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Machine Learning based Approach for the Prediction of Surface Integrity in Machining

Kryzhanivskyy, V. LU ; M'Saoubi, R. LU ; Bhallamudi, M. and Cekal, M. (2022) 6th CIRP Conference on Surface Integrity, CSI 2022 In Procedia CIRP 108. p.537-542
Abstract

This paper presents a two-stage procedure to create a surface integrity predictor. The first stage includes data clustering, which allows to evaluate the achievable surface quality. The second stage consists in training the model to predict which cluster the machined surface will belong to. To demonstrate the applicability, an experimental plan for machining of Inconel 718 in milling was developed. The validation through confusion matrix showed that the accuracy of prediction ranged from 64.7% to 84.9% for different test and train sets. Prospect of the research is to expand the set of monitored machining parameters and controlled surface integrity parameters.

Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Machine learning, Machining, Surface intergrity prediction
host publication
Procedia CIRP
series title
Procedia CIRP
volume
108
edition
C
pages
6 pages
conference name
6th CIRP Conference on Surface Integrity, CSI 2022
conference location
Lyon, France
conference dates
2022-06-08 - 2022-06-10
external identifiers
  • scopus:85134606731
ISSN
2212-8271
DOI
10.1016/j.procir.2022.03.084
language
English
LU publication?
yes
id
79ef24cd-4040-4455-9d01-8033151811a8
date added to LUP
2022-09-06 13:24:03
date last changed
2023-05-24 14:33:06
@inproceedings{79ef24cd-4040-4455-9d01-8033151811a8,
  abstract     = {{<p>This paper presents a two-stage procedure to create a surface integrity predictor. The first stage includes data clustering, which allows to evaluate the achievable surface quality. The second stage consists in training the model to predict which cluster the machined surface will belong to. To demonstrate the applicability, an experimental plan for machining of Inconel 718 in milling was developed. The validation through confusion matrix showed that the accuracy of prediction ranged from 64.7% to 84.9% for different test and train sets. Prospect of the research is to expand the set of monitored machining parameters and controlled surface integrity parameters.</p>}},
  author       = {{Kryzhanivskyy, V. and M'Saoubi, R. and Bhallamudi, M. and Cekal, M.}},
  booktitle    = {{Procedia CIRP}},
  issn         = {{2212-8271}},
  keywords     = {{Machine learning; Machining; Surface intergrity prediction}},
  language     = {{eng}},
  pages        = {{537--542}},
  series       = {{Procedia CIRP}},
  title        = {{Machine Learning based Approach for the Prediction of Surface Integrity in Machining}},
  url          = {{http://dx.doi.org/10.1016/j.procir.2022.03.084}},
  doi          = {{10.1016/j.procir.2022.03.084}},
  volume       = {{108}},
  year         = {{2022}},
}