Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework

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 to the initial composition, and 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.

Review: Multi-principal element alloys by additive manufacturing

“…Moreover, fluctuations in laser scan speed, variation of mechanical properties (elasticity, friction coefficient, and damping coefficients) of powder particles, variation of diffusion coefficient of the material, uncertainty of absorption coefficient and measurement errors of AM increase the uncertainty of AM. With the added complexity imposed by the presence of multi-principal elements in MPEAs, it becomes even more challenging to obtain satisfactory repeatability and reproducibility of the AM process for these alloys [167][168][169]. For example, even for MPEA samples manufactured in the same batch within the same PBF machine using the same processing parameters, different microstructures, and properties may arise in the MPEAs.…”

Data‐Driven Approaches Toward Smarter Additive Manufacturing

“…A method to quantify the uncertainty between prealloyed and target powder composition is developed using a CALPHADbased integrated computational materials engineering (ICME) framework. [88] Uncertainty in AM part material composition, including feedstock variation between batches, localized compositional deviation from nominal feedstock, and contamination from powder recycling, negatively affects the success rate of the overall AM processes. Therefore, it is important to capture this uncertainty in manufacturing during AM materials design.…”

“…Therefore, it is important to capture this uncertainty in manufacturing during AM materials design. [88] This study identified an optimized average composition of the high-strength low-alloy (HSLA) powder used in AM, increasing the success rate of AM builds by 44.7%. [88] Other computational thermodynamics methods are also being integrated with CALPHAD to understand complex solidification events and phase formation in AM.…”

“…[88] This study identified an optimized average composition of the high-strength low-alloy (HSLA) powder used in AM, increasing the success rate of AM builds by 44.7%. [88] Other computational thermodynamics methods are also being integrated with CALPHAD to understand complex solidification events and phase formation in AM. An alternative diffusion module, DICTRA, enables a closer-to-practice prediction of solidification parameters by accounting for diffusion in the liquid and solid phases.…”

Post-heat treatment design for high-strength low-alloy steels processed by laser powder bed fusion

“…The composition of the HSLA-100 steel powder used for the LPBF process was modified based on the cast HSLA-100 composition using a high-throughput CALPHAD-based ICME (CALPHAD: Calculation of Phase Diagrams, ICME: Integrated Computational Materials Engineering) modeling technique, which will be described elsewhere [33]. Argon gas-atomized HSLA steel powder with composition (in wt.%) of Al: 0.006, C: 0.046, Cr: 0.4, Cu: 1.44, Mn: 0.9, Mo: 0.8, Nb: 0.03, Ni: 3.47, Si: 0.19 was manufactured by Praxair Co., within a mesh size of -200 to -325.…”