International Journal of Productivity and Quality Management, 2018, 24, 3, 323.
analysis of variance, ANOVA, fatigue, fused deposition modelling, FDM, genetic programming, low cycle fatigue, LCF, and rapid prototyping.
Fused deposition modelling (FDM) can build parts with complex geometry with relatively less material waste and time from computer aided design (CAD) file saved in stereolithography (.stl) format. Since FDM builds functional parts, it is not only subjected to static loading but also dynamic loading. The behaviour of build parts under repetitive cyclic loading resulting in fatigue needs to be established because it affects functionality as well as the durability. The present study aims at investigating the mechanism of fatigue and influence of FDM process parameters on fatigue life when the build parts are subjected to repetitive cyclic loads. Low cycle fatigue (LCF) test is carried out under strain-controlled mode for better characterisation of fatigue life of FDM build parts. Using response surface methodology, the relationship between FDM process parameters and fatigue life is developed. Genetic programming (GP) technique is adopted to predict the fatigue life of the build parts.
International Journal of Productivity and Quality Management, 2011, 7, 1, 22.
dimensional accuracy, FDM, fused deposition modelling, grey Taguchi methods, ANNs, artificial neural networks, fuzzy inference modelling, rapid prototyping, layer thickness, part build orientation, raster angle, air gap, and raster width.
In the present work, effect of five factors viz., layer thickness, part build orientation, raster angle, raster to raster gap (air gap) and raster width each at three levels together with their interactions is studied on dimensional accuracy of fused deposition modelling (FDM) build part. Four performance characteristics i.e. percentage change in length, width, thickness and diameter considered in this study are converted into an equivalent response known as grey relational grade. Optimum factor levels are determined for maximisation of grey relational grade using grey-based Taguchi method. A fuzzy inference system (FIS) is proposed for prediction of overall dimensional accuracy using Taguchi's orthogonal array for developing inference engine. The results of FIS are compared with prediction values obtained through artificial neural network. It has been demonstrated that fuzzy model is able to predict overall dimensional accuracy at all operating condition to a high degree of accuracy.