Bayesian analysis for geometric shapes in additive manufacturing

Abstract

In additive manufacturing, geometric shape deviations are built through statistical deviation models. Nonetheless, the resource constraints limit the manufacturers to test shapes. However, in addition to the power, the simplicity of the deviation models has been demonstrated with illustrative cases for in-plane deviation modelling for regular pentagon, irregular octagon as well as straight edges in free-form shapes utilizing only data and models for single regular pentagon and cylinders. Bayesian deviation models, built for geometric shapes, provide a better fit on the data and help in achieving better predictive accuracy. The main aim of this study is to assess the effect of the error distribution, generally assumed as the normal, and evaluate its impact on the deviation model. In particular, we consider Laplace, logistic, Cauchy, and exponential distributions for the errors. It is shown that logistic distribution provides better alternative error distribution for deviation models as compared to the normal distribution.