Machinability model and multi-response optimisation of process parameters through regression and utility concept
Ashok Kumar Sahoo,
Amlana Panda,
Bijaya Bijeta Nayak,
Ramanuj Kumar,
Rabin Kumar Das and
Ramesh Kumar Nayak
International Journal of Process Management and Benchmarking, 2021, vol. 11, issue 3, 390-414
Abstract:
Over the years, it is essential to produce appropriate dimensions with quality parts for the use in various automotive components. This paper presents modelling and multi-optimisation exploration on the hard part turning of EN 24 grade steel at 48 HRC with coated carbide multilayer inserts for three roughness factors (Ra, Rz, and Rt). Taguchi L16 orthogonal design with three input parameters and three quality characteristics output was applied to suitably model the process requirements. The second model provides a high coefficient of determination (R2 = 0.98 for Ra, 0.97 for Rz and 0.95 for Rt respectively) compared with the linear model that demonstrates high significance. The ideal parametric setting for different multiple quality features was found to be the depth of cut (0.4 mm), feed rate (0.04 mm/rev) and cutting speed (200 m/min) respectively. The multi-responses optimisation has been simultaneously performed using Taguchi method and the utility concept.
Keywords: hard turning; multilayer coated carbide; surface roughness; regression; utility concept. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=115009 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpmbe:v:11:y:2021:i:3:p:390-414
Access Statistics for this article
More articles in International Journal of Process Management and Benchmarking from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().