Creep Lifetime Prediction of Alloy 617 using Black Box Machine Learning Approach

This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Alloy 617. Black Box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The extended qualification process involving chemistry-processing-structure-performance assessment impeded the rapid implementation of suitable heat of alloys into targeted service. There is a critical need to shorten the development-to-deployment timeline for heats of an alloy at specific applications. To that end, black box ML is employed to expedite the HEAT qualification of Alloy 617 and predict performance from chemistry out to an unprecedented timescale. This study gathers creep data for Ni-based alloy 617 – a solid solution strengthened material from a wide range of data sources. Service conditions, mechanical properties, chemistry, and chemical ratios are provided as potential input features. The Pearson Correlation Coefficient with a 95% confidence bound is employed for input feature screening. In the ML algorithm, the selected input features are regarded as predictors, and rupture is regarded as the response. The 3-layered Neural Network (NN) was observed to be the best algorithm for the given data based on statistical rationale. The NN accurately predicts rupture across a range of isotherms and data sources. Post-audit validation confirms the applicability of NN algorithms to unseen data. The black box ML provides a pathway to predict the performance directly from chemistry and opens avenues to rapid heat qualification.