Surface roughness is one of the most significant technical requirements for machined product. Nowadays, machining process in manufacturing sector has been replaced with computers to control machines tool which is known as Computer Numerical Control (CNC) machining where a numbers of process parameters were set in order to control the output.Turning process is one of the most important processes in machining production where a turning machine is used to create rotational parts by cutting away unwanted material. In this study, a Support Vector Machine (SVM) was applied to model and predict the surface roughness of carbon steel AISI 1045 in CNC turning operation. Performance of three different type of SVM models namely Least Square SVM (LS- SVM), SVM- KM and Spider was compared. In the development of predictive models, turning parameters of feed rate, depth of cut and cutting speed were considered as input variables to the model. The prediction results showed that Spider SVM able to predict better as compared to the other two models where the best kernel function is radial basis kernel (RBF).
|Number of pages||11|
|Journal||International Journal of Computing Academic Research|
|Publication status||Published - Feb 2017|
V. Janahiraman, T., Ahmad, N., & Wan Ishak, W. I. B. (2017). Modelling and Prediction of Surface Roughness in CNC Turning Operation using Support Vector Machine. International Journal of Computing Academic Research, 6(1), 1-11. .