Please use this identifier to cite or link to this item: https://essuir.sumdu.edu.ua/handle/123456789/82856
Or use following links to share this resource in social networks: Recommend this item
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  
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.
Appears in Collections: Наукові видання (ТеСЕТ)

Views

Ukraine Ukraine
13

Downloads

Ukraine Ukraine
14

Files

File Size Format Downloads
Pavlenko_Parameter_Identification_of_Cutting_Forces_2020.pdf 609 kB Adobe PDF 14

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.