Optimization of surface roughness parameters in dry turning

• Autorzy: Mahdavinejad, R. A. , Sharifi Bidgoli, H.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The precision of machine tools on one hand and the input setup parameters on the other hand, are strongly influenced in main output machining parameters such as stock removal, toll wear ratio and surface roughnes. Design/methodology/approach: There are a lot of input parameters which are effective in the variations of these output parameters. In CNC machines, the optimization of machining process in order to predict surface roughness is very important. Findings: From this point of view, the combination of adaptive neural fuzzy intelligent system is used to predict the roughness of dried surface machined in turning process. Research limitations/implications: There are some limitations in the properties of various kinds of lubricants. The influence of some undesirable factors in experiments is Another limitation in this research. Practical implications: From this point of view, some samples are machined with various input parameters and then the experimental data is used to create fuzzy rules and their processing via neural networks. So that, the prediction model is created with some experimental data first. Then the results of this model are compared with the real surface roughness. Originality/value: When the cutting speed is increased the machined surface quality is improved.The quality of machined surface is decreased with the feeding rates and the depth of cut.The error of the model is more less than the error of using ordinary equations. The comparison results show that this model is more effective than theoretical calculation methods.

Słowa kluczowe

PL

adaptacyjny system neuronowej inteligencji rozmytej; chropowatość powierzchni; logika rozmyta

EN

adaptive neural fuzzy intelligent system; roughness; fuzzy logic

• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2009
• Tom: Vol. 37, nr 2
• Strony: 571-577
• Opis fizyczny: Bibliogr. 22 poz., rys., tabl.

Twórcy

autor

Mahdavinejad, R. A.

autor

Sharifi Bidgoli, H.

• School of Mechanical Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran,

Bibliografia

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