Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.

<p>Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.</p>

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Bibliographic Details
Main Author:	Fatma Alamri (20855088) (author)
Other Authors:	Imad Barsoum (17178454) (author), Shrinivas Bojanampati (20855091) (author), Maher Maalouf (6318215) (author)
Published:	2025
Subjects:	Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified xlink "> despite mean squared error lt ;&# 8201 gt ;&# 8201 feature importance analysis alsi10mg samples produced 10 &# 181 support vector regression kernel ridge regression additive manufactured samples g ., porosity data including porosity open additive manufacturing scan speed region predicting relative density optimal laser power predict part quality additive manufacturing scan speed relative density laser power lasso regression accurately predict widespread adoption various industries study identified study aims still hindered results presented random forest process parameters poor quality layer thickness improve repeatability hatch distance evaluated based current work computational results additional measurements 120 hv
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