Study on surface integrity in turning of titanium using cryogenically treated CBN inserts

• Published in: Advances in materials and processing technologies (Abingdon, England) Vol. 9; no. 3; pp. 908 - 930
• Main Authors: Chauhan, Shailendra, Pabla, B S, Singh, Ravi Pratap, Singh, Ramesh, Singh, Tarlochan
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.07.2023
• Subjects: ANN; CBN; cryogenic treatment; integrity; microstructure; surface roughness; titanium; turning
• ISSN: 2374-068X; 2374-0698
• DOI: 10.1080/2374068X.2022.2106665

In the engineering world, the machining of advanced materials like titanium alloys, which is having their superior applications in a variety of domains, that is, bio-implants, biomedical, aerospace, and so on, has been a challenge for many decades to the researchers. The optimised surface topography for such alloys has been revealed as a function of various input parameters. In this study, an attempt has been made by conducting the experimental investigation on the surface integrity and the quality machined work surface in turning of titanium alloy, that is, Ti6Al4V through employing cryogenically treated and non-cryogenically processed CBN inserts. The surface quality and the integrity of processed titanium alloy work-surface have been investigated under the influence of various considered input factors. The design of experimentation was constructed for planning the experiments. The analysis of variance test has been attempted for the studied response, that is, surface roughness and the mathematical models for the studied surface roughness (with both CBN inserts; cryogenically and non-cryogenically treated) have also been developed. The attained optimised parametric conditions for surface roughness are; feed rate - 0.067 mm/rev, cutting speed - 63.852 m/min, and depth of cut - 0.526 mm; and feed rate - 0.066 mm/rev, cutting speed - 59.450 m/min, and depth of cut - 0.309 mm, for non-cryogenically and cryogenically treated CBN inserts, respectively. Artificial neural network (ANN)-based modelling has also been attempted to predict and analyse the dataset of experiments. There is a variation of 1.43% between ANN predicted data of NC CBN and NC CBN experimental data. Further, there is an error of 0.2% between ANN predicted data of Cryo-CBN and Cryo-CBN experimental data. The microstructure analysis of the turned titanium surface reflected the presence of few tearing and groove lines with both the CBN inserts employed. The scratches and bamboo lines have been due to the built-up edges produced in the inserts.