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

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>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Fatma Alamri (20855088) (author)
مؤلفون آخرون: Imad Barsoum (17178454) (author), Shrinivas Bojanampati (20855091) (author), Maher Maalouf (6318215) (author)
منشور في: 2025
الموضوعات:
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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الوصف
الملخص:<p>Optimal tuning parameters and accuracy of the relative density, surface roughness and hardness models.</p>

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