Optimizing Milling Parameters in Face Milling Operation

Authors

  • Vipul Verma
  • Peeyush Kr. Gupta
  • Ayush Goel
  • Saurabh Awasthi

DOI:

https://doi.org/10.37591/joeam.v9i3.1629

Keywords:

ANOVA, MRR, multiple regression method, RSM

Abstract

In statistics, response surface methodology (RSM) establish a relationship between several self or system explanatory variables and one or more response variables. Since many variables are involved to control the process, the requirement of some mathematical models to represent the process is found to be necessary. In order to extract the result by this statistical analysis, the experimental results will have to be processed using the analysis of variance (ANOVA). ANOVA is a statistical computational tool that enables the estimation of the sometimes absolute and mainly relative contributions of each control factors to the overall measured response. In this current work, only the selectively signify parameters are used to develop mathematical models while applying response surface methodology (RSM). Multiple regression method is used in experimental results to build first-order and second-order models. The experimental investigation established that the MRR can be optimized by the proper settings of machining parameters. High MRR can also result in better machinability and production rate.

References

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Published

2019-02-19

Issue

Vol. 9 No. 3 (2018)

Section

Research Article

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