... were used to explore the relationship between mechanical properties and print parameters. Furthermore, an artificial neural network (ANN) regression model was implemented to predict mechanical properties based on varying print conditions. Findings The results demonstrated that layer thickness has...

... height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net...

.... Then, aided by the captured phenomena (images), a smart printing failure predictor is built based on the artificial neural network (ANN). Finally, based on the predictor, the printing failures, as well as their types, can be effectively detected from the images captured by the camera in real-time. Findings...

... and a comprehensive test for model validation have not been adequately investigated. Therefore, this paper aims to establish a robust model using empirical data based on optimized artificial neural networks (ANNs) to estimate the surface roughness distribution in fused deposition modelling parts. Accordingly, process...

...-centred central composite design was used to collect data, and two methods, namely, response surface methodology (RSM) and artificial neural network (ANN) were used for predicting shrinkage. Sensitivity analysis based on the developed empirical equations has been carried out to determine the most...

... 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...