Prediction of surface roughness in turning of PEEK cf30 by using an artificial neural network

Autor: Francisco Mata Cabrera, Issam Hanafi, Abdellatif Khamlichi, Pedro J. Nuñez López
Rok vydání: 2012
Předmět:
Zdroj: Journal of the Chinese Institute of Industrial Engineers. 29:337-347
ISSN: 2151-7606
1017-0669
DOI: 10.1080/10170669.2012.702690
Popis: Surface roughness parameters Ra and Rt are mostly used as an index to determine the surface finish quality in the process of machining. Because of the strong nonlinear character of relationships between the process inputs and outputs, it is difficult to accurately estimate roughness characteristics by using traditional modeling techniques. In this work, accurate prediction of the Ra and Rt values during machining of reinforced poly ether ether ketone (PEEK) CF30 with TiN coated tools is achieved. The modeling is performed by using artificial neural network approach to represent the complex relationships between cutting conditions and surface roughness parameters. The input cutting parameters include cutting speed, depth of cut and feed rate. The network was trained with pairs of inputs and outputs datasets generated by machining experimental results that were obtained according to a full factorial design of experiment table. Predictions of the ANN based model were found to fit experimental data very well ...
Databáze: OpenAIRE
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