PENERAPAN NAÏVE BAYES UNTUK KLASIFIKASI KEKASARAN PERMUKAAN BAJA S45C PADA PROSES CNC MILLING

STEVEN, NICO and Yani, Irsyadi (2023) PENERAPAN NAÏVE BAYES UNTUK KLASIFIKASI KEKASARAN PERMUKAAN BAJA S45C PADA PROSES CNC MILLING. Undergraduate thesis, Sriwijaya University.

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Abstract

Surface roughness is one of the key quality parameters in machining that affects the performance and longevity of the produced components. This study examines the application of the Naïve Bayes method to classify or group the surface roughness of S45C steel in the CNC milling process. The Naïve Bayes method, known for its simplicity and effectiveness in solving classification problems, is used to predict surface roughness levels based on various machining parameters such as cutting speed, feed rate, and depth of cut. The Naïve Bayes model is trained using training data and evaluated with test data to measure the accuracy and consistency of the classification or grouping. The results indicate that the Naïve Bayes method can produce an accurate and reliable classification model for predicting the surface roughness of workpieces, thereby helping to improve quality control and process efficiency in CNC milling operations. Keywords: surface roughness, naïve bayes, face milling CNC

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	naïve bayes, machine learning, surface roughness level
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery
Divisions:	03-Faculty of Engineering > 21201-Mechanical Engineering (S1)
Depositing User:	Nico Steven
Date Deposited:	19 Jun 2024 04:47
Last Modified:	19 Jun 2024 04:47
URI:	http://repository.unsri.ac.id/id/eprint/147026

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