Accuracy prediction in fused deposition modeling

Abstract

Fused deposition modeling (FDM) is a common additive manufacturing (AM) technology able to fabricate physical prototypes directly from virtual model without geometrical complexity limitations. Initially used to create concept models to help product design stage, FDM developed as regard materials, accuracy, and the overall quality of the output improved, becoming suitable for end use. At present, it is employed in process chains to significantly shorten product development times and costs and to produce parts in small and medium batch. A critical drawback which inhibits its diffusion is the obtainable accuracy. Only few indications, relating the dimensional deviations, exist, and they are conflicting each other, not allowing a reliable prediction. In this paper, a geometrical model of the filament, dependent upon the deposition angle and layer thickness, has been developed in order to predict the obtainable part dimensions. The model has been validated by an experimental campaign. The specimens have been investigated by means of profilometer analysis in order to study macrogeometrical and microgeometrical aspects. Finally, a case study highlighted the reliability of the model. The direct implication of this work is the capability, in process planning, to know in advance if the FDM part dimensions will satisfy the specification and the component will fit with others. Moreover, this model can be employed to choose the suitable manufacturing strategy in order to comply with industrial constrains and scopes. © 2014 Springer-Verlag London

Investigation of design for additive manufacturing in professional design practice

“…Other reasons relate to the known technological limitations of AM and are consistent with the limitations identified by previous studies (Baumers et al 2016;Boschetto and Bottini 2014;Boschetto, Giordano, and Veniali 2013;Dimitrov et al 2014;Ford 2014;Gao et al 2015;Huang et al 2013;Oropallo and Piegl 2016). There are strong perceptions that AM processes are not repeatable, have poor surface finish and are generally not reliable.…”

Modelling of additive manufacturing processes: a review and classification

“…More specifically, surface roughness modelling is more extensive in ME, with the analytical approach of [124], the numerical of [125] and the empirical ones of [126][127][128]. Topology and dimensional accuracy issues have been modelled in [129,[132][133][134] using analytical methods, whereas in [125,135,136] numerical ones have been used and in [137][138][139][140][141] the empirical approach has been followed. Also, the dimensional deviations, caused by changes made in layer thickness and deposition angle, are analytically modelled in [129].…”

“…Topology and dimensional accuracy issues have been modelled in [129,[132][133][134] using analytical methods, whereas in [125,135,136] numerical ones have been used and in [137][138][139][140][141] the empirical approach has been followed. Also, the dimensional deviations, caused by changes made in layer thickness and deposition angle, are analytically modelled in [129]. Moreover, in [130], a FEA model is used for the evaluation of a part's distortions, using a parametric study, for the evaluation of the deposition parameters effects on residual stresses and part distortions.…”

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

“…As illustrated in the process mapping flowchart in [20], the significant process inputs include AM machine settings, part geometrical designs, build environment factors, and feedstock qualities. There have been numerous researches on the influence of AM machine settings like laser printing strategies [21], laser power, speed, spot diameter [22], powder layer thickness, preheat temperature [23], and hatching spacing [24] [25] on part quality as summarized in [26].…”