Parametric optimization and process capability analysis for machining of nickel-based superalloy
File(s)Gupta2019_Article_ParametricOptimizationAndProce.pdf (3.34 MB)
Published version
Author(s)
Type
Journal Article
Abstract
The manufacturing of parts from nickel-based superalloy, such as Inconel-800 alloy, represents a challenging task for industrial sites. Their performances can be enhanced by using a smart cutting fluid approach considered a sustainable alternative. Further, to innovate the cooling strategy, the researchers proposed an improved strategy based on the minimum quantity lubrication (MQL). It has an advantage over flood cooling because it allows better control of its parameters (i.e., compressed air, cutting fluid). In this study, the machinability of superalloy Inconel-800 has been investigated by performing different turning tests under MQL conditions, where no previous data are available. To reduce the numerous numbers of tests, a target objective was applied. This was used in combination with the response surface methodology (RSM) while assuming the cutting force input (Fc), potential of tool wear (VBmax), surface roughness (Ra), and the length of tool–chip contact (L) as responses. Thereafter, the analysis of variance (ANOVA) strategy was embedded to detect the significance of the proposed model and to understand the influence of each process parameter. To optimize other input parameters (i.e., cutting speed of machining, feed rate, and the side cutting edge angle (cutting tool angle)), two advanced optimization algorithms were introduced (i.e., particle swarm optimization (PSO) along with the teaching learning-based optimization (TLBO) approach). Both algorithms proved to be highly effective for predicting the machining responses, with the PSO being concluded as the best amongst the two. Also, a comparison amongst the cooling methods was made, and MQL was found to be a better cooling technique when compared to the dry and the flood cooling.
Date Issued
2019-06
Date Acceptance
2019-02-06
ISSN
0268-3768
Publisher
Springer Verlag
Start Page
3995
End Page
4009
Journal / Book Title
The International Journal of Advanced Manufacturing Technology
Volume
102
Issue
9-12
Copyright Statement
© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Source Database
manual-entry
Identifier
https://link.springer.com/article/10.1007%2Fs00170-019-03453-3
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Manufacturing
Engineering
Inconel-800
MQL
Optimization
Sustainable machining
PSO
TLBO
LEARNING-BASED OPTIMIZATION
CUTTING FLUID
TOOL WEAR
TEMPERATURE
ALLOY
Industrial Engineering & Automation
09 Engineering
08 Information and Computing Sciences
01 Mathematical Sciences
Publication Status
Published
Date Publish Online
2019-03-04