Abstract
Efficiency and accuracy of evaluation for the key to microstructure parameters in aero-engine materials is crucial for understanding the properties and performance. However, it is still a tough research topic to identify the important microstructural variables using conventional methods. In the present work, attribution (variable importance evaluation) methods based on neural networks have been systematically sorted out with a comprehensive understanding of the strengths and limitations. Microstructures of nickel base single-crystal alloys is taken as an example for discuss the variable importance evaluation methods, namely forward stepwise, backward stepwise, and partial derivative. Suggestions are provided for future application in material science and solid mechanics.
Sign-in/Register to access full text options 
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.
