Knowledge-based manufacturability assessment for optimization of additive manufacturing processes based on automated feature recognition from CAD models

Abstract

Additive manufacturing (AM) is a significant development of the manufacturing sector that has emerged during the last decades and tends to change the way products are designed, manufactured, and repaired, enabling unprecedented flexibility levels. The unique process mechanism of AM enables the realization of complex designs after considering design limitations, which are unique to each process mechanism and machine. These limitations, together with the lack of established AM-related knowledge, lead to the design of parts that are not fully conforming with AM buildability restrictions, resulting in failed builds. To this end, this work presents a methodology that enables to embed the AM-related knowledge and use it for an automated manufacturability assessment. The 3D model of a part is used as an input in an.stp format, and the features that are relevant for AM manufacturability are recognized from the global CAD. Then, an analysis of the manufacturability of these features according to the limitations of the process and/or machine is performed, and design changes are suggested to the user aiming to improve the process outputs. The whole methodology is implemented in a software tool with an intuitive user-interface that supports the users in the design of parts that will be made with additive manufacturing.