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.
Highlights
The ability to produce complex geometries, the capability of processing small batches with low cost, and the capacity to perform in situ repair, make alloy additive manufacturing (AM) a market worth billions of dollars[1]
By employing the integrated computational materials engineering (ICME) framework developed in this work to optimize the composition of the high-strength low-alloy steel (HSLA)-115 steel powders, the probability of achieving the desired properties in the AM build with heat treatment increases significantly
The HSLA steels are widely used in many structural applications, such as bridges, ship hulls, and mining equipment[32,33,34,35]
Summary
The ability to produce complex geometries, the capability of processing small batches with low cost, and the capacity to perform in situ repair, make alloy additive manufacturing (AM) a market worth billions of dollars[1]. We expected that with the implementation of uncertainty quantification through such an ICME model framework, the nominal composition of the cast HSLA-115 steel could be optimized to increase the likelihood of successful AM builds, which should meet all the property requirements after heat treatment. The following models were applied for predicting the properties: (1) CALPHAD (Calculation of Phase Diagrams) method[18] in combination with phenomenological models for predicting the dislocation density[19], grain size[20,21], impact transition temperature (ITT)[22], and carbon equivalent[23]; (2) data-mining decision tree model for martensite start (MS) temperature[24]; and (3) physics-based strengthening model[17] consisting of the simulation of hardening effect caused by dislocations[25], grain boundaries[26,27], precipitates[28,29] and solid solution atoms[30,31] to predict the yield strength, low-temperature ductility, and weldability for a given composition and heat treatment process. By employing the ICME framework developed in this work to optimize the composition of the HSLA-115 steel powders, the probability of achieving the desired properties in the AM build with heat treatment increases significantly
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
