Predictive modeling of surface roughness in ultrasonic machining of cryogenic treated Ti-6Al-4V. Issue 8 (7th November 2016)

• Record Type: Journal Article
• Authors: Dhuria, Gaurav; Singh, Rupinder; Batish, Ajay
• Is Part Of: Engineering computations. Volume 33:Issue 8(2016)
• Page Start: 2377
• Page End: 2394
• Publication Date: 2016-11-07
• Subjects: Artificial neural network -- Surface roughness -- Ti-6Al-4V -- Cryogenic treatment -- Ultrasonic machining; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-11-2015-0360
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to study the effect of ultrasonic machining process parameters on surface quality while machining titanium alloy Ti-6Al-4V. Design/methodology/approach: Effect of cryogenic treatment (CT) of tool and work material was also explored in the study. Taguchi's L18 orthogonal array was chosen for design of experiments and average surface roughness was measured. Findings: Different modes of fracture were detected at work surface corresponding to varied input process parameters. Slurry grit size, power rating and tool material along with CT of work material were found to be the significant parameters affecting surface quality. Originality/value: The results obtained have been modelled using artificial neural network approach.