Machine Learning based Approach for the Prediction of Surface Integrity in Machining
(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:
https://lup.lub.lu.se/record/79ef24cd-4040-4455-9d01-8033151811a8
- author
- Kryzhanivskyy, V. LU ; M'Saoubi, R. LU ; Bhallamudi, M. and Cekal, M.
- organization
- publishing date
- 2022
- 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}}, }