Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Abstract

Surface accuracy directly affects the surface quality and performance of mechanical parts. Circular hole, especially spatial non-planar hole set is the typical feature and working surface of mechanical parts. Compared with traditional machining methods, additive manufacturing (AM) technology can decrease the surface accuracy errors of circular holes during fabrication. However, an accuracy error may still exist on the surface of circular holes fabricated by AM due to the influence of staircase effect. This study proposes a surface accuracy optimization approach for mechanical parts with multiple circular holes for AM based on triangular fuzzy number (TFN). First, the feature lines on the manifold mesh are extracted using the dihedral angle method and normal tensor voting to detect the circular holes. Second, the optimal AM part build orientation is determined using the genetic algorithm to optimize the surface accuracy of the circular holes by minimizing the weighted volumetric error of the part. Third, the corresponding weights of the circular holes are calculated with the TFN analytic hierarchy process in accordance with the surface accuracy requirements. Lastly, an improved adaptive slicing algorithm is utilized to reduce the entire build time while maintaining the forming surface accuracy of the circular holes using digital twins via virtual printing. The effectiveness of the proposed approach is experimentally validated using two mechanical models.

Keywords

surface accuracy optimization / multiple circular holes / additive manufacturing (AM) / part build orientation / triangular fuzzy number (TFN) / digital twins

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51775494, 51821093, and 51935009), the National Key R&D Program of China (Grant No. 2018YFB1700701), the Science and Technology Project of Zhejiang Province, China (Grant No. 2019C01141), and the Zhejiang Provincial Basic Public Welfare Research Project, China (Grant Nos. LGG18E050007 and LGG21E050020).

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