PREDIKSI KEKASARAN PERMUKAAN MATERIAL BAJA S45C PADA PROSES CNC MILLING MENGGUNAKAN METODE DECISION TREE REGRESSOR

PRATAMA, ADITYA MULYA and Yani, Irsyadi (2024) PREDIKSI KEKASARAN PERMUKAAN MATERIAL BAJA S45C PADA PROSES CNC MILLING MENGGUNAKAN METODE DECISION TREE REGRESSOR. Undergraduate thesis, Sriwijaya University.

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Abstract

In Manufaturing Industry, machining process is one of the important field of study because product with high quality and high preecision is in demand. One of the factor that influence the quality of a product is surface roughness. Surface Roughness alone got influenced by a lot of factor such as condition of the cutter, cutting parameter , feed rate, depth of cut and the material itself. Decision Tree Regressor is a supervised learning that known for it accurate intepretation ability. In this experiment, the author conducted an experiment by doing a prediction with decision tree regressor from the results of Milling processes on CNC machines. In this experiment, author made a Decision tree regression prediction on surface roughness average for S45C steel. This experiment was done on Vocational School 2 Palembang with 10 diameter mm, 4 flute cutter and the milling machine type is CNC Milling OPTImill F 105 CNC. After data collection, analysis were carried out using the Decision Tree Regressor. The database of this experiment is 119 data large with 5 variations of Vc, 5 variations of fz and 4 variations of ax. In the prediction model, the data was divided 95 training data and 24 testing data. After programaming process in Google Colab environtment with Python choosen as programming language, the mean absolute error result obtained was 43,4%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	milling, kekasaran permukaan, decision tree regression, pembelajaran mesin
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:	Aditya Mulya Pratama
Date Deposited:	28 Jun 2024 03:01
Last Modified:	28 Jun 2024 03:01
URI:	http://repository.unsri.ac.id/id/eprint/148104

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