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Title | Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks |
Authors |
Pavlenko, Ivan Volodymyrovych
Saga, M. Kuric, I. Kotliar, A. Basova, Y. Trojanowska, J. Ivanov, Vitalii Oleksandrovych |
ORCID |
http://orcid.org/0000-0002-6136-1040 http://orcid.org/0000-0003-0595-2660 |
Keywords |
technological process intensification grinding parameters ANN model regression approach |
Type | Article |
Date of Issue | 2020 |
URI | https://essuir.sumdu.edu.ua/handle/123456789/82856 |
Publisher | MDPI |
License | Creative Commons Attribution 4.0 International License |
Citation | Pavlenko I, Saga M, Kuric I, Kotliar A, Basova Y, Trojanowska J, Ivanov V. Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks. Materials. 2020; 13(23):5357. |
Abstract |
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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File | Size | Format | Downloads |
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Pavlenko_Parameter_Identification_of_Cutting_Forces_2020.pdf | 609 kB | Adobe PDF | 37350701 |
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