Deviation Modeling and Shape transformation in Design for Additive Manufacturing

Abstract : Additive Manufacturing (AM) technologies have gained extensive applications due to their capability to manufacture parts with complex shape, architected materials and multiple structure. However, the dimensional and geometrical accuracy of the resulting product remain a bottleneck for AM regarding quality assurance and control. Design for Additive Manufacturing (DfAM) aims at using different methodologies to help designer take into account the technological or geometrical specificities of AM, to maximize product performance during the design stage. As a main concern in DfAM, the consistency between the digital product and the final outcome should be effectively assessed. Therefore, the geometric deviations between designed model and real product should be modeled, in order to derive correction and compensation plans to increase geometrical accuracy, or to predict product performance more precisely. In this paper, a new deviation modeling method based on the STL file is proposed. A new shape transformation method is developed based on contour point displacement. In each slice, systematic deviations are represented by polar and radial functions and random deviations are modeled by translating the contour points with a given distance derived from the random field theory. The proposed method makes a good prediction of both repeatable and unexpected deviations of product shape, thus providing the designer with meaningful information for design improvement.
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Zuowei Zhu, Nabil Anwer, Luc Mathieu. Deviation Modeling and Shape transformation in Design for Additive Manufacturing. 27th CIRP Design Conference, May 2017, Cranfield, United Kingdom. ⟨10.1016/j.procir.2017.01.023⟩. ⟨hal-01555247⟩

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