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Evaluation of Deviations in a Generative Designed Component Fabricated via Additive Manufacturing
Conference proceeding   Peer reviewed

Evaluation of Deviations in a Generative Designed Component Fabricated via Additive Manufacturing

Angelica Dianda, Lorenzo Maccioni and Yuri Borgianni
Manufacturing 2030: A Perspective to Future Challenges in Industrial Production; Proceedings of the 4th International Symposium on Industrial Engineering and Automation ISIEA 2025 and 18th EPIEM Conference 2025, Volume 1, Vol.1604, pp.338-345
Lecture Notes in Networks and Systems, 1604
ISIEA 2025 4th International Symposium on Industrial Engineering and Automation Manufacturing 2030: A Perspective to Future Challenges in Industrial Production (Bozen, 18/06/2025–20/06/2025)
2025
Handle:
https://hdl.handle.net/10863/51501

Abstract

Deviations Additive manufacturing Generative design Freeform
Additive Manufacturing (AM) displays unique capabilities in terms of fabricating parts including unconventional geometries. Much research is conducted to investigate the performances of AM from both the mechanical and the quality viewpoint. This research is aimed to complete the transition of AM from a rapid prototyping technology to a mature manufacturing option. Interestingly, this research is routinely conducted on standard geometries instead of those kind of topologies for which AM is an advantageous fabrication system. The study presented here originally compares the accuracy of AM for standard and complex shapes. As a case study, we used the model of a leg of a quadruped robot obtained with generative design in previous research. The model includes standard and non-standard geometries, with the latter being predominant. Ten copies of this model have been produced by AM, specifically by means of a 3D printer working with Fused Deposition Modelling. Two different orientations have been used for the model (five copies each). The 3D-printed physical models have been 3D-scanned and compared with the reference CAD model, which has allowed us to evidence deviations. The deviations have been displayed by using both global and local best-fit options in a quality assessment software application. The first analysis of achieved data suggests that printing parameters and the position of surfaces affect the accuracy of AM more than the characterization of surfaces as standard and non-standard.
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https://link.springer.com/chapter/10.1007/978-3-032-03698-8_28View

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