Analytical study of different approaches to determine optimal cutting force model

• Autorzy: Krizek Z. , Jurkovic Z. , Brezocnik M.
• Języki publikacji: EN

Abstrakty

EN

Determination of optimal machining parameters is an engineering task with aim to reduce the production cost and achieve desired product quality. Such exercise can be tackled on many different ways. The goal of this work is to present some of the possible approaches and to benchmark them among each other. These principles are analyzed: response surface methodology (RSM), evolutionary algorithms (GA & GP), support vector regression (SVR) and artificial neural networks (ANN). All methods implement completely different data handling philosophies with the same goal, to build the model which is able to predict cutting force in satisfying manner. Those aspects are chosen to be evaluated and compared: average percentage deviation of all data, ability to find generalized model and minimize the risk of over fitting and at least the runtime of each single model determination. Average percentage deviation is one of the best indicators of the quality of model. The ability to find generalized model is good indicator of the flexibility of model, and shows how model deals with unknown data. The runtime is important in a real time environment or in scenarios where conditions change frequently. Cutting force data used in this benchmark comes from experimental research of longitudinal turning process.

Słowa kluczowe

EN

cutting force; modelling; response surface methodology; genetic algorithm; genetic programming; support vector regression; artificial neural networks

PL

siła skrawania; modelowanie; metodologia oceny powierzchni; algorytm genetyczny; programowanie genetyczne; regresja wektora wsparcia; sieci neuronowe sztuczne

• Wydawca: Komitet Nauki o Materiałach PAN
• Czasopismo: Archives of Materials Science
• Rocznik: 2007
• Tom: Vol. 28, nr 1-4
• Strony: 69--74
• Opis fizyczny: Bibliogr. 6 poz., rys., tabl.

Twórcy

autor

Krizek Z.

autor

Jurkovic Z.

autor

Brezocnik M.

• Wiener Zeitung GmbH, Wiedner Guertel 10, Vienna, Austria

Bibliografia

• [1] J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
• [2] J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, 1992.
• [3] M. Brezocnik, M. Kovacic, Integrated genetic programming and genetic algorithm approach to predict surface roughness, Materials and Manufacturing Processes 18/3 (2003) 475-491.
• [4] N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, 2000.
• [5] A.J. Smolay, B. Scholkopf, A Tutorial on Support Vector Regression, Statistics and Computing 14/3 (2003) 199-222.
• [6] Ch.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.