Clustering results.

<div><p>In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk remains underexplored. The curr...

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Bibliographic Details
Main Author:	Hyeonbin Ji (20485140) (author)
Other Authors:	Ingeun Hwang (20485143) (author), Junghwon Kim (20485146) (author), Suan Lee (20485149) (author), Wookey Lee (20485152) (author)
Published:	2024
Subjects:	Medicine Cell Biology Neuroscience Biotechnology Cancer Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified way anova confirmed quality assurance systems minimizing subjective judgment feature extraction methods extract principal topics enhancing product quality diagnosing failure probability contractive auto encoder advanced fault diagnosis study utilized bertopic risk remains underexplored risk level classification failure risk diagnosis big data analytics div >< p optimal cluster count leveraging feature extraction highest silhouette score employed text embedding contemporary manufacturing landscape equipment </ p silhouette score study aims maintenance data e </ varying evaluations topic modeling similar facilities significantly enhance robust capabilities research offers performance results novel methodology means algorithm manual intervention identical products extruder ’ dimensionality reduction depth analysis current practice based clustering artificial intelligence aluminum extruder 3213 ×
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Clustering results.

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