Deterministic part orientation in additive manufacturing using feature recognition

Abstract

Additive Manufacturing (AM) is becoming an integral part of modern manufacturing systems and therefore, the AM technologies needs to adhere to strict quality demands. Due to the layered nature of AM, the part build orientation has a major influence on final part properties. Previous efforts to optimize the part orientation largely utilizes evolutionary algorithms, which are stochastic in nature. This paper argues for a deterministic solution to facilitate automation and standardization, and proposes a method utilizing feature recognition for faster computation. A case study for selective lase sintering is presented to demonstrate the feasibility of the proposed method.