Analytical Modelling of Surface Roughness during Compacted Graphite Iron Milling Using Ceramic Inserts

This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Authors:

• S. Karabulut
• A. Güllü
• A. Güldas
• R. Gürbüz

Keywords:

• CGI
• milling
• surface roughness
• ANN
• regression
• modeling
• analysis.

References:

[1] Nayyar, V.; Zubayer, A.; Jacek, K.; Anders, K.; Lars, N. An
Experimental Investigation of the influence of cutting-edge geometry on
the machinability of compacted graphite iron. IJMMME 2013, 3.1, 1–
25.
[2] Hick, H.; Langmayr, F. All-star cast. Engine Technology International
Conference (January) 2000, 40–42.
[3] Marquarad, R.; Helfried, S.; McDonald, M.C. New materials create new
possibilities. Engine Technology International 1998, 2, 58–60.
[4] Guesser, W.; Schroeder, T. Dawson, S. Production experience with
compacted graphite iron automotive components. AFS Transactions,
DesPlaines 2001.
[5] Abele, E.; Schramm, B. Wear behaviour of PCD in machining.
Proceedings of the 2nd Industrial Diamond Conference, Rome 2007.
[6] Tsai, Y.H.; Chen, J.C.; Luu, S.J. An in-process surface recognition
system based on neural networks in end milling cutting operations.
International Journal of Machine Tools and Manufacture 1999, 39, 583–
605.
[7] Zain, A.M.; Haron, H.; Sharif, S. Application of GA to optimize cutting
conditions for minimizing surface roughness in end milling machining
process. Expert Systems with Applications 2010, 37, 4650–4659.
[8] Alauddin, M.; El Baradie, M.A.; Hashmi, M.S.J. Optimization of surface
finish in end milling inconel 718. Journal of Materials Processing
Technology 1996, 56, 54–65.
[9] Benardos, P.G.; Vosniakos, G.C. Prediction of surface roughness in
CNC face milling using neural networks and Taguchi’s design of
experiments. Robotics and Computer-Integrated Manufacturing 2002,
18, 343–354.
[10] Bajic, D.; Lela, B.; Zivkovic, D. Modeling of machined surface
roughness and optimization of cutting parameters in face milling.
Metalurgija 2008, 47, 331–334.
[11] Munoz-Escalona, P.; Maropoulos, P.G. Artificial neural networks for
surface roughness prediction when face milling Al 7075–T7351. Journal
of Materials Engineering and Performance 2010, 19, 185–193.
[12] Chien, W.T.; Chou, C.Y. The predictive model for machinability of 304
stainless steel. Journal of Materials Processing Technology 2001, 118,
442–447.
[13] Sağlam, H. Frezelemede yapay sinir ağları kullanarak, çok-elemanlı
kuvvet ölçümlerine dayalı takım durumu izleme, Doktora Tezi, Selçuk
Üniversitesi Mühendislik Fakültesi, Konya, 2000.
[14] Lo, S. An adaptive-network based fuzzy inference system for prediction
of workpiece surface roughness in end milling. Journal of Materials
Processing Technology 2003, 142, 665–675.
[15] Öktem, H.; Erzincanlı, F. AISI 1040 çelik malzemenin CNC frezeleme
ile islenmesi sırasında olusan yüzey pürüzlülüğünün yapay sinir ağıyla
modellenmesi. 2. Ulusal Tasarım Đmalat ve Analiz Kongresi, 2010, 221–
229.
[16] Asiltürk, I.; Demirci, T.M. Karbür kesici kullanarak sertlestirilmis AISI
1040 çeliklerin frezelenmesindeki yüzey pürüzlülüğünün regresyonla
modellenmesi. 2. Ulusal Tasarım Đmalat ve Analiz Kongresi, 2010 20–
30.
[17] Kıvak, T. Optimization of surface roughness and flank wear using the
Taguchi method in milling of Hadfield steel with PVD and CVD coated
inserts. Measurement, 2014, 50, 19–28.
[18] Karabulut S.; Güllü, A. An investigation of cutting forces and analytical
modelling in millling compacted graphite iron with different lead angles.
Journal of the Faculty of Engineering and Architecture of Gazi
University 2013, 28, 135–143.