Machine Learning Prediction of Creep Rupture Time for Steels

Machine learning algorithms for time series in financial markets

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

Representation of the Heat Specific Creep Rupture Behavior of 9%Cr Steels using Neural Networks

The present work aims to demonstrate an innovative way of application of modern data analysis tools for structural power plant materials, such as 9%Cr-steels. The purpose is the optimized prediction of the material behaviour considering the stress/time to rupture data and the determination of position of the specific melt in the scatter band for the given steel. This data is needed for an effec...

A Creep Rupture Time Model for Anisotropic Creep-damage of Transversely-isotropic Materials

Anisotropic creep-damage modeling has become an increasingly important prediction technique in both the aerospace and industrial gas turbine industries. The introduction of tensorial damage mechanics formulations in modeling tertiary creep behavior has lead to improved predictions of the creep strain that develops due to anisotropic grain structures and the induced anisotropy that occurs with i...

Robust prediction of full creep curves from minimal data and time to rupture model

Comparison of Machine Learning Algorithms for Software Project Time Prediction

Software Project Management (SPM) is one of the primary factors to software success or failure. Prediction of software development time is the key task for the effective SPM. The accuracy and reliability of prediction mechanisms is also important. In this paper, we compare different machine learning techniques in order to accurately predict the software time. Finally, by comparing the accuracy ...