Optimizing Time-Constrained Multi-Objective Process Parameters for Thin-Walled Maraging Steels Manufactured by Laser Powder Bed Fusion (LPBF)

Abstract

Additive manufacturing is an innovative manufacturing process that enables complex topological structures and low-volume, high-variety production. One of the major adaptations of this method is in the tire industry. Thin-walled sipes slit the tires to improve drainage and traction. The material properties of thin-walled structures manufactured by additive manufacturing are different and more sensitive than those of conventional cube-shaped specimens. Thin-walled maraging steel specimens are considered to be able to model the relationship between the process parameters and the properties of the sipes adequately. Tire sipes are made of maraging steel. Maraging steels are a class of low-carbon high-alloy martensitic steel generally providing high strength, ductility, and good fracture toughness. In particular, these alloys exhibit a good combination of strength and toughness at elevated temperatures, which has been desirable for applications in aerospace and tooling. In order to consider productivity, multi-objective process parameter optimization with a build-time-constrained model is proposed.

Keywords

• additive manufacturing
• machine learning
• multi-objective optimization
• lpbf (laser powder bed fusion)