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Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals

In metal-cutting processes, the interaction between the tool and workpiece is highly nonlinear and is very sensitive to small variations in the process parameters. This causes difficulties in controlling and predicting the resulting surface finish quality of the machined surface. In this work, vibra...

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Autores principales: Abu-Mahfouz, Issam, Banerjee, Amit, Rahman, Esfakur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434284/
https://www.ncbi.nlm.nih.gov/pubmed/34501138
http://dx.doi.org/10.3390/ma14175050
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author Abu-Mahfouz, Issam
Banerjee, Amit
Rahman, Esfakur
author_facet Abu-Mahfouz, Issam
Banerjee, Amit
Rahman, Esfakur
author_sort Abu-Mahfouz, Issam
collection PubMed
description In metal-cutting processes, the interaction between the tool and workpiece is highly nonlinear and is very sensitive to small variations in the process parameters. This causes difficulties in controlling and predicting the resulting surface finish quality of the machined surface. In this work, vibration signals along the major cutting force direction in the turning process are measured at different combinations of cutting speeds, feeds, and depths of cut using a piezoelectric accelerometer. The signals are processed to extract features in the time and frequency domains. These include statistical quantities, Fast Fourier spectral signatures, and various wavelet analysis extracts. Various feature selection methods are applied to the extracted features for dimensionality reduction, followed by applying several outlier-resistant unsupervised clustering algorithms on the reduced feature set. The objective is to ascertain if partitions created by the clustering algorithms correspond to experimentally obtained surface roughness data for specific combinations of cutting conditions. We find 75% accuracy in predicting surface finish from the Noise Clustering Fuzzy C-Means (NC-FCM) and the Density-Based Spatial Clustering Applications with Noise (DBSCAN) algorithms, and upwards of 80% accuracy in identifying outliers. In general, wrapper methods used for feature selection had better partitioning efficacy than filter methods for feature selection. These results are useful when considering real-time steel turning process monitoring systems.
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spelling pubmed-84342842021-09-12 Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals Abu-Mahfouz, Issam Banerjee, Amit Rahman, Esfakur Materials (Basel) Article In metal-cutting processes, the interaction between the tool and workpiece is highly nonlinear and is very sensitive to small variations in the process parameters. This causes difficulties in controlling and predicting the resulting surface finish quality of the machined surface. In this work, vibration signals along the major cutting force direction in the turning process are measured at different combinations of cutting speeds, feeds, and depths of cut using a piezoelectric accelerometer. The signals are processed to extract features in the time and frequency domains. These include statistical quantities, Fast Fourier spectral signatures, and various wavelet analysis extracts. Various feature selection methods are applied to the extracted features for dimensionality reduction, followed by applying several outlier-resistant unsupervised clustering algorithms on the reduced feature set. The objective is to ascertain if partitions created by the clustering algorithms correspond to experimentally obtained surface roughness data for specific combinations of cutting conditions. We find 75% accuracy in predicting surface finish from the Noise Clustering Fuzzy C-Means (NC-FCM) and the Density-Based Spatial Clustering Applications with Noise (DBSCAN) algorithms, and upwards of 80% accuracy in identifying outliers. In general, wrapper methods used for feature selection had better partitioning efficacy than filter methods for feature selection. These results are useful when considering real-time steel turning process monitoring systems. MDPI 2021-09-03 /pmc/articles/PMC8434284/ /pubmed/34501138 http://dx.doi.org/10.3390/ma14175050 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abu-Mahfouz, Issam
Banerjee, Amit
Rahman, Esfakur
Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals
title Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals
title_full Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals
title_fullStr Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals
title_full_unstemmed Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals
title_short Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals
title_sort evaluation of clustering techniques to predict surface roughness during turning of stainless-steel using vibration signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434284/
https://www.ncbi.nlm.nih.gov/pubmed/34501138
http://dx.doi.org/10.3390/ma14175050
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