Analysis and optimization of surface roughness while machining SS304 using green lubricant

Neeraj Sharma, Kapil Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Stainless steels (SS) are used in a variety of precision, scientific, and industrial applications. All demand excellent surface properties for better functional performance of the products made by SS. Analysis, modeling, and optimization of machining of SS can result in process parameter combinations which facilitate to obtain better surface properties while machining SS using conventional processes. This paper reports the investigation on surface roughness (mean roughness depth) of SS304 machined by conventional turning using TiAlN/TiN coated tools under the influence of green lubricant. In this work total nine experiments with two replicates each have been conducted by varying three important machining parameters i.e. cutting speed, depth of cut, and feed rate at three levels each. A detailed effect of process parameters on mean roughness depth is discussed. Analysis of variance study is also reported. Process parameter optimization resulted in the optimum value 4.81 μm of mean roughness depth.

Original languageEnglish
Title of host publication4th North American IEOM Conference. IEOM 2019
PublisherIEOM Society
Pages897-903
Number of pages7
ISBN (Print)9781532359507
Publication statusPublished - 2019
Event4th North American IEOM Conference. IEOM 2019 - Toronto, Canada
Duration: 23 Oct 201925 Oct 2019

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
ISSN (Electronic)2169-8767

Conference

Conference4th North American IEOM Conference. IEOM 2019
Country/TerritoryCanada
CityToronto
Period23/10/1925/10/19

Keywords

  • Lubrication
  • Machinability
  • Machining
  • Roughness
  • Steel

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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