Prediksi Nilai Aus Tepi dan Kekasaran Permukaan menggunakan Metode Pembelajaran Mesin pada Operasi Pembubutan Keras Baja Aisi 4140 menggunakan Pahat Keramik Berlapis

Abstract

Flank wear and surface roughness are considered important measurement parameters in turning operations. Unwanted issues like these can impact the quality and performance of the produced engine components. Machine learning algorithms are used to determine the extent of flank wear and surface roughness to address this. The Multiple Linear Regression methodology is used in this work to investigate and estimate the extent of flank wear and surface roughness in the hard turning operation of AISI 4140 steel utilizing coated ceramic inserts. This method entails gathering experimental data from prior studies (Das Ranjan et al. 2015), such as cutting speed, feed rate, and depth of cut. This data is used to train and evaluate the machine learning model in order to comprehend the complicated relationship between input and output variables. The results of this study show that the Multiple Linear Regression algorithm model can offer reasonably accurate surface roughness predictions but not flank wear predictions