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Transactions: WSEAS TRANSACTIONS ON APPLIED AND THEORETICAL MECHANICS
Transactions ID Number: 42-530
Full Name: Vedat Topuz
Position: Associate Professor
Age: ON
Sex: Male
Address: Marmara University, Vocational School of Technical Sciences, Goztepe Campus, 347222, Kadýköy, Istanbul,
Country: TURKEY
Tel: +90 216 4182504
Tel prefix:
Fax: +90 216 4182505
E-mail address: vtopuz@marmara.edu.tr
Other E-mails: gbasmaci@marmara.edu.tr
Title of the Paper: Prediction of Nickel-Based Super Alloy Surface Roughness CNC End Milling Operation Using Connectionist Models
Authors as they appear in the Paper: Vedat Topuz, Mustafa Ay, Gültekin Basmacý
Email addresses of all the authors: vtopuz@marmara.edu.tr,muay@marmara.edu.tr,gbasmaci@marmara.edu.tr
Number of paper pages: 10
Abstract: This paper outlines a comparative study of different Artificial Neural Network (ANN) models and Adaptive-Networks-based Fuzzy Inference Systems (ANFIS) approach for predicting the surface roughness (Ra) using cutting speed (v), feed rate (fr ) and cutting depth (d) when machining Nickel-based super alloys with uncoated carbide tool under milling conditions. Nickel-based supper alloys are generally known to be one of the most difficult materials to machine because of hardness, high strength at high temperature, affinity to react with the tool materials, and low thermal diffusivity. In this work, we develop an accurate and reliable model for prediction of surface roughness for Nickel-based super alloys in end milling operations. In this study, to ensure the effectiveness of connectionist techniques, different models such as multi-layered perceptron (MLP), Elman recurrent neural network (ERNN), radial basis function network (RBFN) and ANFIS networks were designed and !
optimal networks parameters were found. All the designed networks modeling and prediction ability were found using root mean square error (RMSE) and regression analysis. We found that RBFN has perfect modeling capability but very poor prediction ability. On the other hand, ERNN and ANFIS have very good modeling and prediction ability. Finally, we concluded that ERNN and ANFIS models could be used to predict the surface roughness (Ra) for nickel-based super alloys in end milling process compared to the other connectionist models
Keywords: Surface roughness, Nickel–based super alloy, connectionist models, ANFIS.
EXTENSION of the file: .doc
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