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Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling

This paper reports the results of measurements of cutting forces and delamination in drilling of Glass-Fiber-Reinforced Polymer (GFRP) composites. Four different types of GFRP composites were tested, made by a different manufacturing method and had a different fiber type, weight fraction (wf) ratio,...

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Autores principales: Biruk-Urban, Katarzyna, Bere, Paul, Józwik, Jerzy, Leleń, Michał
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738306/
https://www.ncbi.nlm.nih.gov/pubmed/36500093
http://dx.doi.org/10.3390/ma15238597
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author Biruk-Urban, Katarzyna
Bere, Paul
Józwik, Jerzy
Leleń, Michał
author_facet Biruk-Urban, Katarzyna
Bere, Paul
Józwik, Jerzy
Leleń, Michał
author_sort Biruk-Urban, Katarzyna
collection PubMed
description This paper reports the results of measurements of cutting forces and delamination in drilling of Glass-Fiber-Reinforced Polymer (GFRP) composites. Four different types of GFRP composites were tested, made by a different manufacturing method and had a different fiber type, weight fraction (wf) ratio, number of layers, but the same stacking sequence. GFRP samples were made using two technologies: a novel method based on the use of a specially designed pressing device and hand lay-up and vacuum bag technology process. The study was conducted with variable technological parameters: cutting speed v(c) and feed per tooth f(z). The two-edge carbide diamond-coated drill produced by Seco Company was used in the experiments. Cutting-force components and delamination factor were measured in the experiments, and photos of the holes were taken to determine the delamination. In addition, modeling of cause-and-effect relationships between the technological drilling parameters v(c) and f(z) was simulated with the use of artificial neural network modeling. For all tested GFRP materials, an increase in f(z) led to an increase in the amplitude of cutting-force component F(z). The lowest values of the amplitude of cutting-force component F(z) were obtained with the lowest tested feed per tooth value of 0.04 mm/tooth for all tested materials. It was observed that materials produced with the use of the specially designed pressing device were characterized by lower values of the cutting-force component F(z). It was also found that the delamination factor increased with an increase in f(z) for all tested GFRP materials. A comparison of the lowest and the highest values of f(z) revealed that the lowest delamination factor increase was archived by the B1 material and amounted to about 12.5%. The error margin of the obtained numerical modeling results does not exceed 15%, so it can be concluded that artificial neural networks are a suitable tool for modeling cutting force amplitudes as a function of v(c) and f(z). The study has shown that the use of the special pressing device during the manufacturing of composite materials has a positive effect on delamination.
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spelling pubmed-97383062022-12-11 Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling Biruk-Urban, Katarzyna Bere, Paul Józwik, Jerzy Leleń, Michał Materials (Basel) Article This paper reports the results of measurements of cutting forces and delamination in drilling of Glass-Fiber-Reinforced Polymer (GFRP) composites. Four different types of GFRP composites were tested, made by a different manufacturing method and had a different fiber type, weight fraction (wf) ratio, number of layers, but the same stacking sequence. GFRP samples were made using two technologies: a novel method based on the use of a specially designed pressing device and hand lay-up and vacuum bag technology process. The study was conducted with variable technological parameters: cutting speed v(c) and feed per tooth f(z). The two-edge carbide diamond-coated drill produced by Seco Company was used in the experiments. Cutting-force components and delamination factor were measured in the experiments, and photos of the holes were taken to determine the delamination. In addition, modeling of cause-and-effect relationships between the technological drilling parameters v(c) and f(z) was simulated with the use of artificial neural network modeling. For all tested GFRP materials, an increase in f(z) led to an increase in the amplitude of cutting-force component F(z). The lowest values of the amplitude of cutting-force component F(z) were obtained with the lowest tested feed per tooth value of 0.04 mm/tooth for all tested materials. It was observed that materials produced with the use of the specially designed pressing device were characterized by lower values of the cutting-force component F(z). It was also found that the delamination factor increased with an increase in f(z) for all tested GFRP materials. A comparison of the lowest and the highest values of f(z) revealed that the lowest delamination factor increase was archived by the B1 material and amounted to about 12.5%. The error margin of the obtained numerical modeling results does not exceed 15%, so it can be concluded that artificial neural networks are a suitable tool for modeling cutting force amplitudes as a function of v(c) and f(z). The study has shown that the use of the special pressing device during the manufacturing of composite materials has a positive effect on delamination. MDPI 2022-12-02 /pmc/articles/PMC9738306/ /pubmed/36500093 http://dx.doi.org/10.3390/ma15238597 Text en © 2022 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
Biruk-Urban, Katarzyna
Bere, Paul
Józwik, Jerzy
Leleń, Michał
Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling
title Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling
title_full Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling
title_fullStr Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling
title_full_unstemmed Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling
title_short Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling
title_sort experimental study and artificial neural network simulation of cutting forces and delamination analysis in gfrp drilling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738306/
https://www.ncbi.nlm.nih.gov/pubmed/36500093
http://dx.doi.org/10.3390/ma15238597
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