Uncertainty Quantification and Composition Optimization for Alloy Additive Manufacturing Through a CALPHAD-based ICME Framework

TL;DR

This paper presents a CALPHAD-based ICME framework for high-throughput alloy composition optimization in additive manufacturing, reducing uncertainties and increasing success probability for AM builds, demonstrated on high-strength low-alloy steel.

Contribution

It introduces a novel high-throughput method combining CALPHAD and ICME for alloy composition optimization with uncertainty quantification in AM.

Findings

Optimized composition increased AM success probability by 44.7%.

Analyzed 450,000 compositions for key properties.

Framework applicable to various alloy systems.

Abstract

During powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME: integrated computational materials engineering) to optimize the composition, and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed the process-structure-property relationships for 450,000 compositions around the nominal composition of HSLA-115. Properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition. With the same uncertainty as the initial composition, an optimized average composition has been determined, which increased the probability of achieving successful AM builds by 44.7%. The present strategy is general and can be applied to other alloy composition optimization to expand the choices of alloy for additive manufacturing. Such a method also calls for high-quality CALPHAD databases and predictive ICME models.