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Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks

The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding a...

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Autores principales: Pavlenko, Ivan, Saga, Milan, Kuric, Ivan, Kotliar, Alexey, Basova, Yevheniia, Trojanowska, Justyna, Ivanov, Vitalii
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728348/
https://www.ncbi.nlm.nih.gov/pubmed/33255880
http://dx.doi.org/10.3390/ma13235357
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author Pavlenko, Ivan
Saga, Milan
Kuric, Ivan
Kotliar, Alexey
Basova, Yevheniia
Trojanowska, Justyna
Ivanov, Vitalii
author_facet Pavlenko, Ivan
Saga, Milan
Kuric, Ivan
Kotliar, Alexey
Basova, Yevheniia
Trojanowska, Justyna
Ivanov, Vitalii
author_sort Pavlenko, Ivan
collection PubMed
description The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles.
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spelling pubmed-77283482020-12-11 Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks Pavlenko, Ivan Saga, Milan Kuric, Ivan Kotliar, Alexey Basova, Yevheniia Trojanowska, Justyna Ivanov, Vitalii Materials (Basel) Article The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles. MDPI 2020-11-26 /pmc/articles/PMC7728348/ /pubmed/33255880 http://dx.doi.org/10.3390/ma13235357 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pavlenko, Ivan
Saga, Milan
Kuric, Ivan
Kotliar, Alexey
Basova, Yevheniia
Trojanowska, Justyna
Ivanov, Vitalii
Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks
title Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks
title_full Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks
title_fullStr Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks
title_full_unstemmed Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks
title_short Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks
title_sort parameter identification of cutting forces in crankshaft grinding using artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728348/
https://www.ncbi.nlm.nih.gov/pubmed/33255880
http://dx.doi.org/10.3390/ma13235357
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