Asset Details
Do you wish to reserve the book?
Application of Machine Learning Algorithms and SHAP for Prediction and Feature Analysis of Tempered Martensite Hardness in Low-Alloy Steels
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Application of Machine Learning Algorithms and SHAP for Prediction and Feature Analysis of Tempered Martensite Hardness in Low-Alloy Steels
Do you wish to request the book?
Application of Machine Learning Algorithms and SHAP for Prediction and Feature Analysis of Tempered Martensite Hardness in Low-Alloy Steels
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Application of Machine Learning Algorithms and SHAP for Prediction and Feature Analysis of Tempered Martensite Hardness in Low-Alloy Steels
2021
Overview
The tempering of low-alloy steels is important for controlling the mechanical properties required for industrial fields. Several studies have investigated the relationships between the input and target values of materials using machine learning algorithms. The limitation of machine learning algorithms is that the mechanism of how the input values affect the output has yet to be confirmed despite numerous case studies. To address this issue, we trained four machine learning algorithms to control the hardness of low-alloy steels under various tempering conditions. The models were trained using the tempering temperature, holding time, and composition of the alloy as the inputs. The input data were drawn from a database of more than 1900 experimental datasets for low-alloy steels created from the relevant literature. We selected the random forest regression (RFR) model to analyze its mechanism and the importance of the input values using Shapley additive explanations (SHAP). The prediction accuracy of the RFR for the tempered martensite hardness was better than that of the empirical equation. The tempering temperature is the most important feature for controlling the hardness, followed by the C content, the holding time, and the Cr, Si, Mn, Mo, and Ni contents.
