Investigation of Different Methods for Estimating Surface Roughness

Abstract

Machining is one of the most widely used manufacturing processes in the mold industry and which affects the manufacturing cost significantly. Particularly, the desired surface roughness/quality at a low cost at minimum machining time is the ultimate target. Surface quality depends on many parameters such as cutting speed, feed, depth of cut, vibration, coolant, insert properties/geometry used. In this study, surface roughnesses after turning of hot work tool steel at different parameters are investigated. At the same time, regression, artificial neural network, and fuzzy logic prediction models are developed from the experimental data. Therefore, surface roughness values at the different parameters are determined. The closest estimate with approximately 5% error is obtained by the Sugeno fuzzy logic model when it compared to experimental results.