Abstract

This paper aims to develop a method for optimizing the geometry of complex-shaped additive manufacturing (AM) parts to minimize the need for assembly. The proposal relies on functional analysis and machine learning. The CA-3 automatic coupling mechanism for railway carriages was chosen as a design prototype. The proposed approach makes it possible to move from conventional to non-assembly AM designs with movable parts that require less material to be fabricated. The results can be used to develop new software solutions, technologies, and design strategies for fabricating complex AM mechanisms.

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