Application of back propagation algorithms in neural network based identification responses of AISI 316 face milling cryogenic machining technique. Issue 3 (27th May 2022)

• Record Type: Journal Article
• Authors: M C, Karthik Rao; Malghan, Rashmi L; Shettigar, Arun Kumar; Rao, Shrikantha S; Herbert, Mervin A
• Is Part Of: Australian journal of mechanical engineering. Volume 20:Issue 3(2022)
• Page Start: 698
• Page End: 705
• Publication Date: 2022-05-27
• Subjects: Artificial neural network (ANN) -- back propagation algorithm (BPA) -- cryogenic (LN2) -- face milling; Mechanical engineering -- Periodicals; Mechanical engineering -- Australia -- Periodicals; Periodicals; 621.05
• DOI: 10.1080/14484846.2020.1740022
• Languages: English
• ISSNs: 1448-4846

Abstract

ABSTRACT: The paper explores the potential study of artificial neural network (ANN) for prediction of response surface roughness (Ra) in face milling operation with respect to cryogenic approach. The model of Ra was expressed as the main factor in face milling of spindle speed, feed rate, depth of cut and coolant type. The ANN is trained using four various back propagation algorithms (BPA). The emphasis of the paper is to investigate the performance and the accuracy of the attained results depicts the effectiveness of the trained ANN in identifying the predicted Ra. The incorporated various BPA in predicting the Ra. The performance comparative study is made among statistical (Response Surface Methodology (RSM)) and ANN (BPA – training algorithm) methods. The various incorporated BPA algorithms are Gradient Descent (GD), Scaled Conjugate Gradient Descent (SCGD), Levenberg Marquardt (LM) and Bayesian Neural Network (BNN). Afterwards the best suitable BPA is identified in predicting Ra for AISI 316 in face milling operation using liquid nitrogen (LN2 ) as cutting fluid. The outperformed BPA is identified based on the attained deviation percentage and time required for the training the network.