Utilizing computational materials modeling and big data to develop printable high gamma prime superalloys for additive manufacturing

Abstract

Metal-based additive manufacturing offers potential to disrupt the manufacturing process across multiple industries. However, the vast majority of modern alloys are incompatible with the complex thermal histories of additive manufacturing. For example, the high gamma prime forming nickel-based superalloys are of considerable commercial interest owing to their properties; however, their gamma prime content renders them non-weldable and prone to cracking during additive manufacturing. Computational materials modeling and big data analytics is becoming an increasingly valuable tool for developing new alloys for additive manufacturing. This work reports the use of such tools toward the design of a high gamma prime superalloy with reduced cracking susceptibility while maintaining similar hardness to CM247. Experimental fabrication and characterization of the candidate alloys is performed. Results show the candidate alloys have improved printability, up to 41x reduction in crack density (mm/mm2) compared with CM247, and good agreement with the modeled predictions.