Fuzzy model-based design for testing and qualification of additive manufacturing components

Abstract

The uncertainties and variation of additive manufacturing (AM) material properties and their impact on product quality trouble designers. The lack of experience in AM technologies renders the experts’ assessment of AM components and the establishment of safety margins difficult. Consequently, unexpected qualification difficulties resulting in expensive and lengthy redesign processes might arise. To reduce the risk of qualification failure, engineers might perform copious time-consuming and expensive specimen testing in early phases, or establish overconservative design margins, overriding the weight reduction benefits of AM technologies. In this article, a model-based design method is proposed for the conceptual design of AM space components with affordable test phases. The method utilizes fuzzy logics to systematically account for experts’ assessment of AM properties variation, and to provide an early estimation of a product qualification likelihood related to design parameters of interest, without the need for copious testing. The estimation of qualification likelihood can also point out which are the unique AM material uncertainties that require further specific testing, to enable the design of a product with a better performance and more affordable test phases. The method is demonstrated with the design for AM gridded of ion thrusters for satellite applications.

Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System

“…Yuan et al [18] also obtained a similar degree of accuracy for the 3D-printed dental implants. Borgue et al [19] considered that imperfections in material properties can lead to errors in 3D printing. They developed a fuzzy logic-based approach for designfor-AM to manage uncertainties in material properties while meeting the quality standards of 3D printed objects.…”

Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System

“…Yuan et al [18] also obtained a similar degree of accuracy for the 3D-printed dental implants. Borgue et al [19] considered that imperfections in material properties can lead to errors in 3D printing. They developed a fuzzy-logic-based approach for design for AM to manage uncertainties in material properties while meeting the quality standards of 3D-printed objects.…”