Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting

Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting

Determining the quality of Ti-6Al-4V parts fabricated by selective laser melting (SLM) remains a challenge due to the high cost of SLM and the need for expertise in processes and materials. In order to understand the correspondence of the relative density of SLMed Ti-6Al-4V parts with process parame...

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Bibliographic Details
Main Authors: Miao Zou, Wu-Gui Jiang, Qing-Hua Qin, Yu-Cheng Liu, Mao-Lin Li
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Materials
Subjects:
machine learning
optimized XGBoost method
small dataset
selective laser melting
Ti-6Al-4V
Online Access:https://www.mdpi.com/1996-1944/15/15/5298

Internet

https://www.mdpi.com/1996-1944/15/15/5298

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