Comparison of two methods for pbkp_redicting surface roughness in turning stainless steel AISI 316L

Abstract:

The present study aimed to explore various models to pbkp_redict the surface roughness in dry turning of AISI 316L stainless steel. Multiple Regression Methods and Artificial Neural Networks Methods were implemented to study the effect of cutting speed, feed, and machining time. In order to increase the reliability and soundness of the registered surface roughness values, a complete Factorial Design was implemented. A statistical comparison of the resultant models was performed. The results produced by both methods show that the surface roughness can be pbkp_redicted. Results of the Artificial Neural Networks models show a better accuracy than those derived from the Multiple Regression models.

Año de publicación:

2018

Keywords:

• Dry turning
• Artificial Neural Network
• AISI 316L stainless steel
• Analysis of regression
• surface roughness

Fuente:

google

scopus