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Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling
Pavel Kovac,Dragan Rodic,Vladimir Pucovsky,Borislav Savkovic,Marin Gostimirovic 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.10
This paper proposes a method for cutting parameters identification using the multi-inputs-multi-outputs fuzzy inference system(MIMO-FIS). The fuzzy inference system (FIS) was used to identify the initial values for cutting parameters (cutting speed, feed rate anddepth of cut) and flank wear using cutting temperature and tool life as outputs. The objective was to determine the influence of cuttingparameters on cutting temperature and tool life. The model for determining the cutting temperature and tool life of steel AISI 1060 wastrained (design rules) and tested by using the experimental data. The average deviation of the testing data for tool life was 11.6 %, whilethat of the cutting temperature was 3.28 %. The parameters used in these testing data were different from the data collected for the designrules. The test results showed that the proposed MIMO-FIS model can be used successfully for machinability data selection. The effect ofparameters and their interactions in machining is analyzed in detail and presented in this study.
Application of Fuzzy Logic in the Analysis of Surface Roughness of Thin-Walled Aluminum Parts
Jovan Vukman,Dejan Lukic,Stevo Borojevic,Dragan Rodic,Mijodrag Milosevic 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.1
This paper presents the development and application of fuzzy logic in the milling of thin-walled parts for the purpose of analyzing surface roughness. Surface roughness is an important performance indicator of finished components. Depending on conditions such as feed ratio and wall thickness, different machining strategies can be applied. The objective was to analyze and determine the influence of the machining conditions on surface roughness. The model for analyzing and determining surface roughness of the aluminum alloy AL 7075 was trained (design rules) and compared by using the experimental data. The average deviation of the compared data for surface roughness was 12.3%. The effect of the feed ratio, wall thickness and machining strategy as well as their interactions in machining are thoroughly analyzed and presented in this study.