Vision-based surface roughness accuracy prediction in the CNC milling process (Al6061) using ANN. (2021)

• Record Type: Journal Article
• Authors: Sanjeevi, R.; Nagaraja, R.; Radha Krishnan, B.
• Is Part Of: Materials today. Volume 37(2021)Supplement Part 2
• Page Start: 245
• Page End: 247
• Publication Date: 2021
• Subjects: Al6061 -- CNC milling -- Artificial Neural Network -- Surface Roughness -- Accuracy; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.05.122
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This paper proposed the methodology of identified the surface roughness accuracy rate in the CNC milling process by Artificial Neural Network. Al6061 preferred as job material for milling processes carried out in CNC milling machines. The various input parameters like speed, federate, and depth of cut used for the CNC milling process. The Artificial Neural Network modeling has consisted of different input parameters and a single output parameter. The surface roughness is fixed as an output parameter in an Artificial Neural Network. Based on the input parameter and the number of neurons, the surface roughness value was derived. The final accuracy rate of surface roughness was calculated by vision measurement value compared with the conventional stylus probe.