Title: Estimation of surface roughness in a turning operation using industrial big data

Authors: Kaustabh Chatterjee; Jian Zhang; Uday Shanker Dixit

Addresses: Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India ' Shantou Institute of Light Industrial Equipment Research, Shantou University, Shantou-515063, China ' Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India

Abstract: Surface roughness prediction in a turning process is of paramount importance. However, there is hardly any physics-based model that can predict it accurately. Recently, thanks to advancements in information technology, there are an ample amount of data in the industry. This article proposes a methodology to estimate surface roughness in turning based on industrial big data. An attempt has been made to extract and preserve the concise, useful information to reduce the burden on data storage. The proposed methodology predicts the lower, upper and most likely estimates of the surface roughness. A case study containing 35,000 datasets is simulated using a virtual lathe to demonstrate the efficacy of the methodology. The whole region of data is divided into 81 cells, and model fitting is carried out in each cell. The developed model based on industrial big data provides reasonable prediction of surface roughness.

Keywords: surface roughness; data analytics; multiple-linear regression; turning; outliers; machining; Cook's distance.

DOI: 10.1504/IJMMM.2021.115307

International Journal of Machining and Machinability of Materials, 2021 Vol.23 No.3, pp.209 - 240

Received: 09 Aug 2020
Accepted: 02 Oct 2020
Published online: 27 May 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article