Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability

Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability

Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a case study on gear steel hardenability. The limitations of current data-driven algorithms and empirical models are identified. Challenges in analysing small datasets are discussed, and solution is prop...

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Bibliographic Details
Main Authors: Dong, H. (Author), Dong, Z. (Author), Li, H. (Author), Nenchev, B. (Author), Panwisawas, C. (Author), Tao, B. (Author), Tao, Q. (Author)
Format: Article
Language:English
Published: University of Science and Technology Beijing 2022
Subjects:
Algorithm model
Alloying elements
Case-studies
Current data
Data-driven algorithm
Empirical model
Forecasting
Gaussian methods
gear steel
Gear steels
hardenability
Hardenability
Large dataset
machine learning
Machine learning
Small data set
small dataset
Xgboost
XGboost
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