameters viz., layer thickness, part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) processed ABSP400 (acrylonitrile-butadine-styrene) part. It is observed that shrinkage is dominant along length and width direction of built part. But, positive deviation from the required value is observed in the thickness direction. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. Therefore, all the three responses are expressed in a single response called grey relational grade. Finally, grey Taguchi method is adopted to obtain optimum level of process parameters to minimize percentage change in length, width and thickness simultaneously. The FDM process is highly complex one and hardly any theoretical model exist for the prediction purpose. The process parameters influence the responses in a highly non-linear manner. Therefore, prediction of overall dimensional accuracy is made based on artificial neural network (ANN).
Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method
AbstractThis paper presents experimental investigations on influence of important process parameters viz., layer thickness, part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) processed ABSP400 (acrylonitrile-butadine-styrene) part. It is observed that shrinkage is dominant along length and width direction of built part. But, positive deviation from the required value is observed in the thickness direction. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. Therefore, all the three responses are expressed in a single response called grey relational grade. Finally, grey Taguchi method is adopted to obtain optimum level of process parameters to minimize percentage change in length, width and thickness simultaneously. The FDM process is highly complex one and hardly any theoretical model exist for the prediction purpose. The process parameters influence the responses in a highly non-linear manner. Therefore, prediction of overall dimensional accuracy is made based on artificial neural network (ANN).
Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method
Sood, Anoop Kumar (author) / Ohdar, R.K. (author) / Mahapatra, S.S. (author)
2009-04-21
10 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2009
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