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The purpose of this study is twofold: firstly, to investigate the effect of the infill value and build orientation on the fatigue behaviour of polylactic acid (PLA) specimens made by fused filament fabrication (FFF), also known as fused deposition modelling; and secondly, to model the fatigue behaviour of PLA specimens made by FFF and similar additive manufactured parts.

A new methodology based on filament characterisation, infill measuring, axial fatigue testing and fatigue strength normalisation is proposed and implemented. Sixty fatigue FFF specimens made of PLA were fabricated and evaluated using variable infill percentage and build orientation. On the other hand, fatigue modelling is based on the normalised stress amplitude and the fatigue life in terms of number of cycles. In addition, a probabilistic model was developed to predict the fatigue strength and life of FFF components.

The infill percentage and build orientation have a great influence on the fatigue behaviour of FFF components. The larger the infill percentage, the greater the fatigue strength and life. Regarding the build orientation, the specimens in the up-right orientation showed a much smaller fatigue strength and life than the specimens in the flat and on-edge orientations. Regarding the fatigue behaviour modelling, the proposed Weibull model can predict with an acceptable reliability the stress-life performance of PLA-FFF components.

This study has been limited to axial fatigue loading conditions along three different build orientations and only one type of material.

The results of this study are valuable to predict the fatigue behaviour of FFF parts that will work under variable loading conditions. The proposed model can help designers and manufacturer to reduce the need of experimental tests when designing and fabricating FFF components for fatigue conditions.

A fatigue study based on a novel experimental methodology that considers the variation of the FFF process parameters, the measurement of the real infill value and the normalisation of the results to be comparable with other studies is proposed. Furthermore, a new fatigue model able to predict the stress-life fatigue behaviour of PLA-FFF components considering variable process parameters is also proposed.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.