Comparison C<sub>p</sub> for training and test sets across ML models.

Comparison C<sub>p</sub> for training and test sets across ML models.

<p>Comparison C<sub>p</sub> for training and test sets across ML models.</p>

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Bibliographic Details
Main Author:	R. S. Jayaram (22139155) (author)
Other Authors:	P. Saravanamuthukumar (22139158) (author), Ahmad Baharuddin Abdullah (22139161) (author), Ramalingam Krishnamoorthy (22139164) (author), Sandip Kunar (22139167) (author), Xu Yong (12675832) (author), S. Prabhakar (20051421) (author)
Published:	2025
Subjects:	Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified traditional taguchi method shap analysis indicated namely print speed improved mechanical performance brought significant changes 216 ° c lowest error metrics functionally graded multi div >< p 2 </ sup lower error low error study focuses produce intricate printing temperature polynomial regression performed best manufacturing sectors layered designs influential parameter greater ease fgms suitable fff process experimentally validated compressive strength 8 mpa 74 mpa 6 ). 44 %. 3d printing 36 mpa 19 mm
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Summary:	<p>Comparison C<sub>p</sub> for training and test sets across ML models.</p>

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