Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625. (5th October 2022)

• Record Type: Journal Article
• Authors: A. S., Rajesh; Prabhuswamy, M. S.; Komalnu Raghavan, Ishwarya
• Other Names: Raja K. Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-10-05
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/7520962
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : In this paper, the influence of process components on surface roughness in turning of Inconel 625 using cubic boron nitride (CBN) is studied. A predictive model is developed to forecast the surface roughness using the cascade forward neural network (CFNN). The experiments are designed based on Taguchi. L 27 orthogonal array (OA) is used to perform the experimental trails by considering speed, feed, and depth of cut as input factors. Out of 27 experimental trails, 18 experiments are used for training and 9 experimental trails are used for testing. The developed predictive model by the CFNN is compared with regression model values. The average prediction error for surface roughness is 2.94% with R 2 = 99.99% by the CFNN. The CFNN is known to be superior to predict the response with minimum of percentage error. The minimum and maximum roughness observed at trail 8 and trail 20 is noted, respectively, and the increases in roughness at experimental trail 8 is equal to 3.384 times higher than the roughness observed at experimental trail.20. The feed rate dominates effectively on the roughness rather than other factors. The consequences of process factors on surface roughness are studied with the help of ANOVA. This experimental study and developed model would be used for aero parts manufacturing to forecast the roughness accurately before to the actual experiment so that actual machining and material cost could be avoided.