Prediksi Nilai Kekasaran Permukaan Pembubutan Baja Aisi 304 dengan Pendekatan Pembelajaran Mesin menggunakan Algoritma Regresi Proses Gauss

Abstract

The point of this research to develop a surface roughness RA (Roughness Average) prediction model in the turning process of AISI 304 steel by applying the Minimum Quantity Lubrication (MQL) technique and using a machine learning approach with the Gauss process regression algorithm. Turning process is an important method in manufacturing industry to produce components with high tolerance and desired surface roughness. The MQL technique is a more environmentally friendly and efficient alternative compared to conventional lubrication methods that use a lot of lubricants. This research involves collecting experimental data on turning process parameters and surface roughness of AISI 304 steel using MQL cited in the research journal of Dubey, et al (2022). This research shows that the prediction results using the gauss process regression algorithm have good accuracy and show competitive results with the algorithm used in the research of Dubey, et al (2022)