Cargando…

The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process

Wood-based composites are increasingly used in the industry not only because of the shortage of solid wood, but above all because of the better properties, such as high strength and aesthetic appearance compared to wood. Medium-density fiberboard (MDF) is a wood-based composite that is widely used i...

Descripción completa

Detalles Bibliográficos
Autores principales: Szwajka, Krzysztof, Zielińska-Szwajka, Joanna, Trzepieciński, Tomasz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420204/
https://www.ncbi.nlm.nih.gov/pubmed/37569999
http://dx.doi.org/10.3390/ma16155292
_version_ 1785088720069722112
author Szwajka, Krzysztof
Zielińska-Szwajka, Joanna
Trzepieciński, Tomasz
author_facet Szwajka, Krzysztof
Zielińska-Szwajka, Joanna
Trzepieciński, Tomasz
author_sort Szwajka, Krzysztof
collection PubMed
description Wood-based composites are increasingly used in the industry not only because of the shortage of solid wood, but above all because of the better properties, such as high strength and aesthetic appearance compared to wood. Medium-density fiberboard (MDF) is a wood-based composite that is widely used in the furniture industry. In this work, an attempt was made to predict the surface roughness of the machined MDF in the milling process based on acceleration signals from an industrial piezoelectric sensor installed in the cutting zone. The surface roughness parameter Sq was adopted for the evaluation and measurement of surface roughness. The surface roughness prediction was performed using a radial basis function (RBF) artificial neural network (ANN) and a Takagi–Sugeno––Kang (TSK) fuzzy model with subtractive clustering. In the research, as inputs to the ANNs and fuzzy model, the kinematic parameters of the cutting process and selected measures of the acceleration signal were adopted. At the output, the values of the surface roughness parameter Sq were obtained. The results of the experiments show that the surface roughness is influenced not only by the kinematic parameters of the cutting, but also by the vibrations generated during the milling process. Therefore, by combining information on the cutting kinematics parameters and vibration, the accuracy of the surface roughness prediction in the milling process of MDF can be improved. The use of TSK fuzzy modelling based on the subtractive clustering method for integrating the information from many acceleration signal measurements in the examined range of cutting conditions meant the surface roughness was predicted with high accuracy and high reliability. With the help of two tested artificial intelligence tools, it is possible to estimate the surface roughness of the workpiece with only a small error. When using a radial neural network, the root mean square error for estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm. The surface of the sample made with the cutting parameters v(c) = 76 m/min and v(f) = 1200 mm/min is characterized by a less concentrated distribution of ordinate densities, compared to the surface of the sample cut with lower feed rates but at the same cutting speed. The most concentrated distribution of ordinate density (for the cutting speed v(c) = 76 m/min) is characterized by the surface, where the feed rate value was v(f) = 200 mm/min, with 90% of the material concentrated in the profile height of 28.2 μm. When using an RBF neural network, the RMSE of estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm.
format Online
Article
Text
id pubmed-10420204
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104202042023-08-12 The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process Szwajka, Krzysztof Zielińska-Szwajka, Joanna Trzepieciński, Tomasz Materials (Basel) Article Wood-based composites are increasingly used in the industry not only because of the shortage of solid wood, but above all because of the better properties, such as high strength and aesthetic appearance compared to wood. Medium-density fiberboard (MDF) is a wood-based composite that is widely used in the furniture industry. In this work, an attempt was made to predict the surface roughness of the machined MDF in the milling process based on acceleration signals from an industrial piezoelectric sensor installed in the cutting zone. The surface roughness parameter Sq was adopted for the evaluation and measurement of surface roughness. The surface roughness prediction was performed using a radial basis function (RBF) artificial neural network (ANN) and a Takagi–Sugeno––Kang (TSK) fuzzy model with subtractive clustering. In the research, as inputs to the ANNs and fuzzy model, the kinematic parameters of the cutting process and selected measures of the acceleration signal were adopted. At the output, the values of the surface roughness parameter Sq were obtained. The results of the experiments show that the surface roughness is influenced not only by the kinematic parameters of the cutting, but also by the vibrations generated during the milling process. Therefore, by combining information on the cutting kinematics parameters and vibration, the accuracy of the surface roughness prediction in the milling process of MDF can be improved. The use of TSK fuzzy modelling based on the subtractive clustering method for integrating the information from many acceleration signal measurements in the examined range of cutting conditions meant the surface roughness was predicted with high accuracy and high reliability. With the help of two tested artificial intelligence tools, it is possible to estimate the surface roughness of the workpiece with only a small error. When using a radial neural network, the root mean square error for estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm. The surface of the sample made with the cutting parameters v(c) = 76 m/min and v(f) = 1200 mm/min is characterized by a less concentrated distribution of ordinate densities, compared to the surface of the sample cut with lower feed rates but at the same cutting speed. The most concentrated distribution of ordinate density (for the cutting speed v(c) = 76 m/min) is characterized by the surface, where the feed rate value was v(f) = 200 mm/min, with 90% of the material concentrated in the profile height of 28.2 μm. When using an RBF neural network, the RMSE of estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm. MDPI 2023-07-27 /pmc/articles/PMC10420204/ /pubmed/37569999 http://dx.doi.org/10.3390/ma16155292 Text en © 2023 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
Szwajka, Krzysztof
Zielińska-Szwajka, Joanna
Trzepieciński, Tomasz
The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
title The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
title_full The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
title_fullStr The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
title_full_unstemmed The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
title_short The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
title_sort use of a radial basis function neural network and fuzzy modelling in the assessment of surface roughness in the mdf milling process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420204/
https://www.ncbi.nlm.nih.gov/pubmed/37569999
http://dx.doi.org/10.3390/ma16155292
work_keys_str_mv AT szwajkakrzysztof theuseofaradialbasisfunctionneuralnetworkandfuzzymodellingintheassessmentofsurfaceroughnessinthemdfmillingprocess
AT zielinskaszwajkajoanna theuseofaradialbasisfunctionneuralnetworkandfuzzymodellingintheassessmentofsurfaceroughnessinthemdfmillingprocess
AT trzepiecinskitomasz theuseofaradialbasisfunctionneuralnetworkandfuzzymodellingintheassessmentofsurfaceroughnessinthemdfmillingprocess
AT szwajkakrzysztof useofaradialbasisfunctionneuralnetworkandfuzzymodellingintheassessmentofsurfaceroughnessinthemdfmillingprocess
AT zielinskaszwajkajoanna useofaradialbasisfunctionneuralnetworkandfuzzymodellingintheassessmentofsurfaceroughnessinthemdfmillingprocess
AT trzepiecinskitomasz useofaradialbasisfunctionneuralnetworkandfuzzymodellingintheassessmentofsurfaceroughnessinthemdfmillingprocess