###### Abstract  
Fused Deposition Modelling (FDM) enables the fabrication of entire non-assembly mechanisms within a single process step, making previously required assembly steps dispensable. Besides the advantages of FDM, the manufacturing of these mechanisms implies some shortcomings such as comparatively large joint clearances and geometric deviations depending on machine-specific process parameters. The current state-of-the-art concerning statistical tolerance analysis lacks in providing suitable methods for the consideration of these shortcomings, especially for 3D-printed mechanisms. Therefore, this contribution presents a novel methodology for ensuring the functionality of fully functional non-assembly mechanisms in motion by means of a statistical tolerance analysis considering geometric deviations and joint clearance. The process parameters and hence the geometric deviations are considered in terms of empirical predictive models using machine learning (ML) algorithms, which are implemented in the tolerance analysis for an early estimation of tolerances and resulting joint clearances. Missing information concerning the motion behaviour of the clearance affected joints are derived by a multi-body-simulation (MBS). The exemplarily application of the methodology to a planar 8-bar mechanism shows its applicability and benefits. The presented methodology allows evaluation of the design and the chosen process parameters of 3D-printed non-assembly mechanisms through a process-oriented tolerance analysis to fully exploit the potential of Additive Manufacturing (AM) in this field along with its ambition: 'Print first time right'. additive manufacturing; fused deposition modelling; statistical tolerance analysis; non-assembly mechanisms; empirical predictive models; geometric deviations +
Footnote †: journal: Applied  
1 February 2021  
11 February 2021  
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**1. Introduction and Motivation**  
Additive Manufacturing (AM) processes have successfully established themselves in industrial applications due to their batch size independent manufacturing costs and the great freedom of design [1]. Recently, 3D-printing of non-assembly mechanisms via Fused Deposition Modelling (FDM) or Stereolithography (SLA) has attracted the attention of research, as a subsequent assembly step becomes thereby dispensable which in turn can help reduce costs [2, 3]. Consequently, whole functional assemblies including movable parts can be fabricated as one component [4]. Figure 1 shows a non-assembly mechanism 3D-printed via FDM. The moveable parts are thereby separated by water-soluble support material for a non-destructive removability to ensure their mobility and thus the functionality of the mechanism after the support has been removed. However, the manufacturing of these mechanisms still implies some shortcomings, viz. comparatively large joint clearances, ensuring the separability of the parts and geometric deviations, mainly influenced by the choice of machine-specific process parameters. Especially geometric deviations remain a major drawback that prevents the further progress of AM in the field of 3D-printing non-assembly mechanisms and hence its usage in industrial applications [2, 5]. In order to meet the principle 'Print first time right' of AM, the virtual assurance of the functionality of these mechanisms through a statistical tolerance analysis with consideration of geometric deviations and joint clearance is essential. Although there has been research effort regarding the statistical tolerance analysis of systems in motion, also using predictive models, the current state-of-the-art lacks in providing suitable methods for the consideration of both geometric part deviations and comparatively large joint clearance, especially for additively manufactured non-assembly mechanisms [6; 7].