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MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrations

DOI:10.1088/1361-651X/aaf0b8 期刊:Modelling and Simulation in Materials Science and Engineering 出版年份:2019 更新时间:2025-09-23 15:23:52
摘要: Alloys-by-design is a term used to describe new alloy development techniques based on numerical simulation. These approaches are extensively used for nickel-base superalloys to increase the chance of success in alloy development. During alloy production of numerically optimized compositions, unavoidable scattering of the element concentrations occurs. In the present paper, we investigate the effect of this scatter on the alloy properties. In particular, we describe routes to identify alloy compositions by numerical simulations that are more robust than other compositions. In our previously developed alloy development program package MultOpt, we introduced a sensitivity parameter that represents the influence of alloying variations on the final alloy properties in the post-optimization process, because the established sensitivity calculations require high computational effort. In this work, we derive a regression-based model for calculating the sensitivity that only requires one-time calculation of the regression coefficients. The model can be applied to any function with nearly linear behavior within the uncertainty range. The model is then successfully applied to the computational alloys-by-design work flow to facilitate alloy selection using the sensitivity of a composition owing to the inaccuracies in the manufacturing process as an additional minimization goal.
作者: Alexander Müller,Paul Git,Irina Roslyakova,Mario Sprenger,Ralf Rettig,Matthias Markl,Carolin K?rner,Robert F Singer
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To develop a fast regression-based model for calculating the sensitivity of alloy properties to variations in element concentrations, enabling robust optimization in alloy-by-design processes for nickel-base superalloys.

A fast regression-based model for property sensitivity has been developed and integrated into the optimization workflow, allowing for online sensitivity optimization with minimal computational overhead. This enables the selection of robust alloy compositions that are less affected by manufacturing inaccuracies, with an average prediction error of about 4%. The model can be extended to include database inaccuracies and future work should incorporate sensitivity calculations for third phases.

The model assumes nearly linear behavior of functions within the uncertainty range and does not account for interactions between design variables in sensitivity calculations. It also relies on the accuracy of CALPHAD databases, which may have uncertainties, especially for TCP phases. The sensitivity calculation for third phases is not included and requires post-optimization processing.

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