Wei Hu
This thesis presents a data-driven approach to the design of high strength low alloy (HSLA) steel, focusing on metallurgical properties and modeling techniques. The objective is to enhance HSLA steel's performance by employing advanced data mining and modeling strategies. A comprehensive methodology is developed, incorporating linear regression models, artificial neural networks, and recursive partitioning techniques to evaluate various properties of HSLA steel. The results indicate significant improvements in yield strength and overall material performance through the application of these methods. This study contributes to the field of materials science by demonstrating the potential of data-driven approaches in optimizing steel design, thereby providing valuable insights for future research and industrial applications.
@article{25587ca8-8438-4d60-a3dc-b83f04c634fc,
title={Data-driven metallurgical design for high strength low alloy (HSLA) steel},
author={Wei Hu},
year={2008},
language={English}
}TY - JOUR TI - Data-driven metallurgical design for high strength low alloy (HSLA) steel AU - Wei Hu PY - 2008 LA - English ER -
Richard Ngenda Banka
Ce document présente une synthèse de la métallurgie du cobalt, en abordant successivement ses propriétés, ses minerais et ses procédés d’extraction. I
Blair Burwell
The exhaustion of current vanadium sources and the emergence of new applications are driving the need for innovative methods of extraction. The aim of
Rajesh Jha, Bimal Kumar Jha
This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t
Reza Arghandeh, Alexandra von Meier
Modern society relies heavily upon complex and widespread electric grids. In recent years, advanced sensors, intelligent automation, communication net
Frank Hohls, Armin C. Welker
The Josephson effect in superconductors links a quantized output voltage Vout = f ·(h/2e) to the natural constants of the electron's charge e, Planck'