PREDIKSI KEKASARAN PERMUKAAN BAJA ST 40 BERBASIS MODEL ANALISIS REGRESI GANDA PADA PERMESINAN CNC FRAIS
The advancement in automation and accuracy of machine tool
made it possible to produce high quality industrial products. One of the
main perceptions of quality in mechanical products is its physical
appearance. One of the most important factors in physical appearance is
the surface roughness. Number of research publications addressed this
issue of surface roughness measurement and analyses. This research
focuses on study and analyses of surface quality improvement in milling
operation of low carbon steel (St 40). These metals are selected as they
are most widely used in education as well as in industry. This research
paper develops an empirical model for surface roughness (Ra) prediction
in milling using St 40. The impact of cutting speed, feed, depth of cut, and
dry cutting condition are studied on surface roughness.
The result produced using Regression Analyses (RA) give a good
prediction of surface roughness when compare with actual surface
roughness. The equation to prediction of surface roughness in dry
condition is Y = 3.0581 - 0.00007 n - 9.4333 Vf - 0.4956 a. By using
Multiple Regression Method equation, the average percentage deviation
of the testing set was 5.955% for training data set.
Keywords: surface roughness (SR), regression analyses (RA), speed, feed,
depth of cut, dry and material St 40.
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